Alex Bekker

With over 30 years of overall experience in IT and data analytics, Alex has been in charge of ScienceSoft’s Data Analytics Practice since 2010. Under Alex’s leadership, the department started business intelligence consulting and implementation practice and extended the service portfolio with big data, machine and deep learning. As the new practices evolved, the team of consultants, data architects, engineers and developers doubled.

Alex contributed a lot to developing partnerships in the data analytics area with technology leaders, such as Microsoft, Oracle and Amazon. The expertise of our data analytics team is acknowledged by Microsoft Solution Partner for Data and AI certification. 

With 20+ years of experience in project management, Alex has been heading multiple data analytics and AI projects, from two months-long engagements to a long-term project that lasted 7 years. Some of Alex’ recent projects include big data implementation for advertising channel analysis in 10+ countries (where such technologies as Apache Hadoop, Apache Hive and Apache Spark were used) and development of an AI model for pentesting automation.

When building data solutions, Alex focuses on creating architectures that let businesses solve their tasks and achieve business goals at an optimal total cost of ownership (TCO) and with a positive ROI. During the design process, he prioritizes architectural characteristics that help his teams achieve this balance between costs and value. Alex promotes modular design and open architectures to ensure that designed systems stay flexible during further development and maintenance. He also fosters the metadata-driven approach that allows for standardized manageability processes, lets developers easily upgrade solutions with new techs, and ensures system adaptability to new business needs.

Alex shares his experience with businesses and data analytics experts, as well as popularizes complex data analytics and data management topics in his articles. For example, recently, he has classified artificial intelligence into 5 different types and explained the typical architecture for real-time big data solutions.

Alex attributes the success of data projects to striking the balance between strategic and tactical goals:

In most projects, it doesn’t take long to design a strategic data architecture — the one that will power the required software capabilities, be easy to maintain and upgrade, and eventually, drive good ROI. However, the vision you have almost always clashes with the need to solve current tasks, and quite often this means creating an architecture that is different from what you’ve pictured, and that will need to be transformed after the tactical goals are reached. The key and the most challenging part here is to find the balance between long- and short-term goals, prioritize aspects that bring maximum business value, and reach a compromise between business needs and technical feasibility.