Good practice:

Data Analytics Accelerator

PROBLEM TO BE SOLVED: The practice addresses the issues of low rate of exploitation of data in various business processes inside SMEs. The purpose is to improve the know-how and practical experience of data analytics in the regional businesses. The project provides a necessary first push for adapting new methods, which requires major investments and competence from the participant companies.
HOW RESULTS ARE REACHED: The project was structured into two tiers: pilots and learning activities:
(1) PILOTS: data analytics pilots with 3 companies. Topics include predictive maintenance, usage trends analysis and cybersecurity. Tools used: machine learning (random forest), clustering, big data computing (Spark), visualisation.
(2) LEARNING: awareness-raising, topic-specific workshops with 20-25 companies.
The workshops are thematically organised sessions, introducing participant businesses into key aspects of data analytics uptake.

STAKEHOLDERS INVOLVED: The project was planned and coordinated by CSC – IT Centre for Science. One of the strongest implementers was the Kajaani University of Applied Sciences. Main beneficiaries are the SMEs involved in the pilot projects and workshops. It can be argued that this project is associated with the valorisation of the LUMI super-computer potential for Kainuu.

The project was funded from the ERDF (70%) through the Regional Council of Kainuu. The remaining 30% came from the City of Kajaani and businesses. Total funding of the project throughout the 30 month duration was 3 man-years. This included experts used to address particular topics.

Kainuu region companies are better prepared to adopt data analytics or to capitalise on the business potential of data analytics.
The Kajaani University of Applied Sciences planned and organised a new, 3-year education programme “From Data to Artificial Intelligence”. The
DATA-ANALYTICS ACCELERATOR project was capitalised to support the planning of the curriculum.

Deepening the work with the SMEs participating in the workshops:.SMEs were reluctant to share business issues or data with 3rd party.
Often businesses were not able to commit enough of the working time from the key personnel, i.e. development activities were left, sometimes, somewhat disconnected

The project concept is open, and freely transferable. It is important to take into account, when planning new, related projects the lessons learnt from the Data accelerator project, both the success and the challenging issues. In addition to the information discussed above,
- awareness raising of data analytics methods to SMEs is an important and practical way to prepare SMEs to Industry 4.0 thinking. It was encouraging part that businesses were willing to open up operational issues that can be developed through data analytics and other I4.0 methods.
- The 2 tier model of the project allowed the more advanced companies to do actual development with the data analytics experts.
- projects promoting uptake of advanced technologies, such as the Data Accelerator project, are important also as pilots contributing filed data to new education curricula as well as research issues is also important.


Contact: Aleksi Kallio, [email protected] .