Tag: Business Intelligence

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Thesis Eemil Häikiö

Abstract:

AI-powered dashboards are business intelligence tools that collect and present data in a comprehendible manner to enhance decision-making. The integration of artificial intelligence (AI) in data-driven decision-making (DDDM) processes is continuously increasing in modern organizations, however, current research examining AI-powered dashboards does not address the deployment of the technology. To complement prior research, this thesis aims to provide a foundation for the implementation of AI-powered dashboards by investigating the current dashboard best practices, as well as the adoption and governance of AI-powered dashboards in the context of executive decision-making. The concepts of DDDM and AI function as a foundation regarding the purpose and functionality of AI-powered dashboards, whereas IT governance provides a basis for their adoption and governance. The research methodology encompasses a review of scientific literature addressing these concepts to enable the proposal of two distinct theoretical frameworks addressing the adoption and governance of AI-powered dashboards.

The results of this study highlight three critical areas regarding AI-powered dashboards: current best practices, adoption processes, and governance. Best practices show that AI-enabled dashboards are dynamic and versatile BI tools that enhance decision-making and operational efficiency through detailed visualizations and data analysis. Concerning adoption, the study emphasizes the importance of selecting a suitable framework tailored to organizational needs, suggesting that a combination of existing models might often be necessary to integrate AI and BI within organizational environments effectively. Results addressing the governance of AI-powered dashboards emphasize the importance of BI and AI governance. Although the development of AI governance frameworks is still in its early stages compared to BI and IT governance, the findings suggest that adopting flexible and diverse governance structures enables organizations to manage the security, transparency, and accuracy risks associated with AI technologies.

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Thesis Rens van Eggelen

Abstract:

Business Intelligence (BI) is commonly used to get value from data. It does have several limitations, though: power users serving business users is a severe bottle neck. A new approach has recently emerged to solve this bottleneck and to make business users independent: Self-Service Business Intelligence (SSBI).

Research on SSBI is slowly emerging, but the adoption is still rather slow. There are several challenges to overcome during SSBI implementations, but all research focuses on the perspectives of the organization adopting SSBI and their employees. Consultants often play a large role in these implementations, but their challenges and strategies for overcoming them is yet to be researched.

To research this, a case study design is used with the single case of consultants at Deloitte, a global leader in Data & Analytics service provision, implementing SSBI. Interviews were conducted with consultants from Deloitte Switzerland and Deloitte the Netherlands. These were then analysed using Thematic Analysis. As a result, eight categories containing a total of 23 challenges and four categories containing a total of eight strategies to overcome those challenges were identified. These were discussed with existing literature and classified as to being specific to SSBI implementations, specific to broader information systems (IS) implementations, and general consulting.

The results found that consultants do not only face SSBI-specific challenges, but also IS and consulting challenges. Furthermore, they do not only use SSBI strategies, but also strategies from IS implementations and general consulting. Although based on past observations, knowing about these challenges and strategies can help increase the success rate of SSBI implementations, as well as increase the adoption in the future.

As such, the thesis introduced a new unit of analysis to the literature of SSBI
implementations. As SSBI consultants face similar challenges and use similar strategies as other consultants, this research does not only shine light on the complexity of SSBI implementations, but also possibly enriches BI implementations and more general IS implementations. This does require future research to validate the findings.