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Inductis White Paper

Diversity on the Road to Analytic Maturity
By Dr. Martin Ahrens, Vice President, Methodology & Quality Assurance, Inductis

INTRODUCTION
In recent years, the evolution of technology and simultaneous growth in data availability have enabled a revolution in information management and customer-focused decision-making. Decisions have become increasingly data-driven and the time frame for these decisions continues to accelerate. We refer to the extent of a company's deployment of processes that capitalize on these capabilities as Analytic Maturity (AM). Fundamentally, AM entails the compilation of inputs from all parts of the business, the full processing of all relevant linkages and the use of this new information to drive optimal business decisions. Although the appropriate components vary across industries, it is clear that companies that excel at Analytic Maturity are the most likely to succeed. So, the important question for any company should be how to get there, rather than whether it is desirable.

Surprisingly some business observers have suggested that this trend may be unimportant or unnecessary for many companies (see for example, Raden at http://www.hiredbrains.com/ Davenport_Rebuttal.htm, commenting on Davenport, www.tomdavenport.com). We believe that Analytic Maturity is compatible with both highly centralized, expert-driven solutions and decentralized distributed decision-making. Although AM impacts entire companies, it needs to be highly customized in order to be effective.

Who Should Own Business Intelligence?
Should it be quantitative experts who analyze patterns in the data to assist in big decisions and direct strategy OR is it at its best when used by a broad range of people and processes at the operational level, marginally improving performance, repeatedly and often?

I suggest that we need both the forest and the trees! Organizations and employees make many thousands of big and small decisions every day. Each of these needs to be supported by appropriately scaled BI. But the whole is clearly more than the sum of the parts: The provision of appropriate data, and execution of appropriate local decisions based on that data (e.g. Should John Doe's expense claim be approved, Is the manufacturing process for the last 1000 units within six sigma specifications, should prospect segment 5 be offered the 5.9% introductory APR) does not automatically ensure that bigger issues will be dealt with in an optimal analytically mature fashion (for example, optimal integration of direct marketing of introductory rates for new customers, promotional programs for existing customers and mass market communications). True Business Intelligence ensures that each individual and department within the organization is supported with the information they need to make the right decisions; and some decisions require both sophisticated models and appropriately trained experts.

Does Software make PhD's Obsolete?
This question could also be cast as the tradeoff between a centralized team of quantitative experts versus more democratic non-expert departmental decisionmaking based on userfriendly tools.

There has been great progress in analytic software over the last 15 years, including tools for business intelligence, campaign management, customer relationship management, statistical modeling and data visualization. The collective impact of this evolution has resulted in much greater efficiency in accomplishing tasks that could previously be done only manually and in "democratizing" many specialized functions. Today, non-experts can be trained to use these tools to accomplish tasks that previously required programmers or statisticians with advanced degrees. However, this is not a fool proof solution since these "software operators" are always vulnerable to the rule that "you don't know what you don't know" - in other words, there are data analytic challenges where expert judgment is still required and no software can be relied upon to make these judgments or even identify these situations. For this reason, the ideal solution is to combine a central (or external) expert group for the really hard problems with a broad dissemination of efficient tools to non-experts throughout the organization.

Centralize or Decentralize?
In other words, should data and expertise by centrally controlled OR do agile organizations require highly decentralized decision making with local BI tools?

Analytic Maturity often entails the existence of a centralized data warehouse or expert modeling group or the presence of a BI system that makes information linkages transparently and flexibly available throughout the organization. However, such systems and processes are not synonymous with centralized control of decision making. In fact their essential value lies in maintenance of data quality, accurate discovery of business process drivers through modeling, and provision of flexible access to data relationships and information through BI systems. When effectively implemented, this results in a high quality support system that enables accurate local (i.e. departmental) decisionmaking, to the benefit of both the department and the whole enterprise.

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