The recent establishment of the Kempelen Institute of Intelligent Technologies (KInIT.sk) was preceded by dramatic events at FIIT STU, which I consider an unprecedented moral failure of the current faculty leadership.

I appreciate Professor Maria Bielikova and her team’s determination to transform the situation into a constructive solution that maintains top scientific capacities within one perspective unit.

In the same way, the reaction of companies and a large part of the IT community in supporting the new institute gives hope that our society will be able to take advantage of the technological and financial opportunities that we have here today and that the new institute will help to steer Slovakia towards a fundamental digital transformation.

Why is this important?

I will try to bring one insight. Of course, there are more reasons.

With accelerating digitisation, institutions are collecting ever-greater amounts of data. Looking at it through the eyes of a machine can bring greater insight into your own space and new, unexpected opportunities. At the same time, the complexity of the systems used is increasing. Management or control systems have to keep track of rising numbers of indicators, charts, graphs, and statistics to make optimal decisions.

Several external data sources come into play that can significantly expand decision-making capabilities beyond traditional considerations. Combining external and internal data brings a new opportunity, especially if an institution takes full advantage of machine learning capabilities.

Paradoxically, the real opportunity is not about technology; it is just a prerequisite for automating and speeding up the process. The opportunity is primarily in a deeper understanding of one’s workspace and the ability to make decisions based on a much larger set of information. These can only be understood effectively based on knowledge of your own space. Organisations that undergo an evolutionary transformation and augment the digitisation of processes with data-driven decision-making can gradually fine-tune their decision-making processes so that information provides real decision support.

Ecosystem of information

Information is created and changes its parameters in real time. Trying to keep all processes and records in one monolithic system is an ambition of the past.

A Master data management (MDM) approach, for example, can provide a foundation for understanding your data space. Each type of information should have a well-defined source. This can be an accounting system, a production or banking system, a CRM, an ordering system, an eShop, internet banking, open data, a mail server, documents, spreadsheets and free texts in a document management or knowledge base. Engaging enrichment can also come to the ecosystem through commercially available external datasets. Customer, supplier, and partner data is increasingly entering the information ecosystem.

Understanding and implementing processes that can link this data and enable its efficient exploitation without human intervention is essential for the information ecosystem.

The following processing steps create derived data by introducing automated mathematical operations. Various statistical indicators, summaries, comparisons over time, etc., are often calculated. At this point, the first phase of building a data ecosystem mostly ends. If the organisation absorbs its use, more sophisticated questions like: How can sales be increased? How to fine-tune the production process? How do we predict critical situations? That’s the space for introducing machine learning algorithms and, in some cases, big data processing, the output of which is the following data set needed for effective decision-making.

Bringing a data ecosystem to life takes several forms. A unified and connected view of data feeds back into source systems that leverage augmented information from other systems or computations. Automated processes track important metrics in broader contexts and trigger alert or intervention processes. Data is also usually transformed into data cubes that data analysts use to prepare various insights for themselves or management, often in automated reports in emails, knowledge bases, or business intelligence products. It is a living process where the data ecosystem is continuously expanded, data quality is improved, and more algorithms are applied.

Digital transformation does not mean replacing humans

Many areas can be automated in the digital world, But this does not mean replacing humans with machines that will make informed, complex decisions based on the data they collect. Machine learning brings new dimensions to the ecosystem of information on which we make decisions. Understanding and appropriately interpreting information in an organisation is a daunting task. Technology does not solve this task; it is a matter of a well-built team and processes. This is reflected in most successful projects involving experts and external consultants directly in the management processes. Organisations are driven by management. If an organisation wants to expand the involvement of data in its decision-making, transformation projects need to be managed and defined at the management level. IT will deliver what is required but not formulate the assignment and coordination for such transformation.

Building an entire ecosystem in Slovakia is necessary to manage the transition from organisational management to natural, data-driven management. On the side of institutions and companies, it is the managers who will see the need and the potential of the opportunity. This can be stimulated by education, examples from similar areas, and organic adaptation of existing solutions used today. Vendors can understand their customers’ domain and provide increasingly sophisticated IT solutions. Still, some problems are so complex and solutions so costly that they are not feasible for one firm.

Why does the country need KInIT experts?

I believe that the new institute, KInIT.sk, will be able to act as an extension of the ecosystem mentioned above, will become an active partner of the IT industry alongside academic research activities and will eventually bring solutions specific to our environment. From the perspective of the IT industry, I see a lot of room in Slovak-specific areas, such as the Slovak language itself and the processing of language-specific data for Slovakia. At the same time, some data sources are unique to our environment, so there are unlikely universal foreign solutions. Last, I also see an enormous scope for transferring scientific knowledge from abroad and publishing new information, suggestions, and inspirations in our space and context.

Due to the specifics of scientific institutions’ functioning, we do not have many examples of successful cooperation between commerce and the scientific world in Slovakia. Collaboration with universities has been especially problematic. Establishing a private institute in intelligent technologies and informatics simultaneously brings another challenge and hope. Perhaps we will finally learn to effectively combine Slovakia’s scientific potential with applied practice.

Gabriel Lachmann, CEO, EEA, part of BiQ Group