Data-based decision making is only as good people’s initiative and ability to make sense of the data, and the ability of their tools to help them think critically about it. The goal of the Elev8 (pronounced “elevate”) project was to determine the opportunities for, and barriers to, reaching “casual” users of quantitative business information with a more engaging experience. The research fell within the area of Business Intelligence (BI), but was not targeted at a particular existing SAP product. Rather, it was fundamental research relevant to the entire SAP portfolio.
The study revealed that to reach casual business users, SAP must radically revisit its current solutions and features. Yet it also showed a clear opportunity to provide deeper value to these users. Across industries, we identified some common obstacles to deeper analytic thinking about data, where helping people take a small step to the next level would have a high probability of delivering business value.
Spectrum of users involved in the study:
• People bracketing the active user/casual user boundary
• People for whom internal quantitative business data is at least potentially important, whether or not they also need and use external quantitative information
• People whose primary job is not to provide analytical support for others, but who consume quantitative information as an input to their own business thinking and decision-making
• People who are not power users or BI pros, although the latter served as secondary sources of information about casual users in their companies or work group
• We conducted a series of preliminary focus groups with more than 50 professional analysts, active users and casual users in Cologne, Germany and the Bay Area, California, in order to learn how these groups viewed, conceptualized, and articulated the role of quantitative information in their jobs, and how the use of quantitative information supported them in their roles.
• The team then conducted in-depth, onsite, field research consisting of 2-hour visits with 54 participants in a range of business units/functions at 8 companies. In these visits we explored specific examples of business tasks in which quantitative information played an important role. This resulted in approximately 125 detailed case examples, covering a broad range of business task categories
Eight companies were involved in the study from the following sectors:
• Higher education
• Enterprise software
• Online retail
What were the main lessons learned?
Contrary to expectations, the research indicated that among casual users , there was little explicit, pent-up demand for more or better support for using quantitative information. The limitations in their use were not the result of ease-of-use problems blocking them from doing analytical work they wanted to do. Rather people had difficulty recognizing the limitations of their current approach. People tended to apply unvalidated rules of thumb and “accepted wisdom” and beliefs for estimating frequency, ratios, likelihood, etc. This was true even when the data was available to them and simple additional calculations would have led to a different picture of the relevant business issue.
Sometimes, these limitations in analytical thinking led to faulty conclusions about the meaning of data (e.g., using an average when the median or mode was more relevant to their business question, using averaging percentages across items without weighting them according to the widely varying frequency of those items ), but more often, it led to overlooking implications of the data for their business decisions, or to over- or under-estimating the support in the data for business decisions.
In addition, the research showed a complex interplay and lack of integration between qualitative and quantitative information and thinking. Qualitative information was often essential for making sense of patterns or exceptions in quantitative information, but to a surprising extent this information resided only in individual or “tribal” memory and was not documented or accessible to others who might use the same data.
Outcome of the research project:
The findings suggesting that casual users were content with their current analytical approaches and did not envision the potential benefits of going somewhat further may seem discouraging. However, our research also supported the possibility that, with a limited number of targeted experience design strategies, it could be possible to help users discover unanticipated value in their data. These findings and their implications have been shared with solution owners and product teams working in the BI space. Additionally, the User Experience team has outlined experience design concepts based on the Elev8 research data.
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