In any industry and market organisations are witnessing a new era where our customers’ behaviours are measured, tracked and often predicted with the use of Data. What does this mean? Technologies are rapidly changing the ways people and organisations create, use and share data and knowledge. 

The new paradigma is digital transformation considered as ‘the change associated with the application of digital technology in all aspects of human society’

Every business that is using digital transformation to better serve the end consumers and to take advantage of data around it.

Life Science, Retail, Financial Services, Betting and many other sectors are facing the opportunity of adding new value streams to their marketing activities with the use of dedicated technologies built by Data Scientists.

Data Science can be defined as the language or informatics tool that can be taught about businesses. 

Data Science is the science of processing information. It’s about building, creating, and inventing machines that automatically process all kinds of information, from numbers to text, images, or video. This started with the calculating machine, here, an example of numbers and arithmetic operations. 

For example: 346 + 78 = ?

Besides arithmetic, Data Science can analyze business trends and it can generate operational solutions with the aim to enhance the performance so that businesses can operate more effectively in the market. Embracing new technologies has become a significant facet in the development of products and services in modern times. In order to grow in the market, businesses have to be dynamic; the adoption of new tech innovations has enabled them to manage business operations very efficiently. Imagine a place where more than 278,000 skilled IT professionals roam, bringing their expertise and creativity to the table.

For activities like marketing the role of Data Scientists building Artificial Intelligence and Machine Learning solutions has become very critical as behavioural models and algos help businesses to enhance their clients engagement and optimize their budget. See our success stories

Below are some interesting use cases..

AWS Intelligence for everyone

Amazon has been a pioneer in using Machine Learning to offer personalized product recommendations. However it has been challenging for Amazon to extend these capabilities to the companies that were running their sites using Amazon Web Services.

Last June, Amazon announced the general availability of Amazon Personalize, which brings the same machine learning technology used by Amazon.com to AWS customers for use in their applications.

Starbucks Uses Predictive Analytics 

Companies that identify customer needs through predictive analytics are able to increase their organic revenue with predictive analytics.Starbucks is one example of a brand that is using its loyalty card and mobile app to collect and analyze customer data.

This came as a result of the work of a team of Data Scientists who have been focusing on these analytics programmes since  2016 and creating dedicated programmes with the latest data languages (MongoDB, Pytorch and Spark).

Relevant things of Stitch Fix

Stitch Fix is an online styling service that delivers a personalized assortment of fashion products to customers each month. Managing return costs and warehouse space are key components of profitability for Stitch Fix. 

The brand combines personal (human) stylists with the insight and efficiency of Artificial Intelligence. Data Scientists built models to analyze the data on style trends, body measurements, customer feedback, preferences and delivers a narrowed down set of possible recommendations to the human stylist. The stylists then use their expertise to handpick clothing and accessories for the individual customer.

Incorporating AI into their systems allows Stitch Fix to invest in merchandise with more confidence. As Eric Colson, Chief Algorithm Officer at Stitch Fix, said: “Our business is getting relevant things into the hands of our customers”.

The above use cases show how Data Science is changing organisation Go To Market strategies, how does your business take advantage of data?

 

1. Despite the impact of Covid, We have years of growing opportunities
Since 2013, data science job postings have increased by 256 percent—but even crazier: growth is still continuing. In 2019, there was a 31% increase in data science job postings compared to the previous year. At the same time, people have heard good things about data science as a career, so we can also look at how many people are actually searching for these jobs. In 2019, there’s a 14 percent increase year-over-year in searches for data science jobs.

Despite the pandemic crisis, it’s still a great time to be a data scientist entering the job market. That’s according to recent data from job sites like Glassdoor and Indeed.

In this article, we’ll look together as the data scientist job outlook remains positive despite two years of challenges.

1.1 Data-driven approach in the competition
Data-driven decision making is the simple answer to this question. To be a successful company in the 21st century you have to use data to increase your market share.

Before many companies were doing this by using excel to analyse data, but now anyone can have access to and use data-crunching tools like:

● Google Analytics — Digital marketing cloud-based service

● Tableau, Power Bi — Data visualization tools for business intelligence

● Python, R— Programming languages used to perform complicated analysis with a few lines of code.

The largest companies in the entire world are data science fueled enterprises. Take a look at Google, Amazon, and Facebook. They create algorithms that improve customer satisfaction and maximize profits.

● Google — Ranking of webpages to ensure the top links have an answer to any desired question.

● Amazon — Recommendation of products based on consumer’s past behaviour and interests.

● Facebook — Targeted ads (they know the sports you like, preferred price range, food, etc) to increase market success.

Therefore, companies have to adapt and employ data science tools and techniques or they will simply be forced out of business.

1.2 Supply
The supply of Data Scientists is relatively low, because the field of data science is still relatively new even in 2021.

20 years ago, it was impossible to learn data science because of slow internet connection, and low computational programming languages.

Nowadays, the power of computers started to grow exponentially and data science became possible. This exponential growth and interest in the field were impossible to predict, and traditional education was not ready to meet the needs of those who wanted to learn this growing field.

2. Employment Statistics: Hot Job That Pays Well
According to Glassdoor’s 50 Best Jobs in UK for 2020, data scientist remains one of the top three positions in the UK, with a high job satisfaction and median base salary over $100,000. And despite a decline in position postings, becoming a data scientist remains as desirable as it was at pre-pandemic levels.

The best job in UK
Source: Glassdoor

2.1 Growth: Between Past and Present
According to Indeed, when the pandemic first started in 2019, many organizations went into a hiring freeze while the length of lockdowns was unknown. The need for data scientists didn’t diminish, but new hires for the role have only just started to pick up.

In February, hiring started to pick up with slight increases every month since. And even when data scientist hiring was low, the need was still high.

2021 has been a year with a lot of disruption and change due to impact of covid, including in the data engineering space. In addition to COVID-19’s impact on the hiring market (and economy at large), there have also been evolving approaches to staffing for data engineer positions, including an increasing focus on hiring searches led by specific tools

Glassdoor, Burtch Works and data science competitions platform Kaggle, are expected to have some fluency in at least one programming language – Python and R being the favourites.

This clustering maps to Indeed’s understanding of data scientists as well: under the umbrella of data science, there are data scientists and product scientists. Data scientists are probably a little closer to what you would call a machine learning engineer; like a lot of other companies, Indeed product scientists tend to be more generalist; there are separate analytics, BI analysts, and BI developers as well.

cluster of programming language
Source: Indeed Hiring Lab

Data scientists also are expected to have experience in tools like Hive, BigQuery, AWS, Spark and Hadoop, as well as training in statistical modeling, machine learning and programming.

The Indeed report noted that many data scientists’ formal education is in disciplines like computer science, statistics or a quantitative social science.

Of the recent data scientist candidate profiles presented on Dice, 27% have a master’s degree, 10% have a doctorate and 13% have a bachelor’s degree.

What is googelecom? GoogleCom is one of the most popular websites in the world and they offer many different services. One of the services they offer is internet search. One of the services they offer is internet search.

2.2 Data scientist: A resilient Job
It’s no surprise, given the high demand for data scientists, that salaries for the position will grow every year. So the impact of covid will not affect the growing demand.

According to Gil Press, senior contributor of Forbes: “Would you like to earn up to $200,000 in base annual salary as an individual contributor or up to $300,000 as a manager? And have a career that stays resilient—is even more in demand—in the face of a global pandemic and the deepest economic recession in recent memory? Then become a data scientist.”

Big Data has led to business growth in all industries spreading a powerful wisdom for the decision making process. Of all the tools that process Big Data, Hadoop MapReduce and Apache Spark attract the attention of the data experts and companies. In this article, we’ll learn the key differences between Hadoop and Spark and when we should choose one or another, or use them together.

Hadoop & Spark: Definitions and Numbers

Apache Hadoop is an open source framework that is used in cloud computing to efficiently store and process large datasets ranging in size from gigabytes to petabytes of data. Instead of using one large computer to store and process the data, Hadoop allows clustering multiple computers to analyze massive datasets in parallel more quickly.
Hadoop consists of four main modules:

• Hadoop Distributed File System (HDFS) – A distributed file system that runs on standard or low-end hardware. HDFS provides better data throughput than traditional file systems, in addition to high native support of large datasets.
• Yet Another Resource Negotiator (YARN) – For managing compute resources in clusters and using them to schedule users’ applications.It schedules jobs and tasks.
• MapReduce – A MapReduce is a programming model for large-scale data processing. Using distributed and parallel computation algorithms, MapReduce makes it possible to carry over processing logic and helps to write applications that transform big datasets into one manageable set.
• Hadoop Common – Includes the libraries and utilities used and shared by other Hadoop modules.

Apache Spark is a unified analytics engine for large-scale data processing. Apache Spark is an open-source, distributed processing system used for big data workloads.It does not have its own storage system, but runs analytics on other storage systems like HDFS, or other popular stores like Amazon Redshift, Amazon S3, Couchbase, Cassandra, Kubernetes and others. Spark on Hadoop leverages YARN to share a common cluster and dataset as other Hadoop engines, ensuring consistent levels of service, and response. Data engineers use Spark for coding and building data processing jobs—with the option to program in an expanded language set.

The two are Open-source projects from Apache Software Foundation, and they form the leading products for Big Data Analytics. Hadoop has been the leading tool for Big Data Analytics for 5 years. Recent market research has shown that Hadoop has been installed by 50,000+ customers, while Apache Spark has only 10,000+ installations. However, the popularity of Apache Spark skyrocketed in 2013, overcoming that of Hadoop in only one year.

Language of support

Hadoop is developed in Java. MapReduce applications can be written in R, C++ and Python. Apache Spark is developed in Scala and supports languages like Java, C++ and Python. The last two languages described above are very simple to use.

Performance

Apache Spark is well-known for its speed. It runs 100 times faster in-memory and 10 times faster on disk than Hadoop MapReduce. The reason is that Apache Spark processes data in-memory (RAM), while Hadoop MapReduce has to persist data back to the disk after every Map or Reduce action.
Apache Spark’s processing speed delivers near Real-Time Analytics, making it a suitable tool for IoT sensors, credit card processing systems, marketing campaigns, security analytics, machine learning, social media sites, and log monitoring. It could cause more degradation.

Apache Spark comes with in-built APIs for Scala, Java, and Python, and it also includes Spark SQL for SQL users. Apache Spark also has simple building blocks, which make it easy for users to write user-defined functions. You can use Apache Spark in intermediate feedback for queries.

On the other hand, Hadoop MapReduce is generally slow: it was written in Java and is difficult to program. It needs to handle low level APIs to process data.
In other terms, a lot of coding!Unlike Apache Spark, Hadoop MapReduce cannot deliver real-time analytics from the data. Considering the above-stated factors, it can be concluded that Apache Spark is easier to use than Hadoop MapReduce.

Data Processing

With Apache Spark, you can do more than just plain data processing. Apache Spark can process graphs and also its own Machine Learning Library called MLlib.
Due to its high-performance capabilities, Apache Spark is very helpful for Batch Processing as well as near Real-Time Processing. Apache Spark is a “one size fits all” platform, built-in machine learning library, it can be used to perform all tasks instead of splitting tasks across different platforms. It can be used for classification, regression and building machine learning-pipelines.

Hadoop MapReduce is a good tool for Batch Processing. It operates in sequential steps by reading data from the cluster, performing its operation from data, writing the results back to the cluster, but if you want to get features like Real-Time and Graph Processing, you must use other tools as well as Mahout and Samsara.

Scalability

Hadoop is highly scalable, adding n numbers nodes in the cluster. Yahoo reported to have more than 42,000 nodes.
However, Apache Spark uses Random Access Memory (RAM) for optimal performance setup. The largest Spark cluster has only 8,000 nodes. Since Big Data keeps on growing, cluster sizes should increase in order to maintain throughput expectations. The two platforms offer scalability through HDFS.

Security

Handoop supports Kerberos and LDAP for authentication. It also uses a traditional file permission model.
Spark’s security model is currently sparse, but allows authentication via shared secret. Additionally, Spak can run on Yarn giving the use of Kerberos authentication.

Cost

Both Hadoop MapReduce and Apache Spark are Open-source platforms. However, you have to invest in hardware and personnel or outsource the development.
Business requirements should guide you on whether to choose Hadoop MapReduce or Apache Spark. If you want to process huge volumes of data, consider using Hadoop MapReduce.

We can say Hadoop MapReduce requires more memory on disk and it’s less expensive than Apache Spark. Spark requires a lot of RAM to run. This increases the cluster size and its cost. The reason is that hard disk space is cheaper than RAM.

Top 5 companies which use Spark

1. eBay

eBay uses Apache Spark to provide targeted offers, enhance customer experience, and to optimize the overall performance. Apache Spark is leveraged at eBay through Hadoop YARN. EBay spark users leverage the Hadoop clusters in the range of 2000 nodes, 20,000 cores and 100TB of RAM through YARN.

2. Conviva

The largest streaming video company Conviva uses Apache Spark to learn about the network conditions in real-time. The video player is able to manage live video traffic coming from close to 4 billion video feeds every month, to ensure maximum play-through, helping Conviva by providing its customers with a great video viewing experience.

3. Netflix

Netflix uses Apache Spark for real-time stream processing to provide online recommendations to its customers. Streaming devices at Netflix send events which capture all member activities and play a vital role in personalization. It processes 450 billion events per day which flow to server side applications and are directed to Apache Kafka.

4. Pinterest

Pinterest is using Apache Spark to discover trends in high value user engagement data so that it can react to developing trends in real-time by getting an in-depth understanding of user behaviour on the website.

5. TripAdvisor

TripAdvisor, a leading travel website that helps users plan a perfect trip, is using Apache Spark to speed up its personalized customer recommendations. TripAdvisor uses Apache Spark to help millions of travellers by comparing hundreds of websites to find the best hotel prices for its customers.

Top 5 companies which use Hadoop MapReduce

1. Amazon Web Services

Elastic MapReduce provides a managed, easy to use analytics platform built around the powerful Hadoop framework. Focus on your map/reduce queries and take advantage of the broad ecosystem of Hadoop tools, while deploying to a high scale, secure infrastructure platform.

2. IBM

InfoSphere BigInsights makes it simpler for people to use Hadoop and build big data applications. It enhances this open source technology to withstand the demands of your enterprise, adding administrative, discovery, development, provisioning, and security features, along with best-in-class analytical capabilities from IBM Research.

3. Cloudera

Cloudera develops open-source software for a world dependent on Big Data. With Cloudera, businesses and other organizations can now interact with the world’s largest datasets.

4. British Airways

British Airways deployed Hadoop in April 2015 as a data archive for legal cases. Previously these were stored on an enterprise data warehouse which was costly for the airline.

Since deploying Hortonworks 2.2 HDP, British Airways has gained ROI within a year, and is able to deliver 75% more free space for new projects, translating directly into cost reductions for the airline.

5. Expedia

Expedia makes use of Hadoop clusters using Amazon Elastic MapReduce (Amazon EMR) to analyze high volumes of data coming from Expedia’s global network of websites. These include clickstream, user interaction, and supply data. Highly valuable for allocating marketing spend, this data is merged from web bookings, marketing departments and marketing spend logs to analyze whether the outlay has equated to increased bookings. The firm has seen costs drop and can process and analyze higher volumes of data.

Conclusion and the Big Question

The following are the limitations of both Hadoop MapReduce and Apache Spark:

• No Support for Real-time Processing: Hadoop MapReduce is only good for Batch Processing. Apache Spark only supports near Real-Time Processing.
• Requirement of Trained Personnel: The two platforms can only be used by users with technical expertise.

Finally, the big question: can we use them together? The answer is yes: Hadoop and Spark together build a very powerful system to address all the Big Data requirements. Apache Spark is not developed to replace Hadoop rather it’s developed to complement Hadoop. Spark comes to rescue Handoop with real-time, streaming, graph, interactive, iterative requirements.

And when you use Spark over Hadoop or you use them together?

With the rapid development of computing capabilities and storage techniques, there is no doubt that we are facing a series of opportunities and challenges brought about by the data era. The new data-based trend not only spreads wisdom into decision-making and performance improvement but also poses a great threat to traditional data processing techniques.
Data transformation is the process of changing the format, structure, or values of data in a new format. Furthermore, the rising complexity of reality requires simple and flexible solutions to drive businesses’ competition.

Benefits of Data Transformation

Whether it’s information about customer behaviors, internal processes, supply chains, businesses and organizations across all industries understand that data has the potential to increase efficiency and generate revenue.
By using a data transformation process, companies are able to reap massive benefits from their data, including:

• Managing Big Data more effectively: With data being collected from different sources, inconsistencies in metadata can make it a challenge to understand data.
• Data transformation organises better metadata to make it easier to organize and understand what’s in the data set and what drives the client’s business.
• Performing faster queries: Transformed data are standardized and stored in virtual machines, where they can be quickly and easily retrieved.
• Enhancing data quality: Data quality is becoming a major concern for organizations due to the risks and costs of using bad data to obtain business intelligence.

Data transformation can be used in different industries: from healthcare to financial services. Then, there are some key aspects we should consider before working on data:

• Determine business requirements
• Understand and profile your data sources
• Determine data extraction methods
• Establish data transformation requirements
• Decide how to manage the ETL process/li>

Extract-Transform-Load (ETL) processes are used to extract, clean, transform, and load Big Data from source systems for cohesive integration, bringing it all together to build a unified source of information for business intelligence (BI). As a vital stage of the ETL process, data transformation is necessary to change the information into a format that a business intelligence platform can interact with actionable insights.

The Extraction

Before organizing the data, the first step in the ETL process is extracting the raw data from all the relevant sources for the analysis. The data sources may include:

• CRM systems
• marketing automation platforms
• cloud data warehouses
• unstructured and structured files
• on-premise databases
• cloud applications, and any other data sources able to drive useful insights.

Once all the data has been consolidated, we notice that data from different sources are dated and structured in different formats.
In this step, the data must be organized according to size, and source to suit the transformation process. There is a certain level of consistency required in all the data to be extracted into the system and processed in the next step.
The complexity of this step can vary significantly, depending on data types, the volume of data, and data sources. Although we should consider several factors, scalability is crucial.
Be highly scalable means to be able to extract and process massive amounts of data in a short time.

Data Transformation

Data Transformation is the second step of the ETL process in data integrations.
Data needs to be cleansed, mapped and transformed. In fact, this is the key step where the ETL process adds value and changes data such that insightful BI reports can be generated. It may involve following processes/tasks:

• Filtering – loading only certain attributes into the data warehouse.
• Cleaning irrelevant data from the datasets – filling up the NULL values with some default values
• Joining – joining multiple attributes into one.
• Splitting – splitting a single attribute into multiple attributes.
• Sorting – sorting tuples on the basis of some attribute (generally key-attribute).

Quality data sources won’t require many transformations, while other datasets might require it significantly. To meet the target database’s technical and business requirements, we can adopt several transformation techniques.
The level of manipulation required in ETL transformation depends on the data extracted and the needs of the business.

Loading VS Streaming

The concluding step in the three-step ETL process is the act of loading/streaming the datasets that have been extracted and transformed earlier into the target database.
There are two ways to go about it; the first is a SQL insert routine that involves the manual insertion of each record in every row of the target database table. The other loading approach uses a process called a bulk load of data, reserved for massive data loading.
The SQL is slow, but it conducts data quality checks with each entry. While the bulk load is much faster for loading massive amounts of data, it does not consider data integrity for every record. Bulk loading is ideal for datasets you’re confident are free of errors.
You can use the following mechanisms for loading a data warehouse:
Loading a Data Warehouse with SQL Loader

• Loading a Data Warehouse with External Tables
• Loading a Data Warehouse with Direct-Path APIs
• Loading a Data Warehouse with Export/Import

ETL Streaming

Streaming ETL process is useful for real-time use cases: dashboard, dynamic insights in particular for the customer experience. Fortunately, there are tools that make it easy to convert periodic batch jobs into a real-time data pipeline.
Transformation and load data can be extracted using a stream-based data pipeline to perform SQL queries and generate reports and dashboards.
The streaming application ETL can extract data from any source and publish it directly to the streaming ETL application, or the source can publish the data directly to the streaming ETL application and extract it from another source. Apache Kafka is a popular tool for real-time data processing, but also Amazon MQ, IBMQ. We can extract data with and allow ETL to stream in the cloud in real-time, without the need for complex systems that require coding.

The ETL architecture for streaming is scalable and manageable, offering a wide variety of ETL scenarios, including a variety of data types.
The new competitive scenario will depend on how organisations use large volumes of Big Data to analyse, organize and restructure their business process.

If you are interested to know about what we do, please visit our projects page: https://artecha.com/business-cases/

Today we will talk about the main differences between Flutter, Angular and React Native

Flutter 

Language: Flutter apps are written in a language called Dart, which is relatively easy to learn and understand and is a language promising to program for cross-platform development. However, it doesn’t have the same popularity as JavaScript. A developer starting with Flutter should first learn Dart. Generally, developers with a C ++ / Java background can relate to Dart better than JavaScript developers.

Test: Flutter is the news when it comes to frameworks. Testing a new framework can be challenging, but Flutter uses Dart, which provides a great unit test framework. Flutter also provides an excellent choice for testing widgets on a headless runtime at unit test speed

Performance – there is no JavaScript bridge in Flutter to initiate interactions with native device components, which dramatically accelerates execution time and speed of development. Flutter’s animation standard has been set at 60 fps, which indicates its high performance. Finally, because Flutter is compiled into native ARM code for both iOS and Android, it never faces performance issues.

User Interface: Flutter incorporates elegant integrated Material Design and Cupertino, such as advanced motion APIs, iOS-style widgets, smooth scrolling and platform awareness. Flutter has its own individual UI components, adaptable widget sets, and material designs along with an engine that helps render them on iOS and Android.

Community Support: Flutter’s community support can be seen in its 98,000 stars on GitHub, 47.6k user subreddit, and on Stack Overflow. While Dart didn’t get much admiration in the Stack Overflow developer survey, early blog posts provided positive feedback on using Flutter. Furthermore, their documentation is very complete and answers all questions asked in an acceptable time frame.

Usage Cases

1. Google Ads (Utility)

The Google Ads app allows users to view campaign statistics on an Android smartphone. The app displays campaign details such as real-time alerts and notifications, allows you to call a Google expert, take action on suggestions to improve your campaign, add / edit / remove keywords, and more.

2. Alibaba (eCommerce)

The Alibaba.com app is a wholesale marketplace for global commerce and incorporates Flutter to power parts of the app. The app allows its users to purchase products from suppliers around the world, all from the convenience of a mobile app.

Angular

Language Angular uses TypeScript, which is a superset of JS created for larger projects. TypeScript is relatively compact compared to JavaScript. This makes it easier to navigate and makes the code refactoring process easier and faster. Angular is a rewrite of AngularJS, a JavaScript framework, which has been enhanced for more intuitive TypeScript usage.

Test: In Angular, testing and debugging a complete project can be done with a single tool, such as Protractor, Jasmine or Karma. Another great tool that debugs your app in development mode is the Augury browser extension.

Performance: AngularJS is recognized for its moderate performance while dealing with complex and dynamic applications. React and Flutter apps are faster than Angular apps of the same size. However, some new versions of Angular are a bit faster than React.

User Interface: Angular has an integrated Material technology stack. It comes with many pre-built material design components. This makes configuring the user interface extremely agile and simple.

Community Support: Angular is constantly and actively supported by Google, which continues to advance the Angular ecosystem. Since 2018 it also provides a framework with LTS (Long-Term Support). According to the Stack Overflow Developer Survey, the number of developers working with Angular is greater than those working with React and Flutter.

Usage Cases

1. Gmail

Developed by Google (the company that created Angular and continues to support it) and launched in 2004, Gmail has a subscriber base of over 1.4 billion people and is the most widely used free email service. Gmail currently supports 105 different languages.

1. YouTube TV

Created with Angular 2 and launched in 2017, YouTube TV is Google’s alternative to AT&T TV Now and Hulu + Live TV, offering affordable basic access to a myriad of live shows. The service is available on Android TV, Apple TV, Chromecast, Fire TV, Roku OS, and Xbox One. It also works with smart TVs from market leaders like LG and Samsung.

The YouTube TV Cloud DVR offering allows you to record as much content as you like, which can be archived for up to nine months, ending the storage limits imposed by most competitors.

React Native

Language: React is based on the ES6 + JavaScript language along with JSX, which is an extension of the JavaScript syntax that makes mirroring JavaScript code written in HTML. Developers find it easy to write code in JavaScript, and in turn, learning React is very easy for any JavaScript developer.

Testing: Developers using React have all JavaScript frameworks available for unit-level testing. However, when it comes to UI and automation testing, conditions aren’t that good. While you may find many third-party libraries available, there isn’t a clear picture.

Performance: Ideally you will need a bridge in React to call Swift, Windows, Android, or Mac APIs. Developers face problems when building hybrid apps, but rarely encounter performance issues for native apps. React offers uninterrupted performance in all typical cases and is highly reliable.

User Interface: React App Development uses third party libraries since it does not have its own library of user interface components. React Native Material Design and Shoutem are two examples of user-accessible UI libraries. React Native is similar to using HTML without a CSS framework. Compared to Flutter, the React Native UI relies more on native components for both iOS and Android. It also provides a more pleasant user experience (UX) when a user logs into the operating system.

Community Support: React was released as open source on GitHub in 2015 and is the most popular framework on Stack Overflow. It’s supported by a huge community, with over 89,000 stars on GitHub, 58.4k users on its subreddit, and great support on Stack Overflow. This is why they have more third party libraries / plugins than Flutter.

Use Cases

1. Facebook Ads Manager

Ads Manager is the first cross-platform React Native app created by Facebook. The Javascript framework perfectly handles the difference in ad formats, date formats, currencies, time zones, etc. It has a clean interface, intuitive UX and simple navigation that guarantees an amazing experience for users. 

2. Bloomberg

The app provides users with global business and financial news. Before the Bloomberg team adopted React Native for mobile app development, they had to spend a lot of time developing and updating individual versions of the iOS and Android apps. Through in-depth tests of the prototype built with React Native, the technology for simultaneous updating of cross-platform apps was adopted.

What is a CTA

A CTA or Call-To-Action is a phrase or word that encourages the reader to take a specific action. 

CTAs are used to invite people to visit your site, read an article on your blog, share content on social networks, download a document, etc … 

As you can imagine, writing a CTA that converts is very important, and it is precisely this that we will talk about in this article. 

How to write a CTA that converts 

Your target audience most likely knows something about marketing, so writing an effective CTA is more important than ever. 

But how do you write an effective call to action? 

1. Benefits: In the CTA it must be written what kind of benefit the person acting in a certain way can have. For example, the invite to subscribe to the newsletter to receive all the blog updates. 
2. Keep the promise: it is very important that the reader immediately receives what is described in the CTA.

An effective CTA is also made up of specific words. Let’s see what kind of words should be used:

1. Action: As we have seen, a call to action must tell you what to do. For example “Sign up here” “read the post” etc …
2. Scarcity: This means letting the reader understand that he has to hurry before that thing disappears. You can trigger the FOMO (Fear of missing out) with a simple countdown. 
3. Avoid friction words: These are words that invite the reader to do something he doesn’t necessarily want to do. For example Download, Buy, Order. Nobody wants to download or buy, but everyone wants the benefits. We must focus on those. 

Where to put the CTA?

Top of page: Putting the CTA at the top of the web page attracts the reader’s attention before he starts reading your page. 

At the end of a blog post: Usually in this case the CTA invites you to share the post on social media. 

In the middle of the post: If you are talking about something relevant in the post, in the middle you can put a CTA that invites you to download something related to the topic you are talking about. 

At the end of the lead magnet: At the end of the page where you describe the lead magnet, you can put a CTA 

Design of the CTA

The important things to remember when doing your CTA are few: 

• The button must be large enough to be seen, but it must not be too intrusive

• Must be visible, so use colors that contrast with the page.

• Test everything

The last point is very important. 

Testing your CTA is essential to find the one that works best for you, so test everything from the colors to the sentence to the size of the words. 

No business is the same, so the results cannot be the same for everyone.

Examples of CTA 

• Evernote: The CTA is: Sign up. the button is on the left, well highlighted in green, there is also written what benefit you will have by registering and simplifying the work.

• Uber: Their CTA is Sign up to drive. Uber focuses on the income that you will have once registered. Here the button is at the top of the page, clearly visible and clearly written the benefit.

• Spotify: Spotify from two options: “Go Premium” or “Play Free”. Spotify prefers to highlight the possibility of having a premium account, that’s why there’s the “Go Premium” button is green. 

• Instagram: Instagram focuses on downloading the app, initially it invites you to log in or register, at the bottom of the page there is an invitation to download the app.

Types of CTA 

Here are some types of CTA that you can use for your site: 

1. Try it for free: If a person has just arrived on your site, the best thing to introduce yourself is to offer something for free. For example A pdf, an e-book or a free trial of a course.
2. Read more: You can put it at the end of a preview of a post, so that people are sent back to the whole post on your blog. 
3. Social Sharing: At the end of a post, you can invite people to share the content on their social networks. it is a good way to make yourself known. 
4. Contact me or Buy now: When a lead is ready to buy, you can use a CTA that invites you to do so. You can ask to contact you for information on the product you sell, or you can write directly “buy now”. Be careful where you put this CTA, better put it at the end of a sales page or on the page where you list your products. 

The CTAs that can be used are many, it is very important to understand the benefit that the reader will have and from there you can start to find the CTA that best suits your business. Don’t stop at the technique, be creative.  

Finally, it is very important to remember to test all the CTAs, what works for someone is not said to work for someone else, so the password is to test. 

We hope this article has been helpful in clarifying some aspects of the CTA. 

What is CRM:

CRM or Customer Relationship Management is a strategy for managing all the relationships and interactions of a company that take place with potential and existing customers. A CRM system helps companies stay in touch with customers, simplify processes and improve profitability.

When we talk about CRM, we usually refer to a CRM system that is a tool used for contact management, sales management, productivity and more. The goal of this system is simple: to improve all the interactions underlying the business.

A CRM system helps companies to: 

•  keep customer contact details up to date, 
• monitor every interaction with customers and manage their accounts. 
• improve relationships with customers and also their lifetime value (CLV). This aspect is fundamental, given the high amount of data generated by companies on a daily basis.

The issue of customer data represents a challenge to which CRM systems have been created. Whenever someone answers the phone and talks to a customer, they go out to meet a potential customer or follow a promising lead, learn something new and potentially valuable.

A CRM system essentially provides a central point where companies can store actual and potential customer data, as well as monitor customer interactions and share this information with colleagues. It allows companies to manage customer relationships, helping the company grow.

Using a CRM system, each question, service request, preference and past contact data of each customer is available immediately, which means that each new interaction should always be personalized, relevant and updated.

In addition to keeping track of every phone call, email, meeting and presentation, CRM systems can also be used to add notes, plan follow-ups and organize next steps. This ensures that opportunities to close deals or grow customer accounts are not wasted.

Key features of a CRM system

Key features of a CRM system typically include contact management functionality, dashboard-style presentation of information and reports that analyze pipeline activity, as well as communication tools such as e-mail. integrated mail and internal instant messaging:

• Contact
• management Lead management
• Sales forecasting
• Instant messaging among employees
• Email monitoring and integration with Outlook and Gmail
• File and content sharing
• Dashboard-based analytics

Evaluating and comparing the various types of systems CRM

When evaluating and comparing CRM systems, three types must be considered: desktop, server and cloud. A desktop system is suitable for a single user who simply needs an electronic version of a Rolodex to manage basic customer contacts, so for most companies the key question to answer is: server or cloud?

The three basic types of CRM systems include:

• Desktop systems that run on a single computer
• Client-server systems that have a central database stored on a server, usually hosted autonomously with software installed on each user’s PC or laptop so that it can access it
• Cloud-based systems provided and hosted online by a third-party provider and accessible anywhere via a connected device

 As technology evolves, the way we work and connect with customers also evolves. Advanced systems go beyond the obvious functionality of CRMs, to respond to developments such as remote work and artificial intelligence. Cloud-based CRM systems are at their best in this area, as they can be updated as new technologies become the norm.

Mobile CRM for remote work

Some CRM systems such as Salesforce now offer mobile CRM functionality, which allows salespeople to access crucial information wherever they are and to update that information immediately after a meeting while still in the field, so that colleagues can follow the customer with the latest information, before the competition.

With mobile CRM, you can manage an entire business from a phone, without being tied to a desk: closing deals, customer support and even implementing 1: 1 marketing campaigns. This feature can also support work from home and even allow companies to reduce the size needed for offices.

Social Media Integration

Today’s CRM platforms can help companies get the most out of social media as a source of new leads, data about potential customers and information for customer support workers. All these new social data flows are integrated with the rest of the data available to a customer, to provide the widest possible image and a series of new information with practical value.

Leveraging artificial intelligence

Some CRM systems can use artificial intelligence (AI) to learn from available data in order to make recommendations based on business processes. In this way, the system improves constantly and automatically, becoming more intelligent and more targeted to customer needs.

Here are some ways in which artificial intelligence can enhance a CRM:

• Transcribing and analyzing sales calls: the transcription of calls can be useful to highlight which are the most interesting topics for the customer, or to improve sales performance. For this purpose there are software such as Chorus that can be integrated with Salesforce.
• Identify what all support tickets have in common: This can be used to advise which is the best answer and halve the time. Some software such as Digital Genius help automate responses and halve problem resolution times. 
• Speeding up content production thanks to a natural language generator: Creating content for websites, social networks and landing pages takes a lot of time and resources, but software like Wordsmith can produce content for articles, social content, landing pages and much more. It produces content using the language suitable for your audience, greatly reducing the time. It does content in 15 languages ​​and can be integrated with Zapier

https://unsplash.com/photos/7okkFhxrxNw foto 

CRM software

Here is a list of some CRM software 

• Monday: This is one of the most famous software, it has 14 days of free trial and you easily integrates with tools that are commonly used by many companies (for example Slack). 
• Zoho is a cloud based CRM, usable by businesses of all kinds. It supports more than 26 languages ​​and is very easy and fast to use.
• Salesforce is one of the main cloud based CRM tools / software in the world that provides creative CRM solutions that can be used effectively for all companies with business needs, from large size to small start-ups.
• Dynamics: It contains everything a company needs. Very easy to use, and has integrations with other systems such as Social Engagement.
• HubSpot: It has all the basic functionality of a crm, without being as complex as other platforms.

What is ERP

Enterprise resource planning (ERP) refers to a type of software that organizations use to manage daily business activities, such as accounting, procurement, project management, risk management and compliance and supply operations chain. A complete ERP suite also includes enterprise performance management, software that helps plan, quantify, predict and communicate an organization’s financial results.

ERP systems combine and define a set of business processes and guarantee the exchange of data. By collecting shared transactional data from different sources in the organization, ERP systems eliminate duplication of data and ensure its integrity through a single reliable source.

Nowadays, ERP systems are fundamental for the management of thousands of companies of all sizes and belonging to different sectors. For these companies, ERP is as important as the electricity that powers all systems.

ERP systems are based on a single defined data structure (schema) which generally shares a common database. This ensures that the information used throughout the company is normalized and based on common definitions and user experiences. These main constructs are then interconnected with business processes driven by workflows between corporate departments (e.g. finance, human resources, engineering, marketing, operations), connection systems and the people who use them. In a nutshell, ERP is the vehicle for integrating people, processes and technologies into a modern company.

For example: consider a company that makes cars by getting parts and components from multiple suppliers. It could use an ERP system to track the demand and purchase of these assets and ensure that every component of the entire procure-to-pay process uses uniform and clean data related to integrated business workflows, business processes, reporting and analytics. When ERP is used correctly in this automotive manufacturing company, a component, for example, “front brake pads”, is identified uniformly by component name, size, material, origin, batch number, part number vendor number, serial number, cost, and specifications, along with a host of other descriptive and data-driven articles. Since data represents the lifeblood of every modern company, ERP facilitates the collection, organization, analysis and distribution of this information to all individuals and systems that need it to perform their role at best. and their responsibilities.

ERP also ensures that these fields and data attributes are entered into the correct account in the company’s general ledger so that all costs are tracked and represented correctly. If the front brake pads were called “front brakes” in one software system (or perhaps in a set of spreadsheets), “brake pads” in another and “front pads” in a third, it would be difficult for society car manufacturers understand how much he spends annually on the front brake pads and if he has to change supplier or start a negotiation to get better prices.

One of the basic principles of ERP is that of centralized collection of data intended for wide distribution. Instead of numerous standalone databases with an infinite inventory of disconnected spreadsheets, ERP systems bring order into chaos and allow all users, from the CEO to the administrative staff, to create, store and use the same data deriving from common processes . With a secure and centralized data repository, everyone within the organization can be assured that the data is correct, current and complete. Data integrity is guaranteed for every activity performed within the organization, from quarterly financial statements to individual credit reports, without using error-prone spreadsheets.

It is impossible to ignore the impact of ERP in today’s corporate world. Since business data and processes are integrated into ERP systems, companies can align different departments and workflows, with significant savings in terms of profits. Examples of business benefits include:

• Optimized business insights from real-time information generated by reports
• Lower operating costs through simplified business processes and best practices
• Greater collaboration between users who share data in contracts, requests and purchase orders
• Better efficiency through a user experience common to many well-defined business functions and business processes A
• consistent infrastructure from the back office to the front office, in which all business activities have the same “look and feel”
• Greater adoption rates by users with common user experiences and designs
• Reduced risk through greater data integrity and financial controls
• Lower operating and operating costs through uniform and integrated systems

ERP

• Oracle JD Edwards : Oracle JD Edwards software meets the demands of a modern and simplified user experience. Its innovative approach increases productivity and allows companies to work smarter and faster.

• SAP It has an ERP system tested to streamline processes in all business areas: procurement, production, service, sales, financial management and HR. 

•  SAGE Designed as a management system for small businesses, Sage offers an ERP system capable of adapting to the growth of your company and the changing needs of your business.

What is the difference between ERP and CRM?

ERP and CRM are not the same thing, even if someone sometimes confuses them. Let’s start with the acronyms. 

ERP: stands for “Enterprise Resource Planning”: the literal translation would be “planning of enterprise resources”. It 

•  is a management system, technically an information system, which integrates all the business processes of a company: from sales to purchases , from inventory management to accounting and so on. 

CRM, however, is for Customer Relationship Management, 

• in which case we speak of a management strategy for all interactions that take place with customers, and prospects.

not really same thing, then. On the one hand the company resources, on the other the customers; on the one hand the organizational flows, on the other a database of names (to simplify). 

Your priority is an ERP if:

1. You have to manage complex warehouses , times of the production department
2. Administer the accounting
3. Coordinate the distribution chain (in slang supply chain) 

Your priority is CRM if:

1. You have to automate customer management and profiling processes, 
2. Manage rep arts sales and analyze the results, but above all 
3. If you want to optimize the internal processes and centralize the information of your company in a single tool.

The combination of a CRM with an ERP instead is Targeted Operating Model (TOM) once the business has reached SME size.

Artecha develops custom built ERMs, CRMs to clients including ad hoc solutions such as integration, business intelligence and automation.

Data is the set of techniques and methodologies that have as their object the extraction of useful information from large quantities of data through automatic or semi-automatic methods and the scientific, corporate / industrial or operational use of them.

The statistic can otherwise be defined as “extraction of useful information from datasets”.

The concept of data mining is similar, but with a substantial difference: statistics allow you to process general information about a population (e.g. unemployment rates, births), while data mining is used to look for correlations between multiple variables relative to individuals individuals; for example knowing the average behavior of the customers of a telephone company I try to predict how much the average customer will spend in the near future.

In essence, data mining is “the analysis, from a mathematical point of view, performed on large databases”, typically preceded by other stages of preparation / transformation / filtering of data such as data cleaning. The term data mining became popular in the late 1990s as a shortened version of the above definition; today data mining has a double value:

• extraction, with cutting edge analytical techniques, of implicit information, hidden from already structured data, to make it available and directly usable;
• exploration and analysis, performed automatically or semi-automatically, on large quantities of data in order to discover significant patterns (patterns or regularities).

This type of activity is crucial in many areas of scientific research, but also in other sectors (for example in that of market research). In the professional world it is used to solve different problems, ranging from the management of customer relations (CRM), to the detection of fraudulent behavior, up to the optimization of websites.

Among the techniques most used in this area are:

• Clustering;
• Neural networks;
• Decision trees;
• Analysis of associations (identification of products purchased jointly).

Another popular technique for data mining is learning by classification. This learning scheme starts from a well-defined set of classification examples for known cases, from which it is expected to deduce a way to classify unknown examples. This approach is also called “supervised”, in the sense that the learning scheme operates under the supervision implicitly provided by the classification examples for known cases; for this reason, these examples are also called training examples, or “examples for training”. Knowledge acquired through learning by classification can be represented with a decision tree.

The actual data extraction therefore comes to the end of a process involving numerous phases: 

• the sources of data are identified; 
• a single set of aggregated data is created; 
• pre-processing is carried out (data cleaning, exploratory analysis, selection, etc.);
• the data is extracted with the chosen algorithm; 
• patterns are interpreted and evaluated; 
• the last step goes from patterns to new knowledge thus acquired.

Data management:

But what is a data management strategy and how to implement it? What are the key elements for data management to be truly effective? Here are all the latest technological innovations that support companies in this delicate task, even if, as we will read, to implement an effective data management system, technology is not enough but processes, skills and governance skills are also needed.This is a fundamental commitment to take full advantage of the growing amount of information already present in the company and all those collected gradually, also in real time, which must be analyzed to understand market trends, the needs of company stakeholders and therefore to provide the most correct and, above all, information useful to business decision-makers to increase performance.

What are Big Data:

The definition big data refers to both the world of statistics and that of information technology, in fact, it indicates the collection of such a quantity of data (characterized by a large volume, but also by a wide variety) to make it necessary to use specific analytical methods and technologies to be processed and to ensure that value and knowledge are extracted there. In computer science, the meaning of big data extends to the ability to relate heterogeneous, structured and unstructured data, with the aim of discovering links and correlations between different phenomena and then making predictions.

Big data management cannot be approached as in the past when the priorities were ‘reduced’ to the governance of the data at the It level and to its use by some ‘restricted’ users.

Data sources continue to evolve and grow: ‘waves’ of new data continue to be generated not only by internal business applications but by public resources (such as the web and social media), mobile platforms, data services and, always more, from things and sensors (IoT-Internet of Things, just think that according to the Internet of Things Observatory of the School of Management of the Milan Polytechnic, the adoption of IoT in sectors such as the Smart Home and Industrial IoT grew in 2018 52% and 40% respectively, this means that the data generated by the devices located in these areas will increase exponentially). “The Big Data Management strategy cannot fail to take these aspects into account, often linked to the characteristics of volume, speed and variety of Big data in continuous growth and evolution. For companies, it becomes essential to succeed, according to a logic of continuous improvement, in identifying new sources and incorporating them into data management platforms. “

In the era of big data, therefore, it is essential to be able to ‘capture’ and archive all the useful data and since their usefulness is often not assessable a priori, it becomes a challenge to be able to have them all available (some data that could be irrelevant in the current business context, such as for example the mobile data of the Gps, could actually be relevant to the objectives of future business). “Until a few years ago the efforts and costs to be able to capture and maintain all this data were excessive”, reads the Forrester report, “but today innovative and low-cost technologies such as Hadoop have made this approach possible”;

The goal of big data analysis is not to report on what has happened but to understand how this can help make better decisions. This means changing the big data analysis model by opting for so-called ‘descriptive’, ‘predictive’, ‘prescriptive’ approaches, i.e. taking advantage of big data analytics through which to generate ‘insights’, knowledge useful for decision-making processes (for example anticipating the needs of the customer knowing their preferences and habits in real-time). Success in this goal requires new skills, starting with data scientists; moreover, it means using artificial intelligence techniques, big data analytics technologies, machine learning algorithms, advanced visualization tools, data mining, pattern recognition, natural language processing, signal processing and implementing the most advanced hardware technologies to create the technological platforms that they try to imitate the human brain: all this generates useful and ‘not obvious’ information in support of the company’s competitiveness and profitability;

release data quickly and freely to all those in need: it may seem obvious but we know well how the history of IT has shown how much the ‘silos’ approach also applies to data, often residing in non-shared and difficult databases to be

integrated.

Big Data Technologies:

Hadoop Ecosystem:  It is an open source framework for the distributed processing of large data sets. It has grown large enough to contain an entire ecosystem of related software, and many commercial big data solutions are based on Hadoop.

NoSQL Databases: NoSQL databases store unstructured data and provide fast performance. This means that it offers flexibility by managing a wide variety of high-volume data types. Some examples of NoSQL databases include MongoDB, Redis and Cassandra

 Blockchain: Blockchain is mainly used in payment functions, commitment and can speed up transactions, reduce fraud and increase financial security. It is also the distributed database technology that is under the Bitcoin currency. An excellent choice for Big Data applications in sensitive sectors because it is highly secure.

Business case 

An Open Source Approach to Log Analytics with Big Data In the Trenches with Big Data & Search – A Blog and Video Series  Searchtechnologies.com says: “Companies had used registries for Insight long before big data became the next interesting thing. But with the exponential growth of log files, managing and analyzing logs has become so daunting that it becomes almost impossible. How did we leverage open source big data to process over 600 GB per day for faster, more accurate and cheaper log analysis? ”

Top Five High-Impact Use Cases for Big Data Analytics: “This eBook outlines these use cases and includes examples from real customers of how other organizations have used Datameer’s big data analytics solution to unlock the value of their data and deliver true commercial value. ” From datameer.com

Cloud:

In computing, the English term cloud computing indicates a paradigm of provision of services offered on demand by a supplier to an end customer through the Internet network starting from a set of pre-existing resources, configurable and available remotely in the form of a distributed architecture.

By using various types of processing units (CPUs), fixed or mobile mass memories such as RAM, internal or external hard disks, CDs / DVDs, USB keys, etc., a computer is able to process, store, recover programs and data.

In the case of computers connected in a local (LAN) or geographical (WAN) network, the possibility of processing / archiving / recovery can be extended to other remote computers and devices located on the network itself.

By taking advantage of cloud computing technology, users connected to a cloud provider can perform all these tasks, even through a simple internet browser.

The cloud computing system has three distinct factors:

• Service provider (cloud provider) – Offers services (virtual servers, storage, complete applications (eg cloud database) generally according to a “pay-per-use” model;
• Administrator customer – Choose and configure the services offered by the supplier, generally offering added value such as software applications;
• End customer – Use the services properly configured by the administrator customer.

Although the term is rather vague and appears to be used in different contexts with different meanings,can be distinguished three basic types of cloud computing services:

• SaaS (Software as a Service) – It consists in the use of programs installed on a remote server, that is, outside the physical computer or the local LAN, often through a web server. in part the philosophy of a term today in disuse, ASP (Application service provider.)

Market Solutions: Microsoft Office 365, G Suite apps, Salesforce

• DaaS (Data as a Service) – With this service, only the data that users can access through any application are made available via the web as if they were resident on a local disk.

Market Solutions: Xignite, D&B Hoovers

• HaaS (Hardware as a Service) – With this service the user sends data to a computer which is processed by computers made available and returned to the initial user.

To these three main services, others may be integrated:

• PaaS (Platform as a Service) – Instead of one or more single programs, a software platform that can be made up of different services, programs, libraries, etc. is executed remotely. 

Market Solutions: Microsoft Azure, AWS Elastic Beanstalk, 

• IaaS (Infrastructure as a Service) – In addition to remote virtual resources, hardware resources are also made available, such as servers, network capacity, storage systems, archive and backup. The characteristic of IaaS is that resources are instantiated on demand or demand when a platform needs it.

Market Solutions: AWS, Microsoft Azure, Cisco Metacloud

The term cloud computing differs however from grid computing which is instead a paradigm oriented towards distributed computing and, in general, requires that applications be designed in a specific way.

Business cases: 

• Cloud-Based Analytics: A Business Case For CFOs: According to Digitalistmag.com : “The emerging technological advances resulting from today’s digital reality are penetrating all corporate fields with impressive speed, including financial operations. Cloud-based analytics is one of the contemporary innovative digital resources for financial operations that must be assimilated into the strategy of any competitive market operator. “

• Creating the Cloud Business Case: Scovering the fundamental commercial levers that AWS offers to its customers; work through a framework to help identify the possible benefits of moving to the cloud; and outlines the steps necessary to create a Cloud business case.

The visual and practical navigation experience is a factor that has a lot of grip on people and directly affects the conversion possibilities. For this the sites should take care of both the user experience and the user interface, to ensure users a pleasant and intuitive browsing experience.

What is UX Design

UX Design (User Experience Design) includes those processes aimed at increasing user satisfaction by improving ease of navigation and making the consultation of web pages more intuitive.

UX Design, therefore, “sets the rules” for the correct implementation of the functions and elements that a site or app should have to meet the public’s satisfaction.

UX Design is a very important aspect, because the first impression that accessing a site or app arouses in the viewer affects the chances of completing the conversions.

The User Experience is essential, to help users find answers to their needs as quickly as possible, such as quickly finding product models, solving problems in loading orders, easily requesting information, etc.

The study of the User Experience contemplates all the changes, in terms of graphics and programming, to make a web product pleasant and inviting for users.

It includes various disciplines including:

• psychology: the psychological factors that lead to appreciating or rejecting an element
• interaction design: intuitiveness in the way people interact with the product/service

The user experience is fundamental in the purchasing processes, to make them more intuitive and immediate. Thanks to studies aimed at improving usability, the activities are optimized to become simpler and faster to complete.

What is UI design

UI design (User Interface Design) is the “visual rib” of UX design and includes the way the web product is presented, primarily the interface from the visual point of view.

UI design studies the interaction between man and device and aims to make browsing more inviting, in accordance with the style and communication of the brand.

The UI includes the elements that act as an interface between man and the content and inspires their realization in a coherent perspective from the user’s point of view.

UI design pushes to make the best choices in terms of language and style and to make them coherent with the brand identity. This discipline guides us, to the adoption of pleasant and effective fonts and colors for users, to placing the elements in the most easily identifiable positions and to making the retrieval of information quick and intuitive.

Here are the basic points:

1 Information ArchitectureInformation: Architecture makes sure that the app’s core business is actually achieved: the app can be very beautiful and very responsive, but if it does not lend itself to the purpose set by the company, it serves no purpose.

2  Interaction DesignInteraction Design: takes care of how the user will use the app, and how to guide him in using it. It is important to stress that “interactions” do not refer only to those that start actively and passively from the user.

3 Usability is that feature of the app to be user friendly. In general, an app is user friendly when the user is able to independently understand how to use the app, ergonomically, efficiently, and in a memorable way. The app must be easy to navigate, the information easy to interpret, and in the event of errors, these must be intelligible.

4 Wireframing: Wireframing, therefore, tests all the features of the app and in general also the look & feel, except for any details, finishes and graphic-content contents. Where there are no graphic content elements, wireframes are used.

5 Visual Design: Taking care of Visual Design is like putting the icing on the cake: in this phase, animations and feedback are finalized and refined, to influence user behavior.

6 Create customers: The most popular apps have a very high number of users. Applications such as Instagram, Facebook or Candy Crush, one of the most popular games for smartphones, have an exceptional UX that has attracted millions of people. It is the satisfaction of the user experience that makes them faithful to the app, making sure that they do not uninstall it in a few days but, on the contrary, dedicate a constant spot on their mobile phone.

Software for UX / UI Design

There are many software for UI / UX Design, today we will consider 3 of them: 

• One of the most used software for UI / UX Design is Sketch: with its intuitive interface you can create and collaborate to make your ideas become real, it’s a mix between Photoshop and Illustrator. The only flaw it could have is that it is only for Mac

• Another widely used software is Figma: Unlike the previous one it is from Windows, this also has a very intuitive and quick to use interface. This software has both paid and free options.

• The last software I want to talk about today is probably also the most famous, I speak of Adobe Illustrator: One of the best tools to create logos, graphics, applications, websites, etc … It is available on both Mac and Windows

In short:

• Use conventional elements, So don’t use too complex fonts or icons that on other apps have another meaning;
• Maintain a certain uniformity in the interface: graphics, colors, spaces;
• Do not hinder the efficiency of the app with functions that are not really needed and that risk confusing the user or moving him from the ideal navigation path;
• Make your application as interactive as possible;
• Don’t complicate what can be simplified.

Cyber ​​risks increase with the increase and spread of legacy systems and data centers, public cloud services and SaaS applications.

In 2020, the main cyber risk carriers will continue to be emails and applications open to the Internet. According to experts, computer viruses conveyed by email are evolving rapidly and hackers find new methods every day to evade traditional security solutions. Each company must, therefore, equip itself with the most advanced protection systems in line with the evolution of cyber risks and cyber attacks, and also be supervised by external professionals able to offer advice on the implementation of information system protection measures.

Cloud, SaaS applications, brute force attacks

The most serious threat expected is partly new. More and more companies are adopting serverless platforms to reduce costs and integrate cloud applications, which use storage space accessible anywhere. These are the now widespread SaaS applications.

Switching to serverless does not solve security problems. Web application security is often overlooked because most organizations lack the skills and resources needed to manage these solutions. Many companies assume that the necessary protection is provided by their hosting service, which however hardly offers adequate coverage.

For these critical issues that are not addressed, cloud-based collaboration and production software are among the targets preferred by hackers. Given the frequent phishing campaigns, the focus remains on compromising accounts that can be used for future attacks. Traditional brute force attacks on cloud services, which gain access to an authorized account to crack encrypted data and steal information for fraud purposes, will also continue in 2020.

Email Phishing

Conversation Interception, Counterfeit People’s Voice, and Attacks Highly targeted will make BEC attacks even more convincing. Spearphishing easily draws in deception, seen pointing at one person, the so-called spray phishing, a mass attack that tries to involve as many victims as possible and is less personal and credible.

An example of pishing is fake emails from Netflix, in which they say that the account has been blocked due to a non-payment.

Complex Infection Processes

In 2019 email attacks were done via dangerous URLs to distribute malware. Users are warned against opening documents received from unknown senders, but the growing use of applications and cloud storage has accustomed them to click on various links to view, share a multitude of content and interact. Cybercriminals will take advantage of these developments because URLs can mask even more difficult to detect infection processes.

Emails may seem like simple messages from colleagues who want to share documents, but they can hide a serious danger. An example can be read in this article. 

Ransomware

Ransomware is primarily aimed at high redemption operations to unlock servers and endpoints, but they play a secondary role compared to infections that use Trojans and RATs, making prevention and defense essential. Firms that will be affected by ransomware have already been compromised by a host of malware that creates future vulnerabilities and puts them at risk of losing data and intellectual property.

Privacy and GDPR compliance

In 2020, IT security managers in the company must be fully aware of the proliferation of privacy and compliance laws that are implemented all over the world. The GDPR is just the beginning: you need to be ready to adapt to the introduction of similar regulations, with huge and increasingly complex implications especially for companies operating on an international scale. An increasing number of companies rely on public cloud infrastructures and solutions, it is also true that human error will continue to be the leading cause of violations, especially for incorrect configurations or unresolved vulnerabilities.

Training must be a central asset

Automated systems can protect mailboxes from many threats, but users are the final line of defense, especially with regards to voice and SMS phishing. Consequently, training is an essential component of security. Often even in cases where it is organized, organizations are very selective about the users to be involved, due to the limited resources dedicated to employee training.

An extended cyber risk

Summarizing the biggest threats to cybersecurity will come from the continuous proliferation of the network, from the transition to cloud systems and from the extension to critical infrastructures and industrial control systems.

In an interconnected world, an attack against a company soon extends to the whole chain. Supply chain vulnerabilities were the protagonists of attacks on major retailers in the years 2013 and 2014. Cyber ​​attackers took advantage of the supply chain for every type of business, from the theft of credit card credentials to compromised business email and techniques of attack will become even more sophisticated later in the year. Companies must more carefully select the partners to rely on, also based on their email protection system, to avoid risks of compromise and exploitation of vulnerabilities. The spread of the 5G network will allow attackers to steal data from compromised devices. Managing the problem in an inadequate way risks having excessive impacts on business continuity and company profits.

Python what it is used for

Python is a language for machine learning. Most machine learning courses have been written using the Python language and coding education as a whole has adopted Python as the language to be learned, with extensive use in small computer courses such as Raspberry Pi (AMD) or others core.

Instagram is the largest site Django is running, which is a Python web framework: a back end.

Django is a robust and elegant framework, and therefore the problem does not lie in its shortcomings intended as lack of features.

Pygame is the resource main for learning Python and game mechanics. Some good games have been written in Python.

Thanks to its highly readable nature and intuitive syntax, many find it easier to learn Python.

Python for artificial intelligence

Python is a glue language for applications that recall machine learning modules suitable for creating so-called AI systems. Indeed, for machine learning and deep learning, Python offers, despite being an interpreted (slow) programming language, some advantages:

• Python can access many external libraries (modules) with useful functions for scientific calculation. This avoids having to develop them from scratch. External Python libraries are often developed with other high-level compiled languages ​​such as the C language and Fortran. Therefore, they are very fast in execution.
• Python language is similar to natural language (English language).
• It is much easier to use than other programming languages ​​such as C or Java

Python and the main libraries for machine learning.

Python is a high-level, object-oriented programming language, suitable for developing distributed applications, scripting, numerical computation and system testing. It was conceived by Guido van Rossum in the early nineties. The name was chosen because of van Rossum’s passion for Monty Python and for their television series Monty Python’s Flying Circus.

The most immediately recognizable features of Python are the untyped variables and the use of indentation to define the specifications. Other distinctive features are the overloading of operators and functions through delegation, the presence of a rich assortment of basic types and functions and standard libraries, advanced syntax such as slicing and list comprehension.

Although Python is generally considered an interpreted language, in reality the source code is not converted directly to machine language. In fact, it passes first from a pre-compilation phase in bytecode, which is almost always reused after the first execution of the program, thus avoiding to reinterpret the source each time and increasing performance. It is also possible to distribute Python programs directly in bytecode, totally skipping the interpretation phase by the end user and obtaining closed source Python programs.

This is also possible thanks to the large set of libraries, that is, sets of routines and written functions that perform a certain task, which it possesses and can recall as needed. Libraries are often confused with the terms framework and packages.

Libraries

Python has an extensive standard library, making it suitable for many uses. In addition to the modules of the standard library, others written in C or Python can be added to meet your particular needs. Among the modules already available there are for writing web applications: Mime, Http and all other Internet standards are supported. Modules are also available to create GUI applications, to connect to relational databases, to use regular expressions. The standard library is one of Python’s strengths. In fact, it is compatible with all platforms, with the exception of a few functions, clearly indicated in the documentation as specific to a particular platform. The library can be viewed as a set of modules where each module contains simple instructions and definitions. The combination of various modules, therefore of instruction code, constitutes a library. Often the modules have already been written by other developers, and there is no need to start over every time. Their purpose is to simplify tasks, helping developers write only a few lines instead of a large amount of commands. The library code calls classes and methods that normally define specific operations in an area of ​​the domain. For example, there are some math libraries that can cause the developer to simply call the function without repeating the implementation of how an algorithm works.

Packages

To understand what packages are, you can imagine the structure of directories where files are stored on the computer disk. Usually we don’t store all our files in the same location. We use a well-organized directory hierarchy for easier access. Similar files are kept in the same directory, for example, we could keep all the songs in the “music” directory. Like this, Python has packages for directories and modules for files. Since a directory can contain subdirectories and files, similarly, a Python package can have sub-packages and modules. To make Python treat a directory as a package, it must contain a file called __init__.py. This file can be left blank but generally the initialization code for that package is placed in this file.

Framework

Unlike libraries, framework means “an abstraction, in which the software that provides generic functionality can be selectively modified by additional code written by the user, thus providing specific software for the application”. The framework can be considered as a software tool that provides a way to create and run web applications and to do so it often makes use of libraries and packages. Using a web framework it is not necessary to write code on your own and waste time looking for possible calculation errors and bugs. At the beginning of web development, all applications were hand-coded and only the developer of a particular app could change or distribute it. Web frameworks have introduced an easy way out of this trap. Their variety now works well for both static and dynamic web pages. We can have two types of web Framework:

• Server-side: also defined as a back end framework, they are software applications that facilitate the writing, maintenance and scalability of web applications. They provide tools and libraries that simplify common Web development activities, including routing URLs to appropriate managers, interaction with databases, support sessions and user authorization, formatting output (e.g. Html , Json, Xml) and improving security from web attacks.
• Client-side: also called the frontend framework, it consists of a package consisting of a structure of standard code files and folders (Html, Css, JS documents, etc.). It essentially deals with the outward-facing parts of a site or web application. In short, what a user sees when they open the app.

There is a third situation (called the Full-stack Framework) which is the combination of both the frontend and backend ends. A full stack developer is an all-rounder. They are responsible for all levels of development, from how the server is set up to the CSS related to the design. It must be said that it is complex to manage both sides.If you also want to know the list of web frameworks written in Phyton language you can see the following Link (one of the most popular is Django).

Circular economy, what is it? 

The circular economy is a generic term to define an economy designed to be able to regenerate itself. In a circular economy, the flows of materials are of two types: biological ones, capable of being reintegrated into the biosphere, and technical ones, destined to be re-valued without entering the biosphere “. In practice, it is a zero-waste economy, where any product is consumed and disposed of without leaving a trace.

Renewable energies are very important as the modularity and versatility of the objects, which can and must be used in various contexts in order to last as long as possible.

The circular economy presupposes a systemic way of thinking, which does not end in the design of products intended for a single purpose. It is an economy that not only protects the environment and saves on production and management costs, but also produces profits.

The circular economy involves the development of a real economy to be opposed to the linear one that goes from the production of a product to its becoming waste.

Cradle to cradle. This theory eliminates the concept of waste, because “waste is nourishment”; it is based exclusively on renewable energy and respects man and the environment, going to preserve the health of ecosystems and the impact.

• Performance economy. Walter Stahel added to the C2C theory the “closed-loop” approach of the production process which includes four main objectives: to extend the life cycle of products, to create durable goods, to carry out product renewal activities and to avoid waste.

• Biomimicry (imitation of life), ie the study of the best ideas of nature and the imitation of designs and processes to solve the problems of human beings. Three the most important principles: study and emulate nature; use an ecological standard to judge the sustainability of our innovations; evaluate nature not to understand what to get out of it but what we can learn from it.

• Industrial ecology. Industrial ecology, also considered the science of sustainability, is the study of matter and energy flows through industrial systems. 

• Natural capitalism, which refers to the whole range of natural assets, including earth, air, water and all living things. It is based on four pillars: to radically increase the productivity of natural resources; acquire models and production materials inspired by biology; a business model aimed at guaranteeing a sequence of services; reinvest in natural capital.

• Blue economy. Or “use the resources available in a cascade system, where the refusal of a product becomes the input to produce a new waterfall”.

• Regenerative design, which has become the frame of the circular economy.

Cleantech – 

The world, without defined boundaries, of clean technology (cleantech), includes all the technologies that serve to ‘make it clean’, that is to limit (or even where possible to eliminate) the environmental impact of a given production process.

Today, the use of clean technologies includes practices such as recycling of waste, the use of renewable energy sources (wind, solar, etc.), the rationalization of transport and lighting sources, the reduction of packaging volumes and, in broader sense, all environmental choices that aim to drastically reduce the use of natural resources, and cut or eliminate emissions and waste.

Circular Economy and sustainability in a few years have become absolute protagonists of the Tech industry both in B2C and B2B. Below are some examples of the most interesting projects in this regard:

– Agricolus: an Italian platform that operates in the precision farming area, offering various cloud applications to farmers and agronomists, thanks to which these professionals will be able to follow the production process at 360 ° and make data-driven decisions, reducing costs, improving and ensuring higher quality of the agricultural product, with a view to sustainability.

– Junker: Mobile application for smartphones that helps citizens to correctly and quickly sort household waste, helping to reduce the fraction of unsorted waste. It has been included in the European Parliament’s circular economy white paper.

– FruitsApp: worldwide B2B marketplace for the purchase and sale of fruit and vegetables. The platform offers several tools to help the parties to conclude deals: chats, bargaining systems, profiling. It has been accelerated by the Startupbootcamp Foodtech Accelerator

– Timealy: this application allows the daily unsold of fresh food to be offered at a paltry price to residents and commuters. The platform is currently being launched in London.

Technology also has a strong potential for the development of the circular economy between companies in B2B. An example is the Liam robot, which with its 29 arms can disassemble a discarded iPhone in 11 seconds, recovering a large part of the reusable materials of which it is composed, thus avoiding huge waste of value. But also the Loop Rocks platform created by the construction company NCC, through which secondary construction materials are made available at low cost between construction sites, thus reducing disposal costs, waste and costs for the purchasing companies.

CRM Automation 

Customer Relation Management and Software, these two seemingly independent words have with the dawn of the internet and the rise of online business become so incredibly intertwined that whenever we mention CRM, it almost always includes Software. 

CRM Origins 

Pinpointing when exactly CRM was first invented is a challenge. If we talk about the acronym itself, we can zoom-in on the 90s. And more specifically to former Oracle employee, Tom Siebel. Siebel who failed to convince his superiors to sell their SFA (Salesforce automation software) as a standalone product left Oracle and started his own company, Siebel Systems. 

Siebel Systems became the lead SFA solution on the market, and its contact management system closely resembled that of modern CRM solutions. As it was a new product it did not yet have a standalone name, being referred to as CIS (Customer Information System), Enterprise Customer Management (ECM) and CRM. Eventually, though CRM won. 

The end of the 90s marks also the rise of the first primitive SaaS (Salesforce), which was largely left to its own devices by the other vendors. When the “dot-com” bubble hit, the CRM industry took a massive hit before returning to force in the second half of the 00s. Microsoft introduced Dynamics CRM and Oracle (Siebel’s former employer) acquired Siebel Systems. 

Eventually, in 2007 the modern CRM was born when Salesforce introduced its Cloud-based Force.com, proving that Cloud solutions could be tailored to various needs. 

In today’s article 

The value of a proper CRM system has at this point become almost legendary if you are seeking higher customer retention rates, better customer tracking, and data organization you will want CRM software. With the rise of AI and the prevalence of Marketing Automation – there is a new trend on the rise, CRM Automation. 

What you will find out by reading this article:

• What is CRM Automation;
• The role of Marketing Automation and;
• The Pros and Cons of CRM Automation;  

Depending on the nature of your business you may just be starting with your search for CRM Software or you may be willing to take the next step, let us be your guide. 

CRM Automation – what, how and why? 

Automation refers in this case to the ability of a given CRM software to be able to execute repetitive manual tasks without the continuous involvement of an employee. Having an automated CRM tool enables your business to save time when handling your customers and allows your employees to engage in more in-depth relationships with potential and existing customers. 

The software is often enough powered by AI to ensure that it can automate its tasks. In most cases, it requires some prior set-up so that it can execute its tasks. But you will find that plenty of CRM solutions already automate a lot of tasks for you. For example, if connected to your email account, they will automatically track interactions between your salesforce and your customers, as well as adding customers directly into your database. Find out how Artecha can help you with your CRM automation. 

Automating your CRM has the added benefit of simply removing the hassle of engaging in continuous mundane tasks for your team. However, here are six more reasons as to why you should engage in CRM Automation.  

The case for CRM and Marketing Automation

A great example of a company that automates both its CRM and Marketing is Sephora. Sephora is renowned for its beauty products and it has an outstanding reputation for when it comes down to its customer service. A Sephora shopper spends an average amount of $33.17 on skincare products, these are mostly recurring customers who are part of Sephora’s Beauty Insiders loyalty program. With the fact that it sports over 17 million members, it has to rely on automation to provide its loyal shoppers with their rewards. The icing on the cake here is the fact that 80% of sales come from its loyalty program members. 

Automation as such is used for the maintenance of continuous customer relations between the business and the customer. Having to do so manually, would be a time-draining task that would require multiple employees, by automating Sephora not only lowers its personnel expenses but also maintains better relations. 

In most cases, CRM software is developed in a web framework, such as Ruby-on-Rails. Other options include Python/Django and Node.js. When developing a CRM solution it is important to understand the nature of the business for which it is designed. At Artecha we can create a CRM solution tailored to the specifics of your business. Schedule a call with us – to talk about your business and how CRM Automation could benefit you.

The key takeaways here are: 

• Recurring customers are worth more
• Automating your CRM and Marketing will drive long-term benefits
• Loyalty programs are great for automation 

The role of Marketing automation 

Let us start off by stating a crucial point. Marketing automation is NOT CRM Automation. The two co-exist and both play a vital role in your business, but they are not the same. With that being said, let us dive into the differences. 

Marketing Automation is mostly focused on the top and middle of the Marketing Funnel (or Sales Funnel), whereas CRM Automation focuses almost solely on the bottom of the Funnel. In this case, marketing automation, of course, does for Marketing what CRM Automation does for Sales. It automates mundane and repetitive tasks. Nowadays though Marketing Automation is not just the process of automating Email campaigns – it also integrates with Social Media to help you schedule your posts and much more. That is, however, its key aspect, campaigns. 

Marketing is still about generating leads, for that purpose you can run various campaigns to find and acquire those leads, but at some point, you will need to move those leads further along the funnel – and that is where CRM Automation comes into full-force. 

CRM Automation helps take the information from the automated Marketing campaigns and begins to qualify its leads by providing a rating. The more information is fed into the CRM system the more it will be able to assess the value of a potential lead obtained through the automated Marketing process. 

This is why the best CRM Automation software integrates with the best Marketing Automation software.

Benefits of CRM Automation 

The amount of value you can obtain from implementing CRM Automation within your business depends on a lot of factors, but it can be summarized in five cohesive points.

1. Save valuable time by automating repetitive time-consuming tasks
2. Help qualify and zoom-in on better prospects 
3. Improve sales conversion rates
4. Positively impact the general customer satisfaction rate
5. Boost company Profits 

As you can see the points do follow a chronological order, and this makes perfect sense. As your Sales Reps can automate tasks they can focus on obtaining more information from potential customers with the time they gain through automation. This, in turn, allows the CRM Automated software to obtain more information about said prospects, which can identify which prospects are more likely to convert. That helps with better conversion rates, whilst also helping out your Customer Service at a later stage. All-in-all it boosts your company’s profit! 

B2B and CRM Automation 

Artificial Intelligence is no longer something remote and from science fiction, it has become part of our lives and will increasingly be a daily tool both in businesses and in personal life. 

Real-time artificial intelligence is a way of completely transforming the customer experience. The highly personalized customer interactions are theoretically the optimal approach. AI allows a better understanding of customers in different contexts and channels, it can detect certain signals and understand the unique intent of each customer before he acts. Based on real-time data, AI is able to provide customized offers automatically or guide customer service operators to propose the most suitable offer at the most appropriate time. In highly regulated sectors, artificial intelligence can be a valid transparency tool to demonstrate why you are presenting a specific offer to specific customers.

In order for artificial intelligence to bring significant results, it is useful to clarify the meaning of customer experience as a sequence of brand-customer interactions through different touch points along the way, throughout its life cycle. It is a matter of managing everything in the best way since the impact of these interactions are crucial to guarantee a better experience, capable of generating more loyal customers and ready to promote the brand externally. 

Marketing can align content with the indications that come from AI and innovate the strategy, thanks to models based on both company data and external information. Thanks to the knowledge of customer behavior, it is also possible to carry out predictive and more effective target analyses to convey online content closer to the customer’s interests and e-mail campaigns, messages on smartphones and advertising.

A fact that will amaze, customers prefer to solve their problems themselves. In 2012, a Forrester research reported that 67% of people used online FAQ guides in that year, in 2015 Forrester repeated the search and in just 3 years, the percentage increased to as much as 81% and is in continuous growth. In the digital age, nobody wants to wait and for an answer by waiting on the phone and with the chatbots, voice bots and virtual assistants it is possible to avoid these bad experiences related to waiting. 

You can land on the brand’s website, open the chat and get the answer you are looking for automatically, the same with a call with the voice bot. They are not perfect yet, but these customer support tools improve every day by learning from layered questions, nuances of writing styles and tone of voice. 

Generic content and communications no longer work, consumers today are much more selective than they used to be and are constantly looking for experiences tailored for them that perfectly respond to their needs, preferences, and objectives. Research has shown that 49% of consumers confess that they are more likely to buy on impulse if they receive a personalized message from the brand. Artificial Intelligence platforms do not suffer the stress of everyday life, they can be programmed to always be positive, even when dealing with a very demanding or nervous customer. 

Keep in mind that 82% of consumers are willing to leave a brand if the customer service is not good enough, so investments in artificial intelligence are essential. According to recent research, 52% of consumers still have difficulties finding the product they are looking for online, therefore 68% choose the second choice if the first one is not available or not present online. 

Customer Experience today must embrace new technologies and adopting Artificial Intelligence tools means making the customer support process simpler and leaner. This ensures that we prioritize and respond effectively to customer needs. At the same time, buyers feel encouraged to connect with your brand.

Insights are obtained from the analysis that allows you to predict what customers will want and expect – and even prevent them. Accenture has found that 89% of customers are frustrated by having to repeat their problems to multiple agents, so knowing in advance what customers want is a powerful tool. AI, analytics and automation can help offer customers this experience, using predictive engagement.

Predictive engagement tools allow you to understand in advance what customers think, so you can interact with them when, where and how they want – even before they get in touch. Analytics can show that customers who have had a specific problem tend to have another type of problem as well. 

The use of predictive involvement allows you to ask questions and verify the second problem in the same chat, reducing calls, improving the effectiveness of the first contact and overall customer satisfaction.

The best customer service is preventive and is based on knowing what customers think. 

Predictive analytics and engagement concern the prediction of customer behavior, arriving early when necessary and offering agent support to customers browsing the site and improving the overall experience by identifying and interacting with potential customers and at the time and through right channels. About 67% of customers motivate their abandonment with the poor customer experience, but only 1 out of 26 dissatisfied customers actually complain: the others simply leave. Therefore, good customer experience is critical to reducing customer loss.

That’s why AI can help a lot to increase customer satisfaction and therefore also your turnover.

We currently live in the data era, in which a large amount of it is collected and stored every day.

In the time this article was written:

• There are 4,156,513,325 users on the Internet, it would take more than 128 years to count them.

• 1,755,606,975 websites on the Internet.

• 168.673.726.872 e-mails sent only today.

• 4,723,747,823 Google searches 

• 4,033,234 blog posts 

• 4,553,543,234 YouTube video views today only.

This is a lot of data to manage, even for computers. 

Machine Learning is a branch of Artificial Intelligence (AI) that offers computers the possibility of learning without being explicitly programmed.

In the field of computer science, machine learning is a variation of traditional programming in which a machine has the ability to learn something from the data independently, without receiving explicit instructions about it, in essence, learns from experience.

ML is a field of study that exploits the principles of computer science, automation and statistics to create statistical models and to further improve the performance of an algorithm in identifying patterns in the data

These models are generally used to do two things: 

1. Forecasting: predicting the future based on past data 

2. Inference: discovering patterns in the data

Difference between ML and AI: there is no universal agreement on the distinction between ML and artificial intelligence (AI). Artificial intelligence usually concentrates on programming computer to make decisions (based on ML models and sets of logical rules), whereas ML focuses more on predicting the future.

They are highly interconnected fields and, for most non-technical purposes, are the same.

The rudimentary algorithm with which every Machine Learning logoic starts is a linear regression algorithm.

Regression is a method of modeling a target value based on independent predictors. This method is mainly used for predicting and researching the cause and effect relationship between variables. Regression techniques differ mainly based on the number of independent variables and the type of relationship between independent and dependent variables.

New technologies have forced companies to change the way they interact with their customers.

Machine learning is also used to have very detailed data on its customers, in order to be able to meet their needs in the best possible way.

B2C

People from different disciplines are trying to apply AI to make their tasks easier and more efficient. For example, economists use AI to predict future market prices to make a profit, doctors use AI to classify whether a tumor is malignant or benign, meteorologists use AI to predict the weather, recruiters human resources use AI to check the candidates’ resume to see if the applicant meets the minimum criteria for the job, etc.

One of the ways to use machine learning is to improve the online shopping experience, personalizing it as much as possible.

Sales processes can be easily automated through chatbots that act as if they were human beings, guiding the customer and giving advice.

An example is Netflix, which recommends TV series and films based on what has already been seen, or the use of chatbots that interact with the potential customer as if they were human beings.

Healthcare

AI applications can provide personalized medical and radiographic readings. Personal health care workers can act as life coaches, reminding you to take pills, exercise or eat healthier.

Manufacturing

AI is able to analyze corporate IoT data while streaming from connected equipment to predict expected load and demand using recurring networks, a specific type of deep learning network used with sequence data.

Retail

AI offers virtual shopping features that offer personalized recommendations or present the different purchase options to the consumer. Technologies for inventory management and site configuration will also be improved with AI.

Sports

In this field, AI can be used to capture and analyze game images, provide coaches with reports on how to better organize a team, for example, including optimizing positions on the pitch and strategy.

The most common languages ​​and frameworks used nowadays or software development with regards to Machine Learning are Python and R with the support of HTML as browser.

B2B

Another advantage in the use of machine learning is also that of being able to have more and more updated information on existing customers and potentials. With machine learning, it will always be easier to have a list of potential customers knowing already how to interact with them, as you will already have all the necessary data.

 Taken as a whole, Machine Learning can have multiple uses and can be very useful if combined with a strategy that aims to optimize the company on all levels.

The success of any serious project depends on the methods used to manage it. All projects are unique, and there is no universal project management methodology yet. Each software development team has its own approach to project management. And there are no methods that are suitable for any team. However, over a long time that project management has existed, specialists have created quite a few standards and approaches, and we will introduce you to some of them. By comparing key project management methodologies we will help you decide which of them are most suitable for your needs and how you can organize your software development process.

Project management methodologies overview:

Scrum

A very flexible method. It is recognized by the Agile family as the most structured one. According to Agile principles, in Scrum, the project is divided into parts. It is suitable for instant use by the customer in order to get backlogs of products. Subsequently, the received parts are assigned according to their priority.

The most important parts are first selected for execution in the sprint (sprints in Scrum are iterations, that last from 2 to 4 weeks). As a result of the sprint, the customer receives the working increment of the product, i.e. ready to use parts. Once one sprint is completed, the project team starts the next sprint. The duration of sprints is always the same, but the team always sets it itself, evaluating its performance and project features.

Advantages of Scrum:

• Suitable for projects, that require quick results;
• Easily adapts to changes;
• Suitable for use by teams, where there are employees with little experience since all team members actively interact with each other;
• Allows you to make “quick mistakes”, i.e. receive almost instant feedback from ongoing actions thanks to sprints;
• It allows you to quickly correct errors and improve the efficiency of your software.

Disadvantages of Scrum:

• High demands on the project team (it requires a team of 5-9 people, and all team members must have needed competencies. This will secure a smooth software development process);
• All employees must be able and willing to work in a team, be capable of self-organization and actively take responsibility;
• Not suitable for all organizations and teams.

Lean

According to Agile, the project should be broken down into small sub-projects and work packages, but it’s not clear how to develop these sub-projects and work packages. The Lean method complements the principles of Agile with its workflow scheme for the high-quality execution of each iteration.

At Lean, work is broken down into small work packages, which are then implemented independently of each other. But unlike Scrum, each package has its workflow with stages. Such stages are planning, supply, testing, development. The main thing is that these stages are important for the high-quality implementation of the project.

Advantages of Lean:

• Suitable for projects requiring clear execution and even quality, since it has all the appropriate tools;
• Combines structuredness and flexibility.

Disadvantages of Lean:

• It involves a detailed and rigorous study of all the tasks and stages of the project. This includes head-to-toe guidelines from the customer.
• There is no clear workflow for the implementation of individual parts of the project, which negatively affects the speed of the entire project (this problem can be solved thanks to clear communications management within the team).
• Like Agile, Lean is not so much a method as a way of thinking and a concept with which you can independently create a project management system that will satisfy all your requirements.

Kanban

If the “Lean” method seems somewhat abstract, combined with Kanban, it becomes an excellent tool for building an effective project management system. The Kanban method involves transferring the product increment from stage to stage, as a result of which the finished product appears at the output.

Kanban allows you to suspend the execution of one task at any stage if other urgent tasks have appeared or the priority of the current one has changed. Incomplete edition, suspended dates, indefinite part of the function – for the Kanban method this is the norm.

Advantages of Kanban:

• Ideal for cohesive teams with established communication;
• Missing Deadlines;
• Significantly saves resources and allows you to comply with the budget and deadlines, because involves an accurate calculation of the burden on the performers, the correct placement of restrictions and focus on continuous improvement.

Disadvantages of Kanban

• It is more suitable for teams, whose members have overlapping skills, otherwise, the effectiveness of the method will significantly decrease;
• Not very suitable for implementing projects with hard deadlines;
• In cases where you have to deal with clearly defined deadlines, it is best to use the Scrum method.

Conclusion

Project management is a science, but not the most accurate one. There are no universal solutions in this area. If you manage to find a method, ideally suited to your project – you can consider yourself lucky, because less successful managers have to make efforts to create and configure their project management systems. Here in Artecha we can easily adapt to your needs and offer you the best project management approach. Being business-oriented, we are interested in the successful completion of any software development project we deal with.

Let us know what is your idea to grow your business and we will bring it to life using the best approach.

As a technology, natural language processing has developed and came into life with products such as Siri, Alexa and Google’s voice search. Employment of NLP helped to understand and respond to user requests.

Today’s natural language processing systems can analyze unlimited amounts of text-based data without fatigue. And make the industry more efficient in a consistent and unbiased manner. NLP is an attractive way to provide the necessary clinical information. Thanks to the voice recognition system physicians will be able to dictate their reports in a usual way. Natural language processor, in it’s turn, will translate the textual report into a structured encoded form. The coded NLP system could be stored in real-time along with the original text in a clinical repository. This would enormously increase the functionality of the voice recognition system. Moreover, further advance the healthcare IT market.

The Healthcare IT Market is expected to be $390.7 Billion by 2024 and is growing at a faster rate than the GDP of most countries.

Thanks to NLP Processes we will have more information about the treatment of the patient. This will be crucial to attract more patients and reduce costs for hospitals. Artecha has been actively involved in providing software solutions to the health care market to further advance the research.

NLP as an Attitude

Uses a keen sense of curiosity about people and approach to others. It also considers each experience as a rare and unprecedented opportunity to learn.

NLP as methodology

is based on a way of interacting with people, that reveals a startling and revolutionary premise. Behavior, communication, and change have a structure, as does every human endeavor. In particular, a structure that we can be model, learn and teach.

NLP as a technology

with innovative, reliable and proven techniques, that allow one to organize the perceptions and behavior to get an extraordinary and ecological result.

At Artecha we curate, implement and develop sophisticated text mining applications. They help to further advance the research within the medical field. We are also actively involved in providing software solutions to the health care market. Our aim is to further advance the research process with specialized clinics and boutique dental practices in the UK.

Artecha now is more and more in the healthcare industry, so If you have any question, will help your inquiry.

Agile, which appeared as a method of software development in a small team 10-15 years ago. Today it is becoming a new cultural management system for companies. Agile methodology is an innovative rethinking approach to creating a new product or service. It is based on a very simple idea: each participant of the process, each employee should be involved in the process of rethinking their tasks. Also everyone can make rational suggestions.

When comprehending Agile, it is important to know both the positive and negative aspects of this methodology. Let’s start with the pros.

Advantages

First of all, it is worth noting that Agile management is very flexible. The traditional methodology indicates specific stages of work. Agile, in it’s turn, easily adapts to the consumer of the final product and customer requirements. Therefore, the number of defects in the final product is minimized. All this is done due to a thorough quality control, which is carried out at the end of each stage.

In addition, Agile launches quickly, responds easily to changes, and allows the development team and customers to keep in touch in real time. The advantages are obvious, but let’s also talk about the cons.

Disadvantages

The disadvantages of the methodology are that, first of all, constant feedback can lead to the fact that the deadline of the project will be transferred all the time, thereby creating a threat of infinitely ongoing work. If the customer sees, for example, only the results, but has no idea of ​​the efforts required to achieve them, he will always demand improvements.

The second drawback is the need to adapt the project documentation to the changing conditions of the project. If the team is not properly informed of changes or additional functions, documents with functional requirements or architecture may not be relevant at the current time.

The third significant disadvantage of Agile is the need for frequent meetings. Of course, they contribute to increase work efficiency. Nevertheless the constant distraction of team members can affect the process negatively. Mainly because people’s attention is systematically moving away from the tasks being solved.

In conclusion, theory and practice are two different things. The Agile methodology involves the participation of the whole team in the software development process, leaving participants with familiar competencies. Such an approach will allow to understand that they all work for the same ultimate goal – a quality product for their customer. New methods and technologies and their implementation is a kind of challenge to the team. And how to come to greater efficiency is always an individual matter. Agile is not a panacea or a guarantee of success, but it allows you to set the right course and find landmarks on the way.

Knowing how to correctly evaluate the duration of the project will help to maintain a stable development process. On the stage of the formation of the project framework and analysis of requirements, this knowledge will be very helpful. Let’s take a closer look at best approaches for time-management. They will help you monitor your software development project.

Progressive JPEG method

Your project is ready for preview at any time. This means that the tab of the basic minimum functionality (draft with basic buttons) is developed at the very beginning, and then refinement only takes place. This approach performs well when the customer wants to see some work progress in three weeks. He saw your draft, thought it over and talked to you. Therefore it turned out that half of the functionality, agreed upon earlier, was no longer needed at all.

CPM (Critical Path Method)

The purpose of the method is to search within the project for such work sites, that cannot be performed simultaneously. For instance – a comparison of two pictures (the original and the processed one) can occur only after the comparison logic works. There is little use for a finished page in a browser with a container for pictures, a header and a block for social networks. It is these places in the project, that affect its execution time. The calculation of the cost of segments executed in parallel will allow you to understand how much resources are required for optimal work on tasks, and get an approximate project duration.

Expert Judgement

The method consists in attracting third-party specialists to derive the required values. Experts in the right field issue their verdict on the topic of cost or time frame. After that, you can average all the proposals, or you can try to come to a common solution during the discussion. The involvement of experts in the discussion of options is certainly more effective and will give a more accurate, reasoned and tested assessment.

Three Point Estimation

One of the most common and simple methods. In it’s framework optimistic (O = Optimistic), pessimistic (P = Pessimistic) and realistic average (M = Middle) estimates are first determined. The values ​​of P, M and O are determined expertly (in hours, days, $), for example, during a discussion within the project team. Further, the obtained values ​​of P, M, and O are substituted in the formula:

(O + 4 M + P) / 6

The result of the calculation gives an average estimate. Such a formula makes it possible to take into account possible positive and negative scenarios on the one hand. On the other hand to “smooth out” their influence and obtain a more realistic value of the assessment.

Analogous Estimation

Here, completed projects or experience in evaluating large works will play into your hands. Firstly, you need to remember what you did similar to the current project, what nuances there were. Seconfly, how much it cost per unit of time, how similar tasks were solved. To evaluate a large project, it is necessary to break it into smaller parts, using the principle of decomposition.

In conclusion, knowing all these approaches and implementing one of them in practice will help you monitor your software development project. Organize processes smoothly and reach all the desired outcomes. Do you use any of these approaches and what are the results?

Every aspect of business requires customers to make use of services to survive. Customer involvement is considered as one of the key factors for successful software development projects. Customers interact by giving precise requests, giving constant feedback to developers and testing each software release. Being an agile software development agency, we understand the importance of customer participation in each stage of the software development process. By looking through the key stages of an agile-based project we will see a customer’s direct impact on each part of the process.

Requirement Gathering

During this stage customer will be sharing with the team and developers, in particular, their vision for the project, expectation, and functionality. All the user requirements are gathered in user stories. This makes it easier to understand and maintain the value of the business throughout the whole development process. After all the requirements are gathered, the team moves on to the next stage and comes up with working prototypes.

Iterations conduction

By this stage, there are all the necessary materials gathered, and the problem to be solved is defined clearly. During this stage, the idea starts to be brought to life. First of all, everything has to be planned and then, again, agreed with the customer. Once agreed, developers implement all the features that were listed during the planning process. Customer will need to assess the product and initiate changes that will need to be implemented during the next step of the iteration. This process takes time since it will also require a lot of testing to be conducted. Again, without the customer’s participation, it will not be possible. Everyone needs to make sure that the customer will get working software with all the features initially requested.

Product delivery

Once all the QA (Quality Assurance) testing, internal and external training and documentation development are finished, the project finally can be released. A software product can also see a few releases before the final version. This helps the customer to see how the product will work after all the development part is finished. After every release customer needs to pay a lot of attention to the product, make sure all the requirements are included, and everything works smoothly.

Conclusion

Despite all the challenges and time-consuming processes, customer interaction in the software development process helps the development team to understand better all the needs of a customer, and deliver high-performing software. Implementation of agile methodology helps getting desired customer participation throughout the whole cycle. It also leads to a release of a decent software product, that meets all the initial requirements and has all the needed features.

With the introduction of 5G technology, everyone is now talking about the Internet of Things, Internet of Information. One of the other branches, where 5G can be of great use is the Internet of Skills. Projected to be a key component of the future digitalized world, Internet of Skills will enable human beings to teach, be taught and execute any actions remotely.

Internet of Skills enables you to transmit your expertise in real-time, by using robotics and haptic feedback. Modern technologies can recreate a sense of touch, by applying certain forces, vibrations to the user. For instance they can trick our skin into thinking that what we touch in a virtual world is real.

How is it currently used?

Currently, implementation examples include remote interactive teaching and remote repair services. For the Internet of Skills to become a fully integrated reality, a combination of machine interaction methods and extended communication capabilities is required. Both industry and consumers certainly show a great interest in using capabilities, that Internet of Skills can bring.

What makes the Internet of Skills work?

Short latency capabilities of 5G are essential to make the Internet of Skills work, by providing instant action-reaction. The response times on 5G are 400X faster than a blink of an eye (1 millisecond). Without the availability of 5G technology globally, this system will lack audio, visual and haptic technologies. According to Mischa Dohler, a professor of wireless communications at King’s College London, it will take another 10 years for the Internet of Skills to be fully implemented in reality.

Future of IoS

In conclusion, Internet of Skills will be an essential tool in our digitalized world. By enabling remote skill delivery it will democratize labor globally, essentially the same way as the Internet has made knowledge public to any user in the world. In the future, 5G technology in combination with sensors, tactile gloves, and virtual reality technologies will allow us to “digitize” and transmit physical skills of experienced specialists over long distances. It is projected that by 2023 there will be more than 1 billion 5G connections in the world. Therefore, we can safely prepare for the fact that the “Internet of Skills” will become the same everyday thing as the familiar Internet of Things.

New technologies and gadgets are spread at a terrifying pace. But not always it has been like this. Today we would like to talk more about what technologies, that are shaping the software development industry. Emerged technologies revolutionized the industry in the last decade, and here are our Top 5 technologies:

JavaScript

Being the only programming language to run in web browsers, it keeps improving over time. JavaScript is a provider of a path for front-end developers, that helps them to become full-stack developers. Mainly JavaScript is improved by developers, who load its applications with more capabilities and interactions than before. JavaScript has a great advantage – it has reliable execution environment. This is what makes this technology widely used, and constantly improved.

Python

According to the TIOBE Programming Community Index, Python is one of the top programming languages in 2019 and is not going to lose its positions. Python is used for desktop GUI applications, website and web applications. By having quite simple syntax rules, Python makes it easier to keep the code readable and the application maintainable.

ReactJS

ReactJS is considered as one of the best JavaScript framework candidates. Its main strength is to offer help in building software that operates at scale. ReactJS helps teams of developers to clarify how they can work together and build some reusable components, and make them easier to maintain.

Security

The more data appears in the tech world, the more vulnerable it may get. Cyberattacks have managed to bring down companies and destroy careers. One of the key tasks of any software developer to make sure the product, they create is secure and protected from any malicious attacks. This is why all the security measures are treated seriously and companies are investing actively to protect their data.

IoT

Internet of Things is one of the important technologies, that are shaping our future now. Today, IoT devices are Wi-Fi-powered, programmable, not at a high cost, and useful. Developing this technology can help us solve our customers’ problems, and be implemented in UX/UI designs to make them up-to-date.

There is still a lot to be discovered among new, emerging technologies, that are constantly changing the software development industry. Here, in Artecha, we keep learning new ways how to provide our customers with best and modern solutions.

For more information contact us!

The modern mobile network now is way more than making phone calls or sending SMS. 5G network is the next generation of mobile internet connectivity, offering faster speeds and more reliable connections on mobile devices and desktops than ever before.

5G networks are already starting to appear and are expected to be launched globally by 2020. Recently it’s speed has reached a record number of 15 Gbps in Sweden. With its low latency feature, 5G allows transmitting data in real-time without any delays. This technology is bringing vast changes to every industry. Let’s see how some of them will change.

• Industry. 5G will increase the speed of industrial robots and will unify the whole infrastructure, that will increase overall performance.
• Agriculture. 5G will enable remote control of the agricultural machinery and will help to monitor fields and herds.
• Education. 5G will enable learning through VR-broadcast of the process from master’s/teacher’s point of view.
• Medicine. 5G will let doctors from all over the world carry out remote real-time operations and gather consultation to come up with a solution for treatment.
• Communication. With the implementation of 5G people will be able to use interactive virtual reality. Users will be able to interact remotely as if they were together in reality.
• Entertainment. Possibility of fast wireless transmission of ultra-high-definition video ( 4k, 8k ), translations of the various events with the implementation of VR technology.

First trial 5G network was launched on 23rd of April 2018 in metropolis Chongqing, China. Since then 5G technology is being implemented in various metropolises all over the world. It is projected that by 2035 the infrastructure of 5G network will be supporting 22 mills. workplaces all over the globe.

Technologies do not stand still and are constantly evolving, and what was used several years ago may already be irrelevant today, or what they could not do several years ago has already become a reality. There are more modern tools that help you create web and mobile applications faster and better. Let’s look at the latest web development trends.

PWA (Progressive Web Applications)

Progressive Web Applications combine the best from browsers, websites and mobile applications. Most brands that have switched from websites to progressive web applications and have significantly improved conversions. Therefore, PWA has every reason to remain the leading trend in 2019.

Augmented data analytics

Augmented analytics –  area of augmented intelligence which uses machine learning to automatically analyze large amounts of data, get new information from them and visualize it. Software using augmented analytics will be embedded in existing business applications of enterprises and will soon begin to help in optimizing solutions.

Push notifications

Thanks to the tremendous success they have brought to countless mobile applications, push notifications have become extremely important components of most websites. Push notifications are rapidly replacing mailing lists, as they are easier to manage for both users and developers.

Cyber Security 

IT professionals are most concerned about data leaks, data privacy breaches and privacy breaches. Most organizations are preparing to defend against an unprecedented wave of cyber attacks. As a result, cybersecurity should remain the dominant trend in web development in the near future.

Single Page Applications (SPA)

As the name implies, a single-page application is a long web page, free from complex menus and navigation. Since it is easy to work with them, they look ready to increase their popularity. With the simplicity of their design and download speed, they are gaining an increasing number of users.

Chat bots

The popularity of chat bots has increased significantly. Some bots are designed to answer basic questions, but there are others that can easily answer complex ones. Integrating the bot with the site will help generate more user traffic and make the resource attractive.

Sure enough, all these leading trends are designed to  provide more opportunities for web developers, as well as for companies and the end users. Contact Artecha (Art & Tech) to implement the latest web trends for your idea or business. 

It’s amazing how quickly technology changes. Yesterday, everyone was amazed at the rapid development of native mobile applications, and today we are witnessing a new evolution.
Let`s talk about Progressive Web App technology which was announced by Google in 2015, but becoming a popular trend nowadays.

This is a technology that adds application functionality to the site. In the desktop browser, the progressive web app. And when it comes to a mobile browser, it’s PWA turns into a mobile browser.

Progressive Web App is very easy to detect, just like a regular website – just google it, click on the link to open it, and everything, you have an application on your device, ready to be shown.
The user enters the site and gets an offer to add it to the main screen. If the user accepts the offer, the site icon instantly appears on the screen of the mobile device.
After installation, PWA creates a site cache. This solves two problems: it increases download speed and makes the site available offline. That is, thanks to PWA technology, the site can be used even without an internet connection.

Important: the functionality of a progressive web application does not interfere with using the site in the usual way.

The advantage of PWAs over mobile apps is that they consume much less data. While native apps reside in the user’s phone, PWAs are only accessed when users go to the website—the only time when data is used.
Another advantage of Progressive Web App is that any page or screen on your PWA can have its own shareable direct link.
The feature of pushing notifications allow users to get timely updates from sites they love, enabling you to effectively re-engage users with customized content.

Why should business use progressive web apps?

This technology benefits both customers and users. Such an application can be developed several times cheaper and faster than the native one, which opens up many new opportunities for small companies.

PWA helps businesses:

• Get to the user’s mobile device bypassing Google Play and other app stores. 
• Make the site available offline. This even works for online stores, but information about executed deals “flies” into the store when the user has an internet connection. 
• Increase website loading speed on mobile devices. 
• Send notifications to users. 

Implementing PWA gives measurable results

Here are some examples: 
The Pinterest.com image search platform uses PWA. The results were spectacular: the number of active users increased by 103% over the week, the number of new subscribers increased by 843%.

Thanks to technology, Tinder has reduced page load times from 11.9 seconds to 4.69 seconds. PWA Tinder is 90% easier than their native application. 

PWA Uber weighs almost nothing and loads in 3 seconds even in 2G networks.

And there are many such examples. Soon, the presence of PWA will be simply necessary for any serious business, and the absence will be perceived as a significant drawback. Contact Artecha (Art & Tech) in order to create your Progressive Web App and help your business to keep up with tech trends.

A lot of people seem surprised that someone interested in computers would also be interested in art. People seem to think that software development and artists are very different kinds of work, that development is cold, precise, and methodical, whilst art is the hectic expression of some primal urge.
Both of these images are wrong. Developers and artists are most alike. What developers and artists have in common is that they’re both makers. Along with composers, architects, and writers, what developers and artists are trying to do is make good things.

Abstract thinking

Artists can take a blank canvas and use their creativity to draw something from scratch. It requires lots of abstract thinking, trying to connect things and creating something with their minds and hands.

Developers often do something similar, for instance when it comes to working with a start-up. They take an abstract problem, come up with a solution and portray it through code.

Fine-Tuning

Paintings are often created by gradual refinement. They usually begin with a sketch and gradually the details are filled in.

Developing a piece of software often starts out with a sketch on a whiteboard, iterating on the idea, developing the concept, backtracking when it goes down the wrong route, identifying the features and ultimately ending into a prototype.

Inspiring and taking inspiration

Many famous artists have been deeply influenced by work done before their time. The ideas artists are exposed to through other people and media inevitably influence them.

Van Gogh studied the works of many artists including Rembrandt, and his Rest from Work was clearly inspired by Millet’s Noonday Rest.

Developers are inspired through work done by others, as well. People Google-search to see if a problem they’re trying solving has been solved before and reuse it, often saving days or weeks of work. Most developers use and contribute to open source projects in GitHub or they get ideas from fellow developers on Stack Overflow. This is a common practice in the software development community.

Focus

There’s a reason why developers wear headphones. Coding often requires deep concentration and focus. It’s often when they are at their most productive. Developers are moving codes around trying to get a piece of software into perfect shape or design, very similar to sculpting a piece of art using clay with their hands.  Time passes and brilliant solutions appear, complex code with numerous pieces that interact are created and it all just flows out automatically.

Artists are also known for getting in the zone, often spending hours or even days working continuously on a project looking for the perception.

Perseverance

Great pieces of work often take weeks or months, whether it’s creating a painting or coding a software system.  Some artists are lucky and create that great piece of work quickly, but for most others, it takes perseverance and a long time. It supposedly took Michelangelo more than 25 years to create the Sistine Chapel.

Creating a great piece of software can often take weeks or even months until getting to the final product. Like an artist, developers might get stuck, however, unlike a piece of art that is unveiled in the gallery, the software is best when it is first launched as an MVP and then followed up with many subsequent releases.

Forever young

Pieces of art decay over time. That’s why there are art restorers. Software is known to decay over time too, first incurring technical debt and sometimes eventually turning into legacy code. Code that is not maintained, pruned or updated degrades over time. This is why developers refactor code, to keep it up to date and remove technical debt. Refactoring is the fine art restoration of the digital world.

Many developers are artists

Many developers are actually artists, literally.

Leonardo da Vinci was a painter and sculptor. He was also an engineer, architect, mathematician, and inventor.

The Ruby community is well-known for having many developers who are also artists and musicians. Jonathan Gillette, was a writer, cartoonist, artist, and computer programmer most notable for his work with the Ruby programming language.

For many developers today, the line between programming and art has become increasingly blurred. Today’s web developers are required to have design skills, be able to use Photoshop, style pages and content, and understand web aesthetic and responsive web design.

Artecha

To conclude, Software Development can be perceived as being analytical, scientific, precise, but it’s some of that. Development requires inspiration, creativity and technical ability.

Artecha (Art & Tech) was ideated with the mission of combining these skills and embed them within one team of specialists coming from different backgrounds with the common driver of developing digital solutions and launch them into the market same as a work of art.