According to Deloitte Technology Industry Outlook, the General Data Protection Regulation of the European Union (GDPR) requires that technical companies must make architectural and technical changes to comply with the regulations. This is due to the large amount of user data that they contain directly or via their cloud solutions for business customers. However, different industries and companies across Europe are now realizing the technology deficit compared to American and Asian companies and are increasingly focusing on both modernizing and large investments in software development projects. In this article we discuss current trends in the software development industry.

The State of European Tech 2018 recently released their report, which concluded that “while Europe’s general economy and traditional industries are stuck in trouble, fast-growing technology is the best hope for growth.” Some key findings from the report:

$ 23 billion invested only in 2018, against just $ 5 billion in 2013
There were four technical stock market introductions that reached valuations of more than $ 5 billion on the opening day, including Spotify
Europe had three of the top 10 largest technical stock market introductions of 2018 around the world.

Now that we have seen the enormous growth in the software development industry, let’s take a closer look at the rising trends in business models based on Machine Learning from the technology companies and see what impact this has on all industries where they operate, such as healthcare, legally , production, car and agriculture.

Convergence of IoT, AI & Blockchain:
There are a number of expressions currently used to describe artificial intelligence, including machine learning & predictive analysis. Let’s take a look at some of the highlights related to investing in software-based development-based software development projects:
Improves marketing processes and helps with accurate sales forecasts
Accurate medical diagnoses and assistance with treatments
Significant savings in time and personnel costs thanks to a significant reduction in data entry related work / jobs
Real-time customer feedback continuous continuous product updates and augmentation

AI will eventually automate Software Development
Company-wide CRM and ERP systems generate huge data sets for analysis. Software development in Machine Learning / AI aims to synchronize Hardware operating systems, server software and mobile applications to generate insights and patterns to improve product / system performance, profitability and customer satisfaction. When machine learning models are applied to these datasets, IT activity transforms from reactive to predictive to future-proof. When the power of AI / Machine Learning-based software development is applied to organizations, they have reported a consistent increase in their profits, while they have adjusted in real time or at least reacted effectively in a very short time to changing market conditions and expectations of customers.

Software development methodologies:
As companies prepare for rapid response and customer satisfaction, we can discover some of the most important software development methodologies / models that are currently being applied in different organizations, along with their advantages and disadvantages:

Waterfall – Waterfall model focuses primarily on creating a good project plan for the software development process with a linear flow with a specific set of actions for users and that further software development of a function is made progressive after completion of the previous one. Software development based on this model is incompatible with the changing requirements and therefore the decreasing popularity.

Agile – Agile software development is the most popular framework for software engineering projects. It is a methodology that helps to align digital transformation initiatives with business needs. Because digital transformation is a continuous process, agile often helps to deliver valuable results for the company instead of waiting for a long lead time as observed in traditional software development projects.
Extreme programming (XP) – Although it is similar to the Agile framework, XP takes more time and staff compared to the other approaches. XP is mainly used when the functions of the product for software development requirements are not clear, while it ensures greater traceability within the modeling procedure, it is perfect for complicated software development projects.

Minimum Viable Product (MVP) – It is a product with exactly enough fun