How organizations make better decisions

The following article is an edited excerpt of an article distributed by the International Institute for Analytics.

Relatively few businesses and organiza­tions have given full and proper attention to one of their most important activities: making decisions regarding key questions such as what strategies and business models to pursue, which products and services to offer, which customers to target, what prices to charge and what employees to hire. Organizations with poor decision processes and tools eventually encounter poor outcomes, and performance suffers.

However, new analytics, decision auto­mation tools and business intelligence systems make it possible to make better use of information in decisions. “Wisdom of crowds” approaches and technologies allow larger groups of people to partici­pate meaningfully in decision processes. Organizations cannot afford to ignore these new options if they wish to make the best possible decisions.

Given both negative and positive incen­tives to get better, one might expect that organizations would attempt to improve their decisions — that they would prioritize them, examine their current level of effectiveness, investigate new options for making them better and implement some of those options. In my survey and analysis of dozens of corporations, I found that while they are, indeed, doing some of these things, very few organizations have undertaken systematic efforts to improve a variety of decisions. In this excerpt I describe some of the more frequent approaches used to intervene in decision processes.

Analytics, testing and data
Infrastructures predicated on analytics and data were among the most common decision-making frameworks among the surveyed firms. Eighty-four percent of respondents mentioned an analytical component in their decision improvement efforts and 66 percent mentioned efforts to improve data. The range of analytical techniques employed was quite broad. Scoring approaches based on statistical analyses (usually some form of regression analy­sis) were common. Other approaches included optimization, behavior-based customer targeting, statistical forecasting, prediction of various phenomena and the use of text analytics.

Systematic testing was one form of analysis that was being used somewhat frequently by companies; 18 percent mentioned it specifically in interviews. One key virtue is that it creates a decision-oriented context from the start. If a test between two alternative Web page designs is performed, it is gen­erally assumed that a decision to adopt the winning page will be made. Other analytical approaches may not have as clear a path to a decision.

A prerequisite of virtually any form of analytics is high-quality data, so it is not surprising that data-oriented responses were also common. Sixty-six percent of respondents mentioned some issue involving data. The most common were:

• Having difficulty in accessing data.
• Creating a common data architecture.
• Eliminating duplicate data.
• Integrating “master data management.”
• Achieving “one version of the truth” in functional or process areas.
• Dealing with too much data.
• Gathering data from channel partners.
• Creating new metrics. 
  

Technology support – and overrides – for decisions 
Several firms surveyed mentioned spe­cific analytical software, testing software, data warehouses and Web analytics/reporting software. Two other tech­nologies were mentioned frequently: specialized information display technolo­gies and business rule engines.

Thirty-eight percent of companies in the study mentioned some use of specialized information displays such as scorecards and dashboards. These tools, typically found in the business intelligence category, allow decision makers to see only the information that they need to make a decision. Several firms mentioned using specific display approaches not generally supported by conventional BI tools, including the “A3” format for displaying key issues in a particular business domain. Some companies are using neuroscience principles to guide how information is presented and digested. This may be a bellwether of future attempts to link information and decision making.

Another popular decision technology involves using business rules to enable automated or semiautomated decision processes — sometimes in conjunction with analytics (e.g., scoring-oriented applications). Many organizations em­ploy business rules but allow humans to override the recommended decisions when appropriate.

Changes in business processes 
Not surprisingly, many organizations reported that they needed to change business processes to make better decisions. Forty-three percent men­tioned process changes of some type. For instance, some described process changes around supply chain manage­ment in an IT firm, lease processing in an auto financing firm, financial process­es in health insurance or new product development processes. Several organi­zations mentioned changes for decision-oriented processes made in the context of Six Sigma programs.

However, some decision-focused ana­lysts noted that their original goal wasn’t necessarily to identify and implement process changes, and that they had to work with other groups to accomplish them. As one head of an analyst group at an IT firm commented, “We didn’t initially have the franchise to do process improvement — our thing was analytics. But it kept coming up on our projects. So we eventually just made it a part of our standard approach.”

Decision-oriented methods and tools
Several organizations reported that one aspect of their decision processes was an overarching, strategic manage­ment approach to guide all aspects of their efforts. Most of these initiatives are well-known approaches to business and management.

• An insurance company adopted enterprise risk management.
• The Six Sigma approach to process quality and decision outcomes was implemented at a financial payments firm and a staffing firm.
• A financial services firm uses the “net promoter score” for customer satisfaction decisions.
• An economic decision analysis approach, popularized and taught by Stanford’s Engineering School and the Strategic Decisions Group, is used by an oil company.

In addition, three responding organiza­tions developed analytically focused decision processes that have been widely used in IT systems development, but are not widely known in the decision-making or analytics literature. Sometimes called “agile methods” or “rapid prototyping,” they involve the creation of a series of short-term deliverables, and frequent review of them by the client and stake­holders for the decision. The organi­zations that use this approach found that it led to results that better fit the decision-makers’ requirements, and at a faster pace.

Conclusion 
From my research, it’s clear that organizations recognize the importance of improving decisions. Although the survey was not a random sample, individuals in 90 percent of organiza­tions surveyed identified some attempt to improve decisions through better processes. Second, organiza­tions employ a variety of intervention types to improve decisions across analytics, culture and leadership, and data. The most successful organiza­tions adopted multiple interventions at once to improve a decision.

As a result, analysts — previously responsible for data gathering and analysis — are morphing into consultants who may be responsible for framing deci­sions, process redesign, communication and education programs, and change management — all in addition to the traditional analysis functions.

Organizations seeking to implement decision improvements should become familiar with these common intervention types and create ongoing capabilities to deliver them.

Author and researcher Tom Davenport is the President’s Distinguished Professor at Babson College. His newest book is Analytics at Work: Smarter Decisions, Better Results (with Jeanne Harris and Robert Morison, from Harvard Business Press).