OBS' first doctoral dissertation on business analytics deepens the understanding of data-driven management

The first doctoral dissertation in business analytics at the business school was completed in spring 2025 under the title The potential and utilization of predictive business analytics.

The dissertation topic emerged from practical working life and the researcher’s personal interests. Maarit Matkaselkä has worked as a business analytics consultant, helping companies utilize data in management, decision-making, and operational control.

Business analytics offers tools to turn data into business value

Rapid technological advancement and the resulting changes challenge business environments, making them more dynamic and complex than before. At the same time, the volume of data continues to grow.

“Data can be analyzed from various perspectives and used, for example, in scenario building, foresight, and forecasting,” Matkaselkä explains. “Business analytics involves applying different methods, models, tools, and technologies to analyze business data.”

According to Matkaselkä, the potential offered by data is not yet being fully utilized by companies.

“Organizations recognize the business value hidden in data and want to leverage it as effectively and broadly as possible,” she notes. “However, despite the availability of advanced technologies, realizing the true value of data analytics remains challenging for many businesses.”

To address this challenge, Matkaselkä argues that research and practical working life should combine their resources, knowledge, and skills. She also hopes that modern leadership and decision-making will increasingly make use of modern, data-driven tools for analysis and scenario planning.

The dissertation aims to foster academic discussion around predictive business analytics

In her research, Maarit Matkaselkä investigates the resources and challenges of building capabilities for predictive business analytics and examines its potential, adoption, and implementation within welfare services. The dissertation fosters academic discussion around the phenomenon of predictive business analytics through a chosen research design and theoretical framework.

The findings of the study are diverse. They highlight the potential of predictive business analytics to reveal trends, identify correlations, create scenarios, run simulations, forecast the future, anticipate resource needs, and analyze markets.

“In welfare services, the possibilities of business analytics may relate to services, resources and organization, identifying indicators and triggering factors, or even patient care,” Matkaselkä explains.

According to the study, building predictive business analytics capabilities requires an organization to develop a comprehensive data culture along with the appropriate skills, tools, and processes.

“Recognized challenges in building these capabilities include a lack of data culture, data strategy and resources, legal and data-related issues, organizational resistance to change, and integrating data into everyday operations,” she elaborates.

Successful utilization of predictive business analytics in welfare services depends on collaboration between business, data, technology, and partners.

“It also requires leadership skills, technical expertise, and a data-oriented transformation of existing operations and organizational culture.”

Much fertile ground for further and complementary research

Matkaselkä is pleased that the University of Oulu is investing in business analytics education and research.

“Although there are already other doctoral candidates in business analytics on their way, I had the honor of being the first to defend a dissertation in this discipline,” she says proudly.

There is much fertile ground and many perspectives for deepening and expanding research by modifying theoretical lenses and research contexts. Future research could explore the intersection of predictive analytics and strategic-level management and decision-making.

“This kind of research design benefits from a mature data environment where descriptive analytics or reporting is already effectively and broadly in use.”

Emerging trends from the study, such as large language models and data democratization, also present interesting future research topics.

“LLMs (large language models) and data democratization enable users to query data using natural language. Combining different types of data strategies with foresight is also a compelling research avenue,” Matkaselkä adds.

She offers words of encouragement to future researchers and doctoral candidates:

“All future doctoral researchers are driven by an innate thirst for knowledge, which must be accompanied by courage, motivation, determination, and perseverance. We understand the value of research. I wish all current and future PhD candidates an inspiring journey on the path they have chosen!”

Maarit Matkaselkä’s dissertation The potential and utilization of predictive business analytics was publicly examined on 16 May, 2025, at the University of Oulu. Read the dissertation here.