Högskolan i Skövde

Data-driven government agencies frequently rely on using analytics (descriptive, predictive, prescriptive) together with high-quality data. A common benefit of being a data-driven agency is the ability to make informed decisions. Previous research has provided findings from case studies and small sample surveys that mix different types of organizations, e.g., municipalities and government agencies. Hence, it is difficult to generalize for an entire segment, e.g., government agencies, on what they do in practice to become more data driven. This paper aims to investigate what government agencies in Sweden do in practice to become more data driven. Data was collected from 137 government agencies via a web-based questionnaire, and analyzed with respect to current practices, systematicity, and strategies. The findings are that most government agencies are not characterized as data-driven, lack systematicity in using analyzed data in decision-making, and mostly develop strategies for technical platforms and data.

Artificial Intelligence (AI) can take Business Intelligence (BI) to the next level by empowering users in their daily decision-making tasks. Just like Self-Service Business Intelligence (SSBI), AI integrated business analytics comes with many benefits, but also with numerous implementation challenges. In fact, typical SSBI implementation challenges like data quality, data governance, and employee training are equally relevant when integrating AI. Hence, lessons learned from development of SSBI education could increase data literacy and AI literacy. Two case studies of SSBI education in large BI consultancy firms have identified five SSBI education steps: (1) increase the interest of using data; (2) introduce data to all users; (3) clean and define data to create standard reports; (4) develop SSBI data governance and (5) become self-reliant on accessing and using data. SSBI education can create a foundation that leads to being better prepared for the implementation and use of more advanced AI analytics.

Challenges and opportunities for becoming a data-driven organization have been investigated in the literature mostly from the perspective of large organizations. In contrast to previous research, this paper targets a public school in a small municipality that sets up a pilot project for monitoring IoT sensors for indoor and outdoor climate. The public school intended to become more data-driven in its decision-making. Data was collected through interviews, documents, and ethnographic studies. The main findings are that the pilot experienced: i) technical challenges due to faulty sensors and IoT interoperability problems, ii) development challenges due to an unbalanced cross-functional network and poor requirement documentation, iii) raised awareness on indoor and outdoor climate among children and teachers, iv) mixed results of using the dashboards' visualizations, and v) lack of supporting guidelines on how to integrate the system into the organization's daily work.

Key elements for scaling advanced analytics.

Research on assessing a group’s maturity in data-driven culture is rare and fragmented. This article investigates how maturity in data-driven culture can be assessed from a historical perspective. A case study was done on how the Education Council evolved in analytics maturity and as a group during 2014-2023. The assessment showed that the Education Council experienced both successful progression of group development and usage of analytics, as well as regression in group development and analytics usage. The practical implications of the findings are that group leaders need to be aware of the interplay between analytics usage and group development when planning to improve their group’s maturity in data-driven culture.

Master data management programs are large by nature since the aim is to provide the entire enterprise with a shared trusted view of the organisation’s most critical data assets. In this paper, we present what dimensions and activities a master data management program in a large organisation should consider and how to monitor such a program once it is up and running. A heatmap approach is used to visualize the inherent complexity of a master data management program. Our approach is derived from participating in four different master data management programs in four different global organisations during 2007-2020.

Establishing a data-driven culture in teams is on the agenda for many managers and analytics leaders. With a data-driven culture in place, it is envisioned that investments in analytics can be used to their full potential. In practice, most organizations struggle to establish a data-driven culture in teams and have few tools available to assess the level of maturity.

Related research has focused on maturity models in business intelligence & analytics that target the organizational level. Hence, these maturity models provide limited support for assessing the team level, e.g., why some teams do not develop a data-driven culture.

This paper used a systematic literature review and an online questionnaire to develop a matrix for assessing a team's maturity in data-driven culture. The matrix synthesizes previous work in analytics and group development. Findings from the literature review revealed a mismatch between problems addressed by the research community and perceived problems in practice by organizations.

Becoming a data-driven organization is a vision for several organizations. It has been frequently mentioned in the literature that data-driven organizations are likely to be more successful than organizations that mostly make decisions on gut feeling. However, few organizations make a successful shift to become data-driven, due to a number of different types of barriers. This article investigates, the initial journey to become a data-driven organization for 13 organizations. Data has been collected via documents and interviews, and then analyzed with respect to: i) how they scaled up the usage of analytics to become data-driven; ii) strategies developed; iii) barriers encountered; and iv) usage of an overall change process. The findings are that most organizations start their journey via a pilot project, take shortcuts when developing strategies, encounter previously reported top barriers, and do not use an overall change management process.

Conducting pilot projects are a common approach among organizations to test and evaluate new technology. A pilot project is often conducted to remove uncertainties from a large-scale project and should be limited in time and scope. Nowadays, several organizations are testing and evaluating artificial intelligence techniques and more advanced forms of analytics via pilot projects. Unfortunately, many organizations are experiencing problems in scaling-up the findings from pilot projects to the rest of the organization. Hence, results from pilot projects become siloed with limited business value. In this article, we present an overview of barriers for conducting and scaling-up data-driven pilot projects. Lack of senior management support is a frequently mentioned top barrier in the literature. In response to this, we present our recommendations on what type of activities can be performed, to increase the chances of getting a positive response from senior management regarding scaling-up the usage of artificial intelligence and advanced analytics within an organization.