Discovery: A critical first step in decision management

In today’s fast-changing marketplace, effectively managing business decisions can provide significant competitive advantage. But experts say that if organizations aren’t clear about their existing processes and goals, even the best decision management technologies will be ineffective.

“The most successful projects have the best handle on what the decision making is, where it sits in the rest of the business, what a good [decision] looks like, and how to go about making the decision,” says James Taylor, CEO of Decision Management Solutions, a Palo Alto, Calif., consulting firm.

Businesses can obtain all that information during the discovery phase of decision management. This crucial first step involves gathering and analyzing business data, identifying the most important decisions and determining which ones will benefit from decision management.

Both IT and business play a role in this effort. Done well, the approach will lead to more efficient decisions that are easier to manage and better aligned with business objectives.

While it may sound like a straightforward process, gathering business information can create major challenges for discovery efforts. That’s because the people concerned with decision management are typically business-side employees who often lack access to data.

The solution: better business-IT collaboration, says Mike Gualtieri a principal analyst at Forrester Research in Cambridge, Mass. “In a way, you’re discovering data,” he explains. “So IT needs a data management architecture and tools that provide information to the people who need to make decisions.”

IT should be involved in other ways. For example, experts in analytics can glean decision-making patterns from the ever-growing heaps of structured and unstructured information, or “big data,” hidden under an organization’s hood. Data mining, predictive analytics and advanced visualization tools are popular for this type of work.

“The bottom line of what you’re doing is building a decision model of some kind,” says W. Roy Schulte, an analyst at Gartner Inc. in Stamford, Conn.

What that means can vary widely. “In some cases, you can codify the rules and policies just by interviewing people,” Schulte continues. “In other cases, you can spend months doing predictive analytics and data discovery.”

Taylor notes that many successful discovery initiatives employ the same technique: decision dependency modeling. The approach involves starting by discovering the major components of a decision, then working backward to identify the smaller factors on which the decision depends. That means businesses can dig down into a specific set of processes and rules to capture the data, analyses, predictions and policies needed for effective decision making.

More traditional means of discovering decisions remain important, too—and they don’t always involve technology. Experts say that business analysts often simply need to talk to employees throughout the enterprise to identify the most common and crucial everyday decisions.

While managers, employees and subject matter experts can provide valuable insight into the decisions they make—and how to make them well—each is likely to have a myopic perspective on decisions. One way to provide a more holistic, enterprise-wide view: “Start with the first interaction that the firm has with the customer and then trace everything that has to happen to deliver that customer the product or service,” Gualtieri recommends.

That approach helps people understand how each human or automated decision made along the way affects the overall customer experience—which is often central to the most important business goals.

Still, not all key decisions are good fits for decision management. “One of the important questions during discovery is whether a tool is going to help you make better decisions,” Schulte says. “Many decisions are not amenable to tools.”

For example, some decisions are made differently each time, making it difficult to apply a standard management scheme. Other decisions are so dependent upon human decision making that automation may limit the ability to make them well.

To find decisions that do fit the decision management bill, Taylor suggested looking for two factors: repeatability and complexity.

“The best decisions are the ones that repeat the most, because any benefit you get is multiplied by the number of times you make the decision,” he says. “And it’s got to be something that has complexity to it. If it’s too trivial, it’s not worth applying a new technique to it.”

Taylor says another factor is being able to link the decision itself to its business-performance measures and objectives. Without that connection, businesses will be hard-pressed to justify a software investment to manage the decision.

Major strategic decisions, such as investing in new products, may be good candidates as well, Gualtieri says. “When people think about decision management, they don’t normally think about that, and they should,” he says.

He also advised organizations to regularly revisit the discovery phase once decision management has been set in motion. “To have the most optimized decisions, the discovery process has to be continuous, because variables change and environments change,” he says.

READER FEEDBACK: Has your company completed the discovery phase of decision management? If so, ebizQ editors would like to hear about your experience. Contact Site Editor Anne Stuart at

About the Author

Stephanie Mann is the former assistant editor for ebizQ and its sister TechTarget site, SearchSOA. Before joining TechTarget, Stephanie was a contributing reporter and proofreader for a Boston-area weekly newspaper and an editorial intern at a Cambridge, Mass.-based publishing company. She has also worked for several nonprofits and as a freelance editor.

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