analytics at work - how to make better decision and get better results

8/3/2019 Analytics at Work - how to make better decision and get better results

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2010 Harvard Business School Publishing. Created for Harvard Business Review by BullsEye Resources www.bullseyeresources.compowered by

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hoW to mAke better decisionsAnd get better results

featuring t davp

February 2, 2010

key learning summary

in collaboration with


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HARVARD BUSINESS REVIEW WEBINARS

2010 Harvard Business School Publishing. Created for Harvard Business Review by BullsEye Resources www.bullseyeresources.com. 1www.hbr.org

Analytics at WorkHOW TO MAKE BETTER DECISIONS AND GET BETTER RESULTS

Tom Davenport, Presidents Chair in Information Technology and Management, Babson College

Angelia Herrin, Editor for Research and Special Projects, Harvard Business Review

OVERVIEW

Most organizations have massive amounts of data but fail to

use it in a meaningful way. But with the proper analytical

capabilities, culture, and business processes, organizations

can use analytics to achieve their desired resultmaking

better decisions.

Organizations can benefit from identifying their key strategic

and tactical decisions, assess how well they are doing at

making these decisions, determine which ones can be made

better, and then institutionalize more analytical decision-

making processes.

CONTEXT

Professor Davenport discussed the key concepts from his

latest book titledAnalytics at Work: Smart Decisions,

Better Results.

Dr. Davenports previous book, Competing on Analytics,

focused on companies using analytics to create

competitive advantage. In contrast,Analytics at Work is

designed to help any organization become more analytical

and fact-based. This book also emphasizes the important

linkage between analytics and decision making.

KEY LEARNINGS

There is much wrong with decision making.

In many organizations, there are huge investments in data

warehousing, ERP, and reporting, but this data isnt used to

make better decisions. Bad decision processes and outcomesabound. The body of knowledge on good decision making is

often ignored, and decisions often take too long. There is an

over-reliance on intuition and an under-reliance on data and

analytics. There is also little measurement of decision

processes and outcomes and little accountability for

decisions.

Both decision outcomes and processes areoften bad.

Tom Davenport

From the financial crisis to the decisions to invade Iraq and to

stay in Vietnam, there are numerous examples of bad

decision processes and outcomes in both the private and

public sector. And, even though massive amounts of dataexist, organizations continue to have bad decision processes

and make bad decisions. (While a good decision process

doesnt guarantee a good outcome and bad process doesnt

assure a bad outcome, there is a correlation.)

The good news: there are significant opportunities to improve

decision making. Analytics and algorithms can lead to better

decisions, as can the wisdom of crowds. Use of behavioral

economics can improve decision making and some decision

making can be automated. However, to date most

organizations havent taken advantage of the opportunities

that exist to improve their decision making.

A model exists for making better decisions.

The model shown below is designed to deliver better

decisions. It starts with a foundation of analytical capabilities,

creates an organizational context, delivers better decisions,

and then entails a systematic review to continuously improve

the decision process.


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Harvard Business ReviewWebinarsAnalytics at Work: How to Make Better Decisions and Get Better Results


Analytical Capability

Professor Davenport has identified organizations at five

stages in developing their analytical capabilities. This ranges

from the analytically impaired (Stage 1) to analytical

competitors (Stage 5).

The capabilities required for any organization to become

more analytical follow the D.E.L.T.A. model.

Data. This is the prerequisite for analytics. At a minimum,

data must be clean, common, integrated, and accessible in a

central data warehouse. Organizations can realize advantage

by having data that competitors dont. This entails

measurements that are new, distinctive/proprietary, and

important. For example, Marriott has proprietary metrics on

revenue optimization and Harrahs measures employee

smile frequency, which predicts customers experiences.

Collect data in areas that others havent

addressed and then apply this data analyticallyin decision making.

Tom Davenport

Enterprise. To become more analytical, organizations must

go beyond managing data locally or in silos. Successful

analytical competitors manage their data and analytics

program at an enterprise level. They create enterprise-wide

analytical capabilities and invest in enterprise-scale analytical

technologies.

Leadership. This is the most critical trait of analytical

companies, and remains extremely rare. Organizations that

become more analytical have leaders who fully embrace

analytics and lead the companys culture toward fact-based

decision making. Quotes of such leaders include, Do we

think or do we know? and In God we trust; all others bring

data.

Targets. With limited analytical resources, analytical

organizations pick a primary strategic target for their initial

analytical efforts (such as marketing or supply chain) as well

as a secondary target. Over time, the use of analytics and

analytical decision making will expand in an organization.

But long-term success starts with a specific strategic

application.

Analysts. An organization cant become more analytical

without analytical people. The types of analytical talent

required include: 1) Champions, who lead analytical

initiatives (perhaps 1% of the organization); 2) Professionals,

who can create new algorithms (5-10%); 3) Semi-

professionals, who can use visual and basic statistical tools(15-20%); and Amateurs, who use spreadsheets (70-80%).

Organizations need each of these types of analysts.

A table on Exhibit 1 fromAnalytics at Work shows how

organizations progress from Stage 1 to Stage 5 of each of the

success factors in the D.E.L.T.A. model.

Organizational Context

The context needed to become more analytical includes

creating an analytical culture and having analytical business

processes:

Analytical culture. An analytical culture is one where use of

facts, evidence, and analysis is the primary way of making

decisions. There is still room for intuition, but intuition

should be based on experience and expertise. In analytical

cultures, it is okay to push back by asking, Wheres your

data? When facts are lacking, organizations with analytical

cultures emphasize testing and learning, and they focus on

action after analysis.

Analytical processes. Becoming more analytical also entails

thinking very analytically about an organizations business

processes. This entails mapping out key processes, such as

the ordering processes, and understanding all possible steps

in the process. Analytics can be used to understand which

customers are most valuable and what actions might be taken

to improve the process. Today it is rare for organizations to

be highly analytical regarding their business processes.


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Desired Result

The goal for analytics is to make better decisions. However,

while many organizations collect data and some

organizations engage in analytics, few organizations link their

analytics activities to their decision making.

Better decisions are the goal of analytics.

Tom Davenport

In a study of decisions, Professor Davenport found that 90%

of companies made at least some effort to improve a specific

decision. The decisions that organizations looked to improve

tended to be those that were frequent and operational, such

as pricing decisions, targeting decisions, merchandising

decisions, and location decisions.

A system for improving decisions should include the

following steps:

Identify. This entails identifying the organizations key

strategic and tactical decisions, which rarely occurs in most

organizations.

Inventory. Determine how well the organizations key

decisions are being made today. What processes and tools are

being used? Are the key decisions being made intuitively or

analytically?

Intervene. Based on identifying the key decisions and

conducting an inventory of how they are being made,

determine if a decision intervention is requireddoes the

organization need to change how certain critical decisions are

being made? Are better people, processes, and tools required?

(Use of analytics is the most common intervention that

organizations make to improve their decision making).

Institutionalize. Allocate resources (people and technology)

and create processes to institutionalize how the

organizations key decisions are made.

Systematic Review

An important step in improving an organizations analytical

capabilities is to close the loop by engaging in a review of an

organizations key decisions. Many successful organizations

look back at all major decisions to assess the quality of the

decision process and the outcome. They look closely at any

errors that are made and seek to rectify these mistakes in

OTHER IMPORTANT POINTS

Building organizational support. Getting leadership support

for analytics and more analytical decision making can be a

challenge, yet leadership support is critical. Find a senior

executive who is analytical and enlist this person as a partner

in getting the analytical movement going.

The role of intuition. Intuition can play an important role in

generating the hypotheses that analysts investigate and in

deciding when analysis may not be appropriate.

Preventing slow decisions. Organizations can measure how

long decisions take and if they are taking too long, can

investigate to understand why, and can reengineer the

decision process.

Basing KPIs on analytics. In many organizations key

performance indicators (KPIs) are set arbitrarily. By using

analytics, KPIs can be established based on facts.

Analytical measures and incentives. Some organizations are

beginning to measure and reward managers for their decision

processes; not just for the outcomes of their decisions, which

can be delayed by many years.

Analytics for small businesses. Analytics is not just for large

enterprises; small organizations can also use analytics to

make more fact-based decisions. Technology is not the

barrier. Software as a service is increasingly making

analytical tools available to small businesses. The biggest

obstacle is analytical people. However, even such people canoften be rented.

New analytical organization. A new peer-based research

organization has been formedthe Inter-national Institute

for Analytics (IIA). IIA is a community of analytics

practitioners that will conduct research and gather

information on how real-world organizations are deploying

analytics. It is a way to advance the profession of analytics

and analytical decision making. All individual members who

join IIA in February will receive a complimentary

autographed copy of Analytics at Work. Go to

www.iianalytics.com to learn more or join.

Recommended reading. In addition to his own books,

Professor Davenport suggested reading How We Decide by

Jonah Lehrer and Nudge by Richard Thaler and Cass

Sunstein.


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EXHIBIT 1


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BIOGRAPHIES

Tom Davenport

Presidents Chair in Information Technology and Management, Babson College

Tom Davenport holds the President's Chair in Information Technology and Management at Babson College. His books and

articles on business process reengineering, knowledge management, attention management, knowledge worker productivity, and

analytical competition helped to establish each of those business ideas. Over many years he's authored or co-authored nine

books for Harvard Business Press, most recentlyCompeting on Analytics: The New Science of Winning (2007). His next book,

Analytics at Work: Smarter Decisions, Better Results, will be published in February 2010. Davenport has authored fifteen

articles forHarvard Business Review. His byline has also appeared for publications such asSloan Management Review,

California Management Review, Financial Times, Information Week, CIO, and many others.

Davenport has an extensive background in research and has led research centers at Ernst & Young, McKinsey & Company, CSC

Index, and the Accenture Institute of Strategic Change. Davenport holds a B.A. in sociology from Trinity University and M.A. and

Ph.D. in sociology from Harvard University.

Angelia Herrin

Executive Director of Project Development, Harvard Business Review

Angelia Herrin is executive director of business development at Harvard Business Review. At Harvard Business Review, Herrin

oversaw the re-launch of the management newsletter line and established the conference and virtual seminar division for

Harvard Business Review. More recently, she created a new series to deliver customized programs and products to organizations

and associations.

Prior to coming to Harvard Business Review, Herrin was the vice president for content at womenConnect.com, a Web site

focused on women business owners and executives.

Herrins journalism experience spans twenty years, primarily with Knight-Ridder newspapers and USA Today. At Knight-

Ridder, she covered Congress, as well as the 1988 presidential elections. At USA Today, she worked as Washington editor,heading the 1996 election coverage. She won the John S. Knight Fellowship in Professional Journalism at Stanford University in

198990.

The information contained in this summary reflects BullsEye Resources, Inc.s subjective condensed summarization of the applicable conference session. There may bematerial errors, omissions, or inaccuracies in the reporting of the substance of the session. In no way does BullsEye Resources or Harvard Business Review assume anyresponsibility for any information provided or any decisions made based upon the information provided in this document.

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