Toppling the BI Pyramid

Information Management Magazine, January 2007

Neil Raden

By all accounts, the business intelligence (BI) industry is thriving. Numerous reliable surveys confirm that CIOs consistently rate BI a top priority in their plans. Attendance at industry events and vendor conferences is up 10 to 20 percent, and analytics has been featured in top business publications such as The Harvard Business Review thanks to thought leaders like Tom Davenport. The surge in interest is being fueled by the rapidly changing, technology-driven business landscape. Organizations are striving to get "smarter" by forming a deeper understanding of their extended enterprise. That requires intelligence - BI. After decades of being on the periphery of computing, BI is now right in the thick of it.

However, to capitalize on this opportunity, the BI industry must adapt too. Most BI users today are still consumers of information that has been gathered, manipulated and packaged by others. BI deployment practices, and even the BI tools themselves, stratify usage into arbitrary roles based entirely on unchallenged assumptions, not a foundational theory. While the often-stated goal of BI is to present the right information to the right people at the right time so they can make better decisions, the current practice cannot possibly meet those requirements. Decision-making is a more complicated process than just reviewing information. Though the subject of decision-making in organizations has been studied for decades, BI operates without any formal model of its nature or use, leading to arbitrary and ineffective practices and product design.

Is There a Theory of Business Intelligence?

For an industry that is focused on the goal of better decision-making, it is incongruous that so little attention is given to understanding the decision-making processes in organizations. BI seems to operate without any conceptual foundation, just some loose and vague promises about better decisions. There is an extensive academic foundational basis for the "plumbing" of BI, such as software engineering, database design and user interfaces, but the design, support and nurturing of BI environments is a green field. Ironically, the field of decision-making in organizations is rich with material, but connecting this body of knowledge specifically to BI has not been explored.

The late Herbert Simon wrote extensively about decision-making.¹ Simon sequenced the process into problem solving and then decision-making as separate parts of the same process. In BI, the universal assumption is that data leads an analyst to a decision, skipping the problem-solving step completely. Most BI tools and methodologies are designed with this model. But when it comes to the actual workflow of analytics, BI is typically used to inform part of the process not orchestrate it.

Simon's conclusions have direct bearing on how analytics should be orchestrated in an organization:

"It is work of choosing issues that require attention, setting goals, finding or designing suitable courses of action, and evaluating and choosing among alternative actions. The first three of these activities - fixing agendas, setting goals and designing actions - are usually called problem solving; the last, evaluating and choosing, is usually called decision-making."²

Most BI tools are not designed for the first three steps - choosing issues that require attention, setting goals and finding or designing suitable courses of action. All of these activities involve working both individually and in collaboration.

Simon adds: "The very first steps in the problem-solving process are the least understood. What brings (and should bring) problems to the head of the agenda? And when a problem is identified, how can it be represented in a way that facilitates its solution? The way in which problems are represented has much to do with the quality of the solutions that are found."

First, there is the issue of which problems (or opportunities) get attention. The next step is representing the problem in a way that others can understand it. BI is not arranged this way today. It may reach a wide audience, directly and indirectly, but its use is tiered. Understanding the dynamics of work is extremely difficult. As a result, simplified models are devised that are logical and compact and have an engineered quality. But people don't operate according to engineering concepts. Organizations are still trying shake off a century of scientific management, or Taylorism, which is an engineered sort of management initiated by Frederick Winslow Taylor in 1911, where authority is hierarchical and job descriptions are strict.

Sothic Analytics

In ancient Egypt, everything was timed around the annual flooding of the Nile. The priests were aware that a year could be measured precisely by the rising of the star Sirius, but because their calendar was 365 days long with no leap years, it would lose a quarter of a day each year. Thus, Sirius would only rise on the horizon at sundown once every 1,460 years, the Sothic Cycle. As the sole keepers of this fact, the rulers were able to maintain their power over the people with their "divinely inspired" predictions of the flood. Farmers, on the other hand, had to wait until they were underwater.

Society was rigidly stratified. At the top level were the royalty and high administrative officials. The middle class was the largest. In it were low-ranking bureaucrats, scribes, craftspeople, priests and farmers. Below that were slaves.

The parallels between Egypt 5,000 years ago and modern enterprise BI deployments are almost comically similar. User roles in BI are consistently depicted with the image of, what else - a pyramid! In many BI implementations, every user of the system is restricted to the data they are allowed to see. With respect to confidential information, privacy regulations or other mandated restrictions, this seems like a reasonable approach, but in most organizations, the "need to know" restrictions are the result of the pyramid, not logic. The eastern region sales manager is unable to see how the western region sales manager is doing with respect to a certain kind of sale and thus, deprived of potentially valuable insight. Or, some super users handling the duties of complex analysis pass it on to the lower levels as distilled pieces of work but without context such as the assumptions employed. Not only is this an incomplete preparation, it can have unintended effects: It is well known in the field of organizational decision theory that decision-makers often overreact to new information, contrary to Bayes' Law, when it is not presented with adequate context. Rather than streamlining the process, this approach can derail it.

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