Management decision-making typically involves a three-step process of inform, analyze and act. In the earliest days of what came to be known as business intelligence, developers created decision support systems that provided information and analytics to help executives and high-level managers choose the best course of action. Working with numbers rather than gut instinct still is viewed as a best practice. After all, a pilot who doesn’t trust his or her instruments is heading for an accident.

Over the past couple of decades, crude, expensive corporate information systems have become more comprehensive and affordable as organizations collect broader sets of data from a wider range of functional organizations and processes and apply more sophisticated analyses to these data sets. Today, dashboards and scorecards are pervasive even in midsize and smaller companies. Yet alongside the explosion of data available to executives and managers and the tools to make sense of it, there is a stark reality: It’s not enough to know; action is required. This isn’t news to people who have to make those decisions, but most of the software demonstrations I see stop with the “inform” and the most basic part of the “analyze” stages of the three-step process. These sorts of vendor presentations assume that this is all decision-makers need to make consistently good decisions, but from my perspective this level of capabilities falls under the heading of “necessary but insufficient.”

More is needed and, happily, more is possible. Most organizations use analytics to make sense of the past. While this is useful, it also is not enough to be actionable. Addressing shortfalls and capitalizing on successes are a good first step, but these are essentially reactive approaches. Having a better understanding what might happen in the future and weighing the implications of potential actions is more valuable to a line manager or an executive looking to steal a march on the competition. Today, to address these shortcomings, more powerful business analytics are becoming accessible to generalists. New computing architecture (notably in-memory processing) is making it easier for companies to do more interactive and collaborative contingency analysis and planning to explore the implications of future actions.

For several years Ventana Research has been providing research on and stressing the importance of using predictive analytics. Predictive analytics helps enhance the accuracy of forecasts, often by detecting unseen drivers of results, giving companies greater precision in projecting future sales, expenses and operating results. Predictive analytics also provides a baseline set of expectations that can be used for early detection of departures from expected results. At the start of a holiday selling season, for instance, such departures could signal the need for early discounting or (if still feasible) allocating a product in high demand to the best customers. Predictive analytics also can be used to manage supply chains and to project cash flows more accurately.

Leading indicators are another tool that companies use less well than they could. Especially in the areas of demand analysis, supply chain risk and cost projection, such indicators can enable companies to anticipate future changes in markets and gain additional time to develop contingency plans or alter strategy.

Contingency planning and what-if analysis give people the ability to make better decisions more consistently and with greater agility. Thinking about pilots again, they use simulator training to help make the right decisions faster in the event of an emergency. This sort of planning can enable executives and managers to react sooner and with greater confidence when conditions change and even help determine how best to modify strategy or tactics in a volatile business climate. However, most midsize or larger companies have found it challenging to do contingency planning because technology limitations have made it impractical to do except at a very high level. This sort of planning is best done interactively in a collaborative setting. Until in-memory computing systems were applied to this process, response times have typically been too slow for many enterprise systems. If desktop spreadsheets are employed, it can take hours or days to recreate a scenario with a couple of changes in major assumptions.

It would be great if action-oriented decision support systems also provided the framework and capabilities for people to work collaboratively to follow through on decisions once they are made and to track the necessary follow-ups to see the process through to completion. Only with these capabilities could a system rightly be characterized as “closed loop.”

I see performance management, business intelligence and analytic applications becoming increasingly action-oriented over the next three years. Information technology can be of greater value to executives and managers in supporting their assessments and decision-making, and ensuring follow-through once a decision is made. But I question how long it will take before using the technology to do this becomes a standard feature of business. Some technology-inspired changes in behavior have happened rapidly over the past decade, but these have mainly been on the consumer side. While business computing has spurred changes in how organizations work, most businesses have been focused on efficiency by automating processes or eliminating the need for middle managers to coordinate activities. The kinds of changes to, for example, corporate planning that are now feasible require companies to set new expectations for planning and to alter how they plan. Although I believe the need for change is compelling, I fear adoption will take longer than it should.

Regards,

Robert D. Kugel – SVP Research