"D2I is a very timely, strategically important research program with great potential, which may have strong positive impact on Finland's economic growth and competitiveness."
– Piero Bonissone and Xiaohui Liu, international evaluators of D2I

Data to Intelligence (D2I) started in April 2012 and ended in June 2016. The program was financed by Tekes and coordinated by DIGILE (now DIMECC) together with the management team. The high-level objective was to boost Finnish international competitiveness through intelligent (context-sensitive, personalized, proactive) data processing technologies linked to new data-driven services that add measurable value, leading to increased knowledge, comfort, productivity or effectiveness. To reach this objective, the program developed intelligent methods and tools for managing, refining and utilizing diverse data sources, and created new, innovative data-intensive business models and services based on these methods.

D2I had a matrix organization consisting of 2 work packages and 7 research areas. The Proof of Concepts (POC) work package was concerned primarily with user needs and business models and processes, while the algorithms, platforms and tools needed for processing large masses of data were developed in the Enabling Methodologies (MET) work package. The work package structure was complemented by business sector oriented research areas, which focused on applications in traffic, multimedia, security, industry, customer intelligence, wellness and forestry.

D2I organization

This organization structure provided the flexibility needed to pursue new opportunities identified during the course of the program. For example, two entirely new research areas – BeWell and Forest Big Data – were started in the middle of the 4 year term.

The D2I partner organizations included 27 large enterprises, 26 SMEs, and 17 research institutes and universities. Data analysis is one of the strongest areas of computer science in Finland, and combining it with the subject matter expertise and real-world data sets of companies enabled successful technology transfer. In addition, the program trained data scientists with both strong academic background and industrial experience. This kind of expertise is in high demand, and recruitment from the research partners to companies was another important form of technology transfer.