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Barry Devlin

As one of the founders of data warehousing back in the mid-1980s, a question I increasingly ask myself over 25 years later is: Are our prior architectural and design decisions still relevant in the light of today's business needs and technological advances? I'll pose this and related questions in this blog as I see industry announcements and changes in way businesses make decisions. I'd love to hear your answers and, indeed, questions in the same vein.

About the author >

Dr. Barry Devlin is among the foremost authorities in the world on business insight and data warehousing. He was responsible for the definition of IBM's data warehouse architecture in the mid '80s and authored the first paper on the topic in the IBM Systems Journal in 1988. He is a widely respected consultant and lecturer on this and related topics, and author of the comprehensive book Data Warehouse: From Architecture to Implementation.

Barry's interest today covers the wider field of a fully integrated business, covering informational, operational and collaborative environments and, in particular, how to present the end user with an holistic experience of the business through IT. These aims, and a growing conviction that the original data warehouse architecture struggles to meet modern business needs for near real-time business intelligence (BI) and support for big data, drove Barry’s latest book, Business unIntelligence: Insight and Innovation Beyond Analytics, now available in print and eBook editions.

Barry has worked in the IT industry for more than 30 years, mainly as a Distinguished Engineer for IBM in Dublin, Ireland. He is now founder and principal of 9sight Consulting, specializing in the human, organizational and IT implications and design of deep business insight solutions.

Editor's Note: Find more articles and resources in Barry's BeyeNETWORK Expert Channel and blog. Be sure to visit today!

The fourth in a series of posts introducing the concepts and messages of my forthcoming book, "Business unIntelligence--Insight and Innovation Beyond Analytics and Big Data", available in mid-October.

m3.jpgIn my third article in this series, I defined information as "the recorded and stored symbols and signs we use to describe the world and our thoughts about it, and to communicate with each other. Information is mostly digital but also includes paper, books and analogue recordings". I also pointed out that data is best thought of as a rather specialized form of information, optimized for numerical/logical processing by computers. These observations have interesting implications for data modeling and knowledge management. They emerge for the modern meaning model, m3, shown on the right (click to enlarge) which I offer as a successor to Ackoff's DIKW pyramid.

Let me start with a perhaps contentious statement. Knowledge exists only in the human mind and body. If you accept that, you'll need to reconsider most of what you've read about knowledge management. People mostly resist having the insides of their heads managed. Much of what is proposed in knowledge management programs is actually information management. The real value of knowledge management is that it causes us to consider the relationship between recorded information and its interior, mental representation. The distinction between explicit and tacit knowledge is key. We can see that explicit knowledge and (mostly soft) information can be interconverted rather easily through the traditional methods of learning and documentation. Tacit knowledge, a more insightful and personal experience of the world, can only be more recently, and still imperfectly, captured as information by videoing.  More often, we must try to articulate it first as explicit knowledge and then document it.

Modeling, as traditionally defined and done, resides in the bottom layer of the model. This is rather disappointing, because a real understanding of how the business works requires us to look at explicit knowledge, at least, and often the tacit knowledge that business users carry about how things really work. The m3 model thus demands a rethinking of the boundaries of and approaches to modeling. We need to move from data modeling to information modeling and even to knowledge modeling if we are to make real progress in reinventing business in the technologically based, emerging world.

The top layer of this model, meaning, offers even greater challenges to the typical IT thinker. Meaning is a wholly subjective world. Rationality has a role, but a much narrower one than allowed in Western thought. Furthermore, modern research has discovered that meaning is highly influenced by our interpersonal interactions; we are, after all, social primates. And of even more importance to creating a new model for supporting decision making, meaning, or the stories we tell ourselves, is a significant contributor to the decisions we make. Herein lies the reason why BI has often under-delivered in improved decision making: rational "intelligence" and hard information play a much smaller role than suggested by the theories of decision making used in management schools and business for more than fifty years now. Time for a change, I think?

Thumbnail image for Business unIntelligence Cover.jpgI will be further exploring the themes and messages of "Business unIntelligence: Insight and Innovation Beyond Analytics and Big Data" over the coming weeks.  You can pre-order the book at the site above; it will be available in mid-October. For starters, check out my presentation at the BrightTALK Business Intelligence and Big Data Analytics Summit, recorded on September 11th. And my upcoming webinar: "Beyond BI is... Business unIntelligence" on Thursday, Sep 26, 2013 1pm BST.

Posted September 17, 2013 2:40 AM
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