The Magical BI Adoption Formula: Embedded BI
One of the aspects I appreciate about the #BIWisdom tweetchats I conduct each Friday on Twitter is that the participants dont sidestep any issues. Of course, we cant be too long-winded in a 140-character tweet anyway, but often their comments are pithy like the woman who dubbed embedded BI as the magical BI adoption formula in the recent tweetchat about the embedded BI market study report we recently published. Thats a much faster, bottom-line way to convey what Ive advocated for several years that it could help get more eyes on the data and is a great way to engage users in BI in the context of a familiar application.
But, of course, there is also great value in having BI instrumentation above the applications. What about top-down BI vs. embedded BI? Is it a non-issue? Are both necessary? Those are the thought-provoking questions I posed to #BIWisdom participants recently.
Someone tweeted, I submit that companies often think embedded BI has the same capabilities as the rest of the BI spectrum. But thats not true. And I agreed with him. Users mostly want to view content and interact or navigate using embedded BI.
Another tweetchat attendee added he believes the power of embedded BI is that it gives direct insight and provides more context.
Embedded BI is the technological capability to include BI features and functions as an inherent part of another application. The pervasive perception among the tweetchat attendees, as evident in their tweets below, is that the benefit is users dont need to know theyre using BI.
- They have easy access to tools as part of their daily workflow.
- It enables users without having to go through a big rollout.
- The big thing is people dont like switching applications to an analytics tool, so embedded BI is attractive.
- The ease of use makes users happy. Quick results at their fingertip.
But the perception of embedded BI among tweetchat attendees differed around the actual use. The following tweeted discussion in particular highlights the differences:
- Embedded BI is needed for analyzing, acting and optimizing on business objectives.
- It is needed more for operational daily use as part of the business transactions. Finance, operations take your pick. Its complimentary to ERP bundled reports.
- So, adoption of embedded BI is like bundled packaged reports with ERP that users dont like or use?
- Embedded BI apps need to interact with other BI data stores or within transactional apps.
- Data monetization is a driver of increased embedded BI.
I added to that discussion with data points from the report on our recent embedded BI market study:
- Companies are embedding BI in portals to extend access to employees and within specific apps (ERP, for example).
- Interaction with live objects is key, presumably in synch with other app content.
- It depends on the use case, but embedded BI interacts mostly against traditional data stores.
Some companies advocate data monetization as a driver, but most applications of embedded BI are for internal, employee-facing applications. Very few companies seek to expose internal data to external constituents via embedded BI. And monetizing data is tough for companies; there are many issues to contend with that have nothing to do with technology.
Bottom Line: A tweetchat member commented a year ago that the consequences of the push towards embedded BI are interesting and will keep growing in 2016 and beyond. Among BI topics, embedded BI is pretty hot right now in 2017. Im particularly interested in it because it helps realize the vision of Information Democracy, or equal access to actionable insight for all. As I explained, in my book, The Performance Management Revolution: Business Results Through Insights and Action (John Wiley & Sons, Inc., 2008), the goal of achieving Information Democracy cannot be achieved just through technology; it also requires empowering individuals.
Our recent #BIWisdom tweetchat discussion aligned with this goal. A participant tweeted, With end-user empowerment, you may finally have a data-driven culture. And another in the group added that BI insights have business value only if theyre acted upon and impact decisions. Sharing data increases that likelihood.
Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.