Business Intelligence ROI Remains Elusive; A next-generation approach to “ease of use” may hold the answer

ROI on Business Intelligence was the topic of the March 6th, 2013 of the TDWI NYC Tri-State Chapter, on whose board I serve.

Keynote speaker Neil Raden, founder of CEO of Hired Brains Research, and author, with James Taylor, of Smart Enough Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions.

Neil Raden has long been concerned about the fact that usage rates for large-sale BI systems has “stalled at 10 to 20 percent of users, depending on which survey you believe.” Billions of dollars have been invested in presenting insights to business managers, but frequently the ROI has been soft and difficult to measure. 
Of course BI will survive, but Raden said “we may not recognize it ... the need to analyze and use data will not go away, but BI will be part of a 'decision management continuum' incorporating predictive modeling, machine learning, natural language processing, business rules, traditional BI, visualization, and collaboration capabilities.”  Neil's talk focused on these questions:
∎ Why do many Business Intelligence implementations fail to achieve their potential?
∎ Will a broader definition of the concept enable better results?
∎ How can you optimize BI systems when you are not in complete control?
∎ What best practices and case studies are most instructive?

A video of Neil's Presentation can be found here: ROI on Business Intelligence - Neil Raden on Next-Generation "Ease ... 
Neil Raden's talk was followed by a presentation by Tony Baer of Ovum Research entitled " Making Big Data Manageable: Getting Better Results by Managing Big Data Quality"
Intersecting with Neil Raden's keynote, Mr Baer asked “what does it take to turn the promise of Big Data into tangible results?”  Big opportunities to benefit from new technology have come and gone, yet the consistent challenge has been translating new potential into concrete benefits.  Mr. Baer shared a practical perspective on making big data manageable by understanding key challenges you must overcome to leverage big data, especially the unique data quality issues the Big Data sources introduce.
Mr. Baer also shared his insight that while Business Intelligence and Big Data are viewed and managed separately, in reality "Big Data and Business Intelligence must converge." Big Data needs to be approached with "less of a silo mentality," and so does Business Intelligence.

Video:  Tony Baer - Big Data and Business Intelligence Must Converge

Jaime Fitzgerald, Founder and Managing Director at Fitzgerald Analytics, served on the Board of Directors of the TDWI NYC Chapter, and helped organize the event.

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Tags: BI, Big, Business, Data, Intelligence, modeling, predictive


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Comment by Stephen Penn, DM, PMP on April 8, 2013 at 3:48am

I agree everything presented in the slides. The focus is on making tools easier for decision-makers to use and then hoping the decision-makers adopt those tools, but it's only one approach. I believe there should be an equal perspective of helping the decision-makers adopt practices that use those tools.

Business Intelligence (BI) and Data Science (DS) need a framework, or maturity model, for integrating the decision-making process with the tools. The framework should stress the mechanical aspects of data warehousing, master data management, and BI. It should also stress the organic aspects of visioning, strategy, open discourse, and open innovation. As tools become easier to use (as the article above suggests), a complimenting framework would help managers control and use those tools.

Understanding the decision-making process, typical behavior of managers, and how strategy is developed provides a foundation for developing such a framework. This framework would mature as tools mature. As tools become easier to use, this framework would become more applicable and worthwhile. Thus, both easier-to-use tools and best practices wrapped in a framework improve organizational performance.

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