In the last installment of this series, I described everyday BI users as data consumers who use technology to drive insight from diverse data sources. I want to further expand on this idea that everyday BI users are insight-driven data consumers, and articulate what I consider the three key steps to insight.
This final piece sets the stage for our analyses and experiments in the coming posts of the Everyday BI series.
Step One: Observation
In this first step, we’re re primarily occupied with gathering basic data to answer rudimentary questions. At this early stage:
-Our initial observations and perceptions steer our preliminary conclusions, and we’re focused on finding out what happened.
-Our appetite for detail is low, and the action typically taken is in the form of seeking an update or searching for data.
-The frequency with which we access this information may vary depending on the availability and update frequency of the underlying source data. Examples include:
-Reviewing our home utility bill (monthly)
-Checking financial updates after markets close (daily)
-Looking up nutrition information in a supermarket (ad-hoc)
Conclusions are typically:
-One directional (what is the difference +/- or what is the change up/down)-Reached within a matter of seconds thanks to the use of colors, symbols, charts, or other visualization techniques.
Step Two: Perspective
Once we determine through observation what happened, we next attempt to seek a perspective on the magnitude of change. During the perspective stage, we follow several steps:
-First, we start to ask questions that will sort our results into the “good news” or “bad news” categories.
-Then, we make a quick examination of trends, previously published results, or snapshots in order to support our preliminary conclusions
-No further actions are required if:
-Our primary objective was validation, not investigation
-We trust the data provider
-Our findings confirm our assumptions or remove the perceived risk, thus eliminating the need for further detail.
-If needed, we can put current results in context with larger data sets by performing some sort of historical or comparative analysis, like using a historical chart or a comparison in a product category.
Step Three: Insight
In this third step, we will perform the final analysis to get insight because, while step two is likely to provide partial insight, it may not explain why something happened or identify any contributing factors.
-Actionable insight will be limited without some degree of root-cause analysis.
-Acquiring the necessary information typically completes the picture for most queries. For example, a major recall may explain the red flag for an older model product we’re interested in, or the use of additional services without the discounted rates may explain the sudden spike in our monthly phone bill.
-This stage is the most time consuming because:
-If the data is sourced externally, we may not have much control in its presentation.
-The information may be delivered in an unstructured format (news feeds), thus slowing the insight acquisition.
-Unlike the previous steps, we may need to process detailed information that is slower than processing summary information.
Bottom Line
Everyday BI users rely on technology solutions that can deliver insight-driven and action-ready information. This is important for businesses because better-informed consumers tend to make better-informed decisions. And they tend to be more loyal because they recognize the improvements that these solutions can make in their daily lives.
How have you seen these three steps to insight unfold in your everyday BI experiences?
Stay tuned for the next installment of my Everyday BI series.
If you like this blog, you may also enjoy the Mobile BI Design Framework series.
By Kaan Turnali, EPM Channel Contributor, from: http://www.the-decisionfactor.com/business-intelligence/everyday-bi-3-steps-insight/
As Global Senior Director, Business Intelligence (BI), for SAP’s Global Customer Operations (GCO) Reporting & Analytics Platform, Kaan Turnali is responsible for the development, oversight, and execution of strategy for the BI platform across GCO’s worldwide user base. In addition, he manages special mobile BI projects for the Office of co-CEO Bill McDermott and the GCO senior management team. Prior to joining SAP in 2006, he worked as a senior BI consultant specializing in strategy, design, and development of enterprise BI solutions for SMEs and Fortune 500 companies. His background and experience in the integration of business and technology span over two decades. He is also an adjunct professor, teaching BI in the doctor of business administration program at Wilmington University. See Kaan’s articles on EPM Channel here.
Leave a Reply