Make Better Decision

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Make Better Decisions

by Thomas H. Davenport

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The Idea in Brief

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In many organizations, decisions are left up to individuals and the process for making them receives little if any scru-tiny. The recent plague of poor financial decisions is one result.

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Smart organizations can help their man-agers improve decision making in four steps: by identifying and prioritizing the decisions that must be made; examining the factors involved in each; designing roles, processes, systems, and behavior to improve decisions; and institutionalizing the new approach through training, re-fined data analysis, and outcome assess-ment.

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Chevron, the Educational Testing Service, and The Stanley Works are three organi-zations that have overhauled decision-making processes with great success. This article will help like-minded compa-nies give decisions the attention they de-serve.

This article is provided to you compliments of IBM.

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by Thomas H. Davenport

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In recent years decision makers in both thepublic and private sectors have made an as-tounding number of poor calls. For example,the decisions to invade Iraq, not to complywith global warming treaties, to ignore Dar-fur, are all likely to be recorded as injudiciousin history books. And how about the decisionsto invest in and securitize subprime mortgageloans, or to hedge risk with credit defaultswaps? Those were spread across a number ofcompanies, but single organizations, too,made bad decisions. Tenneco, once a largeconglomerate, chose poorly when buying busi-nesses and now consists of only one auto partsbusiness. General Motors made terrible deci-sions about which cars to bring to market.Time Warner erred in buying AOL, and Yahooin deciding not to sell itself to Microsoft.

Why this decision-making disorder? First, be-cause decisions have generally been viewed asthe prerogative of individuals—usually seniorexecutives. The process employed, the infor-mation used, the logic relied on, have been leftup to them, in something of a black box. Infor-

mation goes in, decisions come out—and whoknows what happens in between? Second, un-like other business processes, decision makinghas rarely been the focus of systematic analysisinside the firm. Very few organizations have“reengineered” their decisions. Yet there arejust as many opportunities to improve decisionmaking as to improve any other process.

Useful insights have been available for along time. For example, academics defined“groupthink,” the forced manufacture of con-sent, more than half a century ago—yet it stillbedevils decision makers from the WhiteHouse to company boardrooms. In the six-teenth century the Catholic Church estab-lished the devil’s advocate to criticize canoniza-tion decisions—yet few organizations todayformalize the advocacy of decision alterna-tives. Recent popular business books address ahost of decision-making alternatives (see “Se-lected Reading”).

However, although businesspeople are clearlybuying and reading these books, few companieshave actually adopted their recommendations.



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The consequences of this inattention are be-coming ever more severe. It is time to take deci-sion making out of the realm of the purely indi-vidual and idiosyncratic; organizations musthelp their managers employ better decision-making processes. Better processes won’t guar-antee better decisions, of course, but they canmake them more likely.

A Framework for Improving Decisions

Focusing on decisions doesn’t necessarily re-quire a strict focus on the mental processes ofmanagers. (Though, admittedly, the black boxdeserves some unpacking.) It can mean exam-ining the accessible components of decisionmaking—which decisions need to be made,what information is supplied, key roles in theprocess, and so forth. Smart organizationsmake multifaceted interventions—addressingtechnology, information, organizational struc-ture, methods, and personnel. They can im-prove decision making in four steps:

1. Identification.

Managers should begin bylisting the decisions that must be made anddeciding which are most important—for ex-ample, “the top 10 decisions required to exe-cute our strategy” or “the top 10 decisions thathave to go well if we are to meet our financialgoals.” Some decisions will be rare and highlystrategic (“What acquisitions will allow us togain the necessary market share?”) while oth-ers will be frequent and on the front lines(“How should we decide how much to pay onclaims?”). Without some prioritization, all de-cisions will be treated as equal—which proba-bly means that the important ones won’t beanalyzed with sufficient care.

2. Inventory.

In addition to identifying keydecisions, you should assess the factors that gointo each of them. Who plays what role in thedecision? How often does it occur? What infor-mation is available to support it? How well isthe decision typically made? Such an examina-tion helps an organization understand whichdecisions need improvement and what pro-cesses might make them more effective, whileestablishing a common language for discuss-ing decision making.

3. Intervention.

Having narrowed down yourlist of decisions and examined what’s involvedin making each, you can design the roles, pro-cesses, systems, and behaviors your organiza-tion should be using to make them. The key to

effective decision interventions is a broad, in-clusive approach that considers all methods ofimprovement and addresses all aspects of thedecision process—including execution of thedecision, which is often overlooked.

4. Institutionalization.

Organizations needto give managers the tools and assistance to“decide how to decide” on an ongoing basis. AtAir Products and Chemicals, for example,managers are trained to determine whether aparticular decision should be made unilater-ally by one manager, unilaterally after consul-tation with a group, by a group through a ma-jority vote, or by group consensus. In addition,they determine who will be responsible formaking the decision, who will be held ac-countable for results, and who needs to beconsulted or informed.

Companies that are serious about institu-tionalizing better decision making often enlistdecision experts to work with executives on im-proving the process. Chevron, for example, hasa decision-analysis group whose members facil-itate decision-framing workshops; coordinatedata gathering for analysis; build and refineeconomic and analytical models; help projectmanagers and decision makers interpret analy-ses; point out when additional informationand analysis would improve a decision; con-duct an assessment of decision quality; andcoach decision makers. The group has trainedmore than 2,500 decision makers in two-dayworkshops and has certified 10,000 through anonline training module. At Chevron all majorcapital projects (which are common at large oilcompanies) have the benefit of systematic deci-sion analysis.

An organization that has adopted these foursteps should also assess the quality of decisionsafter the fact. The assessment should address notonly actual business results—which can involveboth politics and luck—but also the decision-making process and whatever information themanager relied on. Chevron regularly performs“lookbacks” on major decisions, and assessesnot only outcomes but also how the decisionmight have employed a better process or ad-dressed uncertainty better.

Let’s look at how two companies have im-proved their decision making.

Better New-Product Decisions at ETS

The Educational Testing Service develops andadministers such widely recognized tests as

Thomas H. Davenport

is the Presi-dent’s Distinguished Professor of Infor-mation Technology and Management at Babson College in Wellesley, Massa-chusetts, and the author, coauthor, or editor of 13 books, including (with Jeanne G. Harris and Robert Morison) the forthcoming

Analytics at Work: Smarter Decisions, Better Results

(Harvard Business Press, 2010).



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the SAT, the GRE, the TOEFL, and the AP. In2007 Kurt Landgraf, ETS’s CEO, concludedthat the organization needed to accelerate andimprove decisions about new products andservices if it was to continue competing effec-tively. ETS had previously employed a stage-gate approval process for new offerings, butthe organization’s matrixed structure and dif-fuse decision-making responsibility made theprocess ineffective.

Landgraf asked T.J. Elliott, ETS’s vice presi-dent of strategic workforce solutions, andMarisa Farnum, the associate vice presidentfor technology transfer, to lead a team thatwould examine the decision process. The teamfound several fundamental problems. First, de-cision makers often lacked information aboutthe intellectual property, partners, cycle times,and likely market for new offerings. Second, itwas unclear who played what roles when a de-cision was being made. Third, the structure ofthe process was vague.

Elliott and Farnum’s team created a newprocess intended to lead to more evidence-based decisions. It introduced a centralizeddeliberative body to make decisions about

new offerings, developed forms that requirednew metrics for and information about eachproposal, and established standards for whatconstituted strong evidence that the offeringfit with ETS strategy and likely market de-mand. The process has been in operation for20 months and is widely regarded as a majorimprovement. It has clearly resulted in fewerbad product-launch decisions. However, thedeliberative body has realized that proposalsmust be nurtured from an earlier stage to cre-ate more

good

offerings. The scope of its gov-ernance was expanded recently to evaluateand prioritize all product-adaptation andnew-product opportunities.

Better Pricing Decisions at The Stanley Works

The Stanley Works, a maker of tools and otherproducts for construction, industry, and secu-rity, has been operating its Pricing Center ofExcellence since 2003. Under the banner ofthe Stanley Fulfillment System, a broad initia-tive for continual improvement in operations,Stanley had identified several decision do-mains that were critical to its success, includ-ing pricing, sales and operational planning,fulfillment processes, and lean manufactur-ing. Because all of them had a strong informa-tion component, a center of excellence wasformed for each. The pricing center bringsdeep knowledge of pricing, data and analysistools, and relationships with pricing experts atconsulting and software firms to Stanley’sbusiness units. It is staffed by a director, inter-nal consultants dedicated to the businessunits, and IT and data-mining specialists.

The center has made a variety of interven-tions in how the business units reach and exe-cute pricing decisions. Over time it has devel-oped several pricing methodologies and is nowfocusing on pricing optimization approaches.It has recommended assigning pricing respon-sibilities to the business unit managers. It holdsregular “gross margin calls” with the units toshare successes and review failures. (Stanley’sCEO, John Lundgren, and its COO, Jim Loree,frequently participate.) Pricing outcomes havebeen added to personnel evaluations and com-pensation reviews. An offshore supplier hasbeen engaged to gather and analyze competi-tors’ prices. The center has helped to developautomated decision making, such as a processfor authorizing promotional events. It uses

Selected Reading

Blink

by Malcolm Gladwell, is a paean to intuitive decision making.

The Wisdom of Crowds

by James Surowiecki, argues for large-group participation in decisions.

How We Decide

by Jonah Lehrer, addresses the psychobiology of decision making and the limits of rationality.

Predictably Irrational

by Dan Ariely, considers behavioral economics and its implications for decision making.

Nudge

by Richard Thaler and Cass Sunstein, is influenc-ing discussions about behavior-oriented policy in Washington, DC.

Two books on analytical and automated decision making:

Competing on Analytics

by Thomas H. Davenport and Jeanne G. Harris.

Super Crunchers

by Ian Ayres.



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“white space analysis” to analyze customersales data and identify opportunities for addi-tional sales or margin. It also trains the busi-ness units on pricing methods, participates inproject start-ups, does coaching and mentor-ing, and disseminates innovations and bestpractices in pricing.

The results of the center’s work speak forthemselves: Gross margin at Stanley grew from33.9% to more than 40% in six years. Thechanges have delivered more than $200 millionin incremental value to the firm. Bert Davis,Stanley’s head of business transformation andinformation systems, says, “We tried to improvepricing decisions with data and analysis toolsalone, but it didn’t work. It was only when weestablished the center that we began to see realimprovement in pricing decisions.”

Multiple Perspectives Yield Better Results

Analytics and decision automation are amongthe most powerful tools for improving deci-sion making. A growing number of firms areembracing the former both strategically andtactically, building competitive strategiesaround their analytical capabilities and mak-

ing decisions on the basis of data and analyt-ics. (See my article “Competing on Analytics,”HBR January 2006.) Analytics are even moreeffective when they have been embedded inautomated systems, which can make many de-cisions virtually in real time. (Few mortgagesor insurance policies in the United States aredrawn up without decision automation.)

But if one of these approaches goes awry, itcan do serious damage to your business. Ifyou’re making poor decisions on loans or in-surance policies with an automated system, forexample, you can lose money in a torrent—justask those bankers who issued so many low-quality subprime loans. Therefore, it’s criticalto balance and augment these decision toolswith human intuition and judgment. Organi-zations should:

• Warn managers not to build into theirbusinesses analytical models they don’t under-stand. This means, of course, that to be effec-tive, managers must increasingly be numeratewith analytics. As the Yale economist RobertShiller told the

McKinsey Quarterly

in April2009, “You have to be a quantitative person ifyou’re managing a company. The quantitativedetails really matter.”

Small-group processmaking effective decisions with just a few people

Analyticsusing data and quantitative analysis to support decision making

Automation using deci-sion rules and algorithms to automate decision processes

Benefi ts premature conver-gence on a deci-sion is unlikely

clear responsi-bilities can be assigned

multiple alter-natives can be examined

decisions are more likely to be correct

the scientifi c method adds rigor

speed and accuracy

criteria for deci-sions are clear

Cautionary messages

norms for debate must be rational, not emotional

everyone must get on board with the decision after debate

gathering enough data may be diffi cult and time-consuming

correct assump-tions are crucial

diffi cult to develop

decision criteria may change

The New Landscape of Decision MakingAncient approaches to decision mak-ing have recently been augmented by improvements in technology and new research. But every approach has both benefi ts and drawbacks.



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• Make assumptions clear. Every model hasassumptions behind it, such as “Housing priceswill continue to rise for the foreseeable future”or “Loan charge-off levels will remain similar tothose of the past 10 years.” (Both these assump-tions, of course, have recently been discred-ited.) Knowing what the assumptions aremakes it possible to anticipate when modelsare no longer a guide to effective decisions.

• Practice “model management,” whichkeeps track of the models being used within anorganization and monitors how well they areworking to analyze and predict selected vari-ables. Capital One, an early adopter, has manyanalytical models in place to support market-ing and operations.

• Cultivate human backups. Automated de-cision systems are often used to replace humandecision makers—but you lose those people atyour peril. It takes an expert human being torevise decision criteria over time or know whenan automated algorithm no longer works well.

It’s also important to know when a particu-lar decision approach doesn’t apply. For exam-ple, analytics isn’t a good fit in situations whenyou have to make a really fast decision. And al-most all quantitative models—even predictive

ones—are based on past data, so if your experi-ence or intuition tells you that the past is nolonger a good guide to the present and future,you’ll want to employ other decision tools, orat least to create some new data and analyses.(For a quick look at the strengths and weak-nesses of various approaches, see the exhibit“The New Landscape of Decision Making.”)

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Decisions, like any other business activity,won’t get better without systematic review. Ifyou don’t know which of your decisions aremost important, you won’t be able to priori-tize improvements. If you don’t know how de-cisions are made in your company, you can’tchange the process for making them. If youdon’t assess the results of your changes, you’reunlikely to achieve better decisions. The wayto begin is simply to give decisions the atten-tion they deserve. Without it, any success yourorganization achieves in decision making willbe largely a matter of luck.

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Neuroscience learning from brain research that illuminates decision making

decision makers know when to use the emotional brain

trains the rational brain to perform more effectively

individual decision making may be overvalued

the brain is still poorly understood

Behavioral economics incorporating research on eco-nomic behavior and thinking into decisions

Intuitionrelying on one’s gut and experience to make decisions

Wisdom of crowdsusing surveys or markets to allow decisions or inputs by large groups

illuminates biases and areas of irrationality

can nudge deci-sions in a particu-lar direction

easy and requires no data

the subconscious can be effective at weighing options

those close to the issue are well posi-tioned to know the truth

crowd-based deci-sions can be very accurate

fi ndings in the fi eld are still sketchy

context and word-ing can be used to manipulate decisions

typically the least accurate of deci-sion approaches

decision makers are easily swayed by context

members of the crowd must not infl uence one another

ongoing participa-tion is diffi cult to maintain


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