Data to Insight, A Sense Making Perspective in Business Intelligence and Analytics
Sammanfattning: Motivation and Objective: This study investigates how analysts generate from data using business intelligence tools. The motivation for study at the premise that organizations are finding it very challenging to gener-ate insights from a vast amount of structured, semi-structured and unstructured data. The ini-tial problem background indicate that this may be because organizations are silo driven, have data governance issues, data quality issues, BI analyst lack skills as well as data literacy com-petence that is needed to analyse and interpret data to understand customers, and among oth-er issues, which limits the use of business intelligence and analytics to generate insights (Deloitte, 2017; Harvard Business Review, 2012; American Marketing Association, 2017). Hence, the process of generating insights is a very slow process, and organizations want better adoption and understanding to increase progress in daily operations/production and other are-as so that they may use their capabilities to the best of their extent, as well as generate real business value (Deloitte, 2017; Harvard Business Review, 2012; American Marketing Associa-tion, 2017). For this research, two case sites are selected for the study: IKEA AB, Sweden and CDON AB, Sweden. For these two case sites, the study further investigates two specific domains: BI Innovation at IKEA and Business Controlling at CDON. Method of data gathering and analysis: The motivation is to understand the BI analysts’ sensemaking perspective when generating insights from data, the qualitative case study research method is adopted as a more appropri-ate method. Accordingly, the unit of analysis are BI analysts at their respective domains, and semi-structured interviews were conducted to collect research data. The findings are presented individually for each case site, for which a cross case analysis have been conducted using a matrix table to bring forth a unified understanding of insight generation from data. Overview of findings: The study finds several key activities that act as the generators of insight in regard to how data is evaluated. Insight-generating actions and characteristics by the BI-analysts include: having clear business questions, Just-In-Time models, access to integrated databases using modern technology, business domain understanding, understanding of data quality and plau-sibility, understanding of data governance issues, application and sharing of solutions (dash-boards, reports etc.) through a community approach, as well as having skills in BI tools, maths, statistics and programming. Granted these requirements for insight generation are fulfilled, insight generation from data will be less time-consuming, in that way the process of insight generation will become faster. The end goals of organizations are to generate overall business value and the adoption of BI&A will become more achievable. Further Implications: Data governance in terms of control mechanism needs to be put into place for organi-zations to avoid legal and financial complications, which is a subject that needs further research. In addition, it affects the over data quality. Expert BI-skills and experience needs to be shared among other BI analysts and busi-ness personnel to close the knowledge gap in organizations. This is also a subject area that calls for further research. Data quality through good-bookkeeping, data governance and the use of advanced BI-tools can insure and impact reproducibility and reusability. This is another area that would benefit from being a focus by organizations and may likewise benefit from fur-ther elaboration in future research. The experimental approach towards BI&A can possibly be improved by organizations by them supporting such practices. Also, further research is needed in this area as well. The understanding of business knowledge, pure logics, and clear business models can be improved by organization from within through training programs and other methods in the BI&A domain. This area also needs further research.
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