Using Advanced Analytics to Drive Better Business Decisions

Information Management Magazine, April 2005

Peter Arena, Stephen Rhody, Michael Stavrianos

The business intelligence (BI) market is moving toward widespread adoption of advanced analytics to drive better decision management. Increasingly, BI practitioners and consumers are recognizing that advanced analytics are essential to the delivery of high-value, decision-oriented functionality (e.g., forecasting, prediction, simulation and optimization). Meanwhile, early disciples are discovering that they can build a sustainable competitive advantage through adaptive and embedded analytics.

The arrival of this market trend is heralded by incisive headlines across the major trade journals and reinforced by industry analysts whose optimistic forecasts suggest that advanced analytics will be a driving force in the BI market for some time to come:

• "Advanced analytics tools ... will become more relevant in an organization's daily operations as the opportunity to embed such tools into analytic applications increases." (IDC, 2003)
• "The market for packaged analytics appears to have a bright future. ... About one-third [of survey respondents] plan to add more sophisticated statistical analysis and predictive modeling capabilities in the next 18 months." (TDWI, 2002)
• "Worldwide services for the analytics market are projected to grow from $16.8B in 2001 to $29B in 2006, an 11.6 percent CAGR." (Gartner, 2001)

This article looks beyond the sound bites to examine the real value of advanced analytics, as well as the unique challenges of integrating decision management into the BI architecture. We evaluate three competing approaches that vendors are using to meet the growing demand for analytic applications, and we offer advice to decision-makers who are exploring this investment. Finally, we articulate five strategic imperatives for successful implementation of advanced analytic applications.

Value Proposition

The driving force behind increased adoption of advanced analytics is a familiar one. Organizations that have embraced each progressive phase of data management modernization (enterprise resource planning, customer relationship management, data warehousing, business intelligence) are still looking to capitalize on their past investments. As they reach maturity in each discipline, they are rewarded only with promises about the watershed ROI offered by the next phase (in this case, decision management). Jaded readers can't be blamed for thinking they've heard this story before; but this time, it may actually be true.

Why now? Because previous phases of the modernization process have focused on tasks that are inherently foundational to better enterprise performance - data capture, data architecture and information delivery. Granted, the industry's emphasis on online analytical processing (OLAP) and other reporting disciplines as the cornerstone of BI has hastened the democratization of information assets, but it has done little to empower information consumers. Faced with a downtick in a critical business metric, decision-makers still must rely heavily on intuition and instinct in choosing how to react.

The promise of embedded analytics is to do for decision making what OLAP has done for information -- that is, to democratize decision-making assets. Believers describe it in terms of a knowledge management initiative, in which the organization's best practices for each decision-making process are pushed to the desktops of end users as embedded logic within analytic applications. These applications are typically powered either by business rules engines (which apply logical conditions to determine how a certain case should be handled) or predictive models (which probabilistically identify the action most likely to achieve the desired results).

Democratization and automation of decision making has the potential to unlock numerous benefits. At a strategic level, it helps executives make more informed decisions on fundamental business questions, such as:

• Who will be our most profitable customers tomorrow?
• Do recent purchasing patterns represent the start of a long-term trend, cyclical behavior or just a short-term aberration?
• How would a price change influence the behavior of various customer segments?
• What will be the impact on profits of introducing a new product line?
• Where should we focus our expansion efforts?

Reporting tools alone are unable to answer these questions. Consider, for example, an executive dashboard report that shows an overall customer attrition rate of 15 percent. Even if the executive is able to drill down by quarter, region or customer demographic, more fundamental questions remain. Why is the attrition rate 15 percent? What is causing attrition for key customer segments? What actions can be taken to reduce attrition?

Business intelligence tools that merely report historical data are ill equipped to guide prospective decisions. Armed with the right tools, however, leaders can forecast and evaluate the impact of their decisions before executing them. This capability is particularly important when facing multiple (often competing) strategic objectives - and when complex or interrelated business processes create the risk of unintended consequences to a decision.

At an operational level, decision-making assets can eliminate the need for human involvement in decisions that are either highly routinized (such as reordering inventory), or so numerous that they prohibit case-by-case oversight (such as identifying fraudulent financial transactions). They can also enable instantaneous decision making on fleeting business opportunities, such as informed customization of up-sell or cross-sell offers during a customer-initiated contact.

Whether aimed at strategic or operational issues, advanced analytic applications can dramatically improve the consistency and quality of decision making throughout an organization -- enabling decisions that are based on quantified costs, benefits and risks, rather than pure instinct. At the same time, they accelerate the decision-making process, allowing managers to focus their time and attention on more pressing issues.

The bottom line, of course, is the bottom line - and the evidence to date suggests that decision-oriented analytic applications offer a superior return on investment compared to their non-decision-oriented counterparts. A recent study conducted by IDC, based on case studies of 40 analytic projects, found that projects involving predictive analytics offered a median ROI of 145 percent, compared with 89 percent for their non-predictive counterparts. IDC attributed the superior ROI of predictive analytics to enhanced operational processes, enabled through improved decision making. Undoubtedly, a related factor in the value of predictive analytics is that it is typically brought to bear on high-impact business processes where executives see the greatest opportunities for ROI.

Unique Challenges

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