9sight Consulting Founder and Principal, Barry Devlin, discusses why business intelligence, usually seen by enterprises as the industry standard in enabling raw data to drive insight and judgement, might only be one part of the future decision making toolset.
Since its birth in the 1990s, business intelligence (BI) has been consistently rated across all industries as a must-have for effective understanding and management of business. However, the term business intelligence itself has long struck me as something of an oxymoron. Much of the business behaviour I’ve encountered has been far from intelligent. Perhaps I’m a little cynical, but business unintelligence seemed a more appropriate term, with decisions sometimes taken despite information available and BI often relegated to little more than generating reports filled with backward-looking data. Furthermore, intelligence is surely setting the bar far too low in the second decade of the 21st century; can’t business do better? What about intuition, insight or inspiration? Add social and collaborative aspects — given that business is largely a collective, collaborative venture—and we have the need for and possibility of something far more sophisticated. Perhaps, I thought, Business unIntelligence could be more than the opposite of, but the solution to the problems besetting BI today? Could the cognitive dissonance of the phrase spur business into action, to finally benefit from extensive information, adaptive processes and the strengths of actual people? Rationality of thought and far beyond it. Logic of process, predefined and emergent. The confluence of reason and inspiration, emotion and intention, collaboration and competition—all that comprises the human and social milieu that is business. A fully holistic approach to the entire information, process and people resource of the enterprise. Not business intelligence. But Business unIntelligence—Insight and Innovation beyond Analytics and Big Data.
So, what does Business unIntelligence look like? Let’s start with the business/technological drivers and move to an architectural picture designed to support them.
Technological advances, particularly in mobile communications and computing, have moved business to an increasingly real-time, interactive model. Reporting needs are instantaneous and actions must anticipate customer needs rather than react to them. A symbiosis between business and technology is now evident—a biz-tech ecosystem that increasingly drives all successful business change. More recently, the terms business, operational, predictive (and more) analytics have come into vogue, with a strong emphasis on forecasting future trends and behaviour to enable faster or more appropriate action—a clear evolution of BI thinking from focusing on the past to envisaging the future. Big data has opened up previously unimaginable types and amounts of information, but has driven even more silos of un-integrated data than ever before. Multi-core parallel processors and ever-cheaper memory promise the merging of operational and informational systems (for example, in SAP HANA), allowing adaptive but highly integrated decision processes that span from operations to strategic planning. Meanwhile, the explosion of social networking tools is enabling collaborative working in business, and simultaneously exposing the human and social underpinnings of how business really works.
For many business people, the preceding paragraph makes for difficult reading. The central role of information technology in all future successful businesses demands a rather simple model around which business and IT people can engage and define the technology-centred business of tomorrow—and even of today. The IDEAL architecture, shown in Figure 1, offers that model. IDEAL is both a goal of the approach and an acronym: integrated, in information and process; distributed, organisationally and geographically; emergent, in the sense of a mathematically chaotic system; adaptive, to the changing needs of business and possibilities of technology; and latent, meaning hidden behind the actual implementation. At a high level, this is a deceptively simple picture. Three spaces of interest: information — all information — as the foundation, process as the intermediate functionality, and people as both the driver and receiver of all below. Read it as a simple vision statement: people process information. Moreover, each of these spaces is defined by three axes that describe its characteristics in terms that business and IT can both use to understand business needs and technical possibilities. For example, the timeliness/consistency axis of information allows coherent discussion about the trade-offs involved when business moves from a make and sell model to on-demand production. The relationships between the three layers and nine axes provide a fertile soil for the biz-tech ecosystem to seed and thrive.
For most traditional IT people, the information and process spaces are of most interest. Here we develop a REAL logical architecture that describes how the technological components operate and interrelate. Of particular relevance is a realignment of information within the business from layers in a single, relational technology, through which information passes sequentially, to pillars fed and processed in parallel in the most appropriate technology for the business needs. We also see that metadata — a long-standing issue for most BI projects and more recently a legal loophole for governmental and marketing intelligence projects — is clearly information, central to the understanding of all information. In fact, it is renamed context-setting information (CSI) in this approach to emphasise this fact. However, the scope of this lower level architecture is such that it cannot be covered adequately in this short article. A series of blog posts elsewhere provides more details.
The remainder of this article focuses instead on the people space, where most change is needed. This is, indeed, emerging as advances in psychology and neurobiology are reinventing our understanding of how and why people behave as they do and how they actually make decisions. As background, we note that BI has, since its inception, been predicated on the theory of rational choice decision making. In this model, much favoured by business schools and management consultants since at least the 1970s, decision makers are described as making choices between different alternatives based on defining objectives, making long lists of pros and cons, adding measures of utility, multiplying by probability, and coming to rational conclusions. In such circumstances, the more information one has to hand on any situation can only lead to better decisions. However, even as early as 1967, Russell Ackoff (later of DIKW pyramid fame) was declaring that managers suffer from an over-abundance of irrelevant information. Rational choice is still much loved by economists, at least according to Tim Harford in The Logic of Life (2008): “Economists are always looking for the hidden logic behind life, the way it is shaped by countless unseen rational decisions.” I suspect that many would disagree, from personal experience of post-2008 economics or in our roles supporting BI initiatives in businesses.
In contrast, German psychologist, Gerd Gigerenzer, proposes in Gut Feelings: The Intelligence of the Unconscious (2007) that the mind should be seen as an adaptive toolbox that has developed a wide range of rules of thumb to best deal with a highly uncertain environment, characterized by ill-defined problems and opportunities, with loose and changeable rules and variable definitions of success. This actual, real world in which we live is computationally intractable; any real decision of business interest cannot be solved conclusively with any conceivable amount of information and processing power. Decision making, using of this adaptive toolbox of heuristics, can often reach conclusions more quickly and directly than logic. Rationality remains, of course, valid, valuable and often necessary, but has been promoted to the exclusion of all other approaches since the Age of Enlightenment in the 17th century. A broader and more inclusive model, which we may call insight, includes intuition and the more classical intelligence. Human intuition — which, of necessity, ignores some available information may reach good conclusions based on the largest number of relevant factors than human intelligence, which may seek too much information and try to balance all possible outcomes. Furthermore, people often prefer to include a certain amount of ambiguity in their formulation of problems and solutions; they tend to bet on reciprocity in their dealings with others to co-create the best solutions to shared opportunities and challenges.
This leads us to modern findings in neuroscience, which clearly show that human mental processes span a number of levels, from primitive survival mechanisms to social interactions, which are all intimately involved in every aspect of our behaviour, including business relationships and decision making. When the human mind works well together, we are integrated and our relationships thrive. Our decision making is sound. We attune to others, allowing our own internal state to shift; we resonate and align with the inner world of another. This resonance is at the heart of empathy. Intuition gives us access to the wisdom of the body — that gut-feel of the “right decision” — coming from the interior of the body, our viscera, and our heartfelt sense of what to do. In Mindsight: The New Science of Personal Transformation (2010), neurobiologist and psychiatrist, Daniel Siegel, contends that “this integrative function illuminates how reasoning, once thought to be a ‘purely logical’ mode of thinking, is in fact dependent on the non-rational processing of our bodies. Such intuition helps us make wise decisions, not just logical ones”. Insight and empathy help us connect the past to the present and the anticipated future. This insightful decision making seamlessly combines left and right brain, and takes account of intuitive and emotional responses to reach an integrated and well-rounded position on the decision in hand.
In my experience, the above thinking often proves challenging for traditional BI and IT people, who question how it relates to information technology and how it can be applied in support of business decision making. However, applied it must be, if we are to move beyond the failed belief that ever more information necessarily leads to ever better decisions, a myth that is particularly prevalent in the big data scene. One way that we can move forward is through the adoption of collaborative and social tools that support teamwork and, through that, on-going innovation. Of particular interest here is informal information, the soft information gathered and exchanged (often unconsciously) between team members in the course of a project. Such information ranges from personal notes, to casual comments and even to bodily postures and facial expressions. These titbits of information are increasingly being (or capable of being) recorded, as our interactions become more and more electronically intermediated. Notes are made on tablets, conversations are recorded, interactions videoed. And, although it may smack of “Big Brother” and there are serious ethical considerations to take into account, this recorded information offers a wealth of insight into how the team works, who contributes, who is enthused and how and where ideas emerge or are stifled. Analysis of such interactions over the longer term can become a basis for encouraging and rewarding innovation, as well as detecting behaviours that run contrary to the aims or ethos of the organisation.
Business unIntelligence thus extends traditional BI in a variety of important ways. It recognises and positions the vaguely defined big data, so that it can be integrated into the information fabric of the organisation. It demarcates the processes that comprise all decision making at an organizational level. And it recognises and includes the true human characteristics — emotional, intuitive and social — that, in addition to rational thought, underpin all personal decision making in life and in business.
Finally, Business unIntelligence brings into clear focus the moral and ethical dilemmas raised by our collection of enormous volumes of data and widespread use of powerful analytics. To what end will we choose to use this technology? The good of humanity or the advancement of the few? To solve the burgeoning problems that beset this overcrowded, tiny blue planet or to drive a new wave of lust for consumption that will surely overwhelm us all?
Dr. Barry Devlin is among the foremost authorities on business insight and one of the founders of data warehousing, having published the first architectural paper on the topic in 1988. With over 30 years of IT experience, including 20 years with IBM as a Distinguished Engineer, he is a widely respected analyst, consultant, lecturer and author of the seminal book, “Data Warehouse—from Architecture to Implementation” and numerous White Papers. His 2013 book, “Business unIntelligence—Insight and Innovation beyond Analytics and Big Data” is available from Technics Publications.
Founder and Principal
Barry is founder and principal of 9sight Consulting. He specializes in the human, organizational and IT implications of deep business insight solutions that combine operational, informational and collaborative environments. A regular contributor to BeyeNETWORK, SmartDataCollective and TDWI, Barry is based in Cape Town, South Africa and operates worldwide.