The Rise of Analytical Performance Management

The Rise of Analytical Performance Management

Tom DavenportCFO Preconference10 June 2009

Thomas H. Davenport – Analytical Performance Management

Information and Performance Management

► Perhaps the most successful area of information management► The first area attacked by IT

► Unit of performance—currency—is clear for many organizations

► CIO often reports to CFO

► Still problematic in many ways► Information not attended to or acted upon

► Still “multiple versions of the truth”

► Too difficult for many to use effectively

► Not sufficiently analytical

2 | 2009 © All Rights Reserved.


Paying Attention to Performance Information


Vol

ume

Time

Data, Information,Knowledge

Information ProcessingTechnology

AccessNetwork Bandwidth

Attention

► Attention: the most important resource in business

► A finite resource► A zero-sum game

► Attention is a two way street:

► Seekers of attention try to capture it

► Givers of attention try to allocate and preserve it


How Attention Works


Awareness Attention Action Decision Action

Meaning/Context

BEHAVIORDATA

Human Attention


Making Performance Information Attention-Getting


► Personalize information to the role, if not the individual

► Decide what information is really important to get across

► Know what information is really critical to the recipient’s strategy

► Embed information in compelling stories► Send out information (if you must) in

small chunks at regular frequencies► Show movement and trends► Measure information consumption

Thomas H. Davenport – Analytical Performance Management6 | 2009 © All Rights Reserved.

LinkingLinkingPerformance Information Performance Information and and DecisionsDecisions

6

InformationInformation DecisionsDecisions

Requires linkage to decision process &

behaviors• Daimler Truck

• GE Energy Finance• New York schools

Requires tight process/ system integration • Zurich Insurance

• JM Family

Structured HumanStructured Human


Requires information

infrastructure

• BlueCross BlueShield of Tennessee

• Wollongong• Miami schoolsLoosely-CoupledLoosely-Coupled

More decisions

AutomatedAutomatedInformationDecisions


Technologies Linking Technologies Linking Information and DecisionsInformation and Decisions

7

•Data warehouses

• Information Integration

•BI packages

•Specialized displays & dashboards

•Recommendations•Scorecards

• Scoring algorithms• Rules engines

• Workflow


Structured HumanStructured Human


Loosely-CoupledLoosely-CoupledMore decisions

AutomatedAutomatedInformationDecisions


LooselyLoosely--Coupled May Be Too Loose!Coupled May Be Too Loose!

► Requires lots of data sophistication and analytical skill (Aberdeen study—6 to 7%)

► How often do “ad hoc query” and “drill down” really happen?

► No way to assess value of information and tools if not tied to a specific decision

► “BI historically has been about dashboards and scorecards developed for specific uses. But that's changing. All of a sudden it's about integrated analytics within applications. The conversation is starting to shift to looking at information in the context of specific decisions and roles.” AMR Research analyst John Haggerty in InformationWeek, “Performance Management Links Strategy and Operations,” Nov. 22, 2009.


Ways to Make Decisions Ways to Make Decisions More StructuredMore Structured

9

Equations/ Scores

Rules

Forms/Displays/Scorecards

Customization by rolesProcesses/Lanes

Tests

Degree of Structure


The Organizational Context for Linking The Organizational Context for Linking Information and DecisionsInformation and Decisions

10

Decision mgmt. groupsBICC organizations

Information groups not in ITIT/business alignment on key issues

IT and business groups playing well together


Nirvana in Performance Data Management


► Major performance-related information entities are defined commonly across the enterprise

► There is one version of the performance truth

► Most data quality problems have been addressed

► Data is easily accessible in a warehouse or mart

► Data that need to be private and secure are private and secure

► There are some unique performance metrics


Two Key Business Management Roles for Data


Senior Management

Team

► Data governance:► Definitions► Commonality► Political models► Appointments

Senior Functional Managers

► Data stewardship► Key domains► Initial structures► Coalitions► Policing


Data Stewardship at the Bank of Montreal


► Business definitions and standards► Consistent interpretation of

information and ability to integrate► Information quality

► Accuracy, consistency, timeliness, validity, completeness of information

► Information protection► Appropriate controls to address

security and privacy requirements► Information lifecycle

► Treatment of information from creation or collection to retention or disposal

All at the strategic,

operational, and tactical

levels


Analytics in Performance Management

► Not very sophisticated thus far► Primarily standard reports, not analytics

► Scorecards considered state of the art

► Mostly financial performance indicators

► Consistent measures of non-financial metrics are a problem

► Some demand for non-financial and “intangibles” metrics from analysts and financial accounting bodies► Loyalty, brand, sustainability

► A few leading firms are beginning to explore more analytical territory



Performance Management Analytics Nirvana

► We’d have predictions of future corporate performance, not reports on the past

► We’d know why the items on our scorecards were there

► We’d be able to confirm our strategies

► We’d know how and where to intervene if performance began to suffer

► We’d simulate and optimize resources to implement strategy

► We’d report externally on “measures that matter” to performance



Analytics + Reporting = Analytical Performance Management

AnalyticsWhat’s the best that can happen?

What will happen next?

What if these trends continue?

Why is this happening?

What actions are needed?

Where exactly is the problem?

How many, how often, where?

What happened?Com

petit

ive

Adv

anta

ge

Reporting

Decision Optimization

Predictive Analytics

Forecasting

Statistical models

Alerts

Query/drill down

Ad hoc reports

Standard reports

AnalyticalPerformanceManagement



Analytical Performance Management Stories“For every 5% improvement in customer retention this year, we will grow revenue by 1.1% next year.”

“If we grow our share of customer gaming budgets by 1%, our share price increases by $1.10.”

“For every 10th of a point increase in employee engagement, we increase operating income by $100,000.”

“Raising the conversion rate by 1% brings more than $35 million in sales and more than $15 million in operating profit.”



What Do These Stories Have in Common?

► Bivariate relationships

► Intermediate or control variables not considered yet

► In service businesses

► Baby steps along the service profit chain

► Relate non-financial measures to financial

► Employ relatively new non-financial measures

► Require time-series or cross-sectional data



Pick Your Favorite Variable

More common

Less common

Innovation metricsSustainability metrics

Customer metricsEmployee metrics



Key Stages in Analytical Performance Management

Stage 5Incentives and Actions

Stage 4Analytical Model

Stage 3Strategy Map

Stage 2Balanced Scorecard

Stage 1Financial reports

Accurate, timelyfinancial reports

Scorecard with non-financial & financial measures

Logical relationships amongnon-financial & financial measures

Statistical relationships amongnon-financial and financial measures

Steps to align behavior with goals



Steps Toward Analytical Performance Management

Define Intangibles

Common Information

Strategy Map

Unit Comparison

Quant Scorecard

Run and Refine

Display and report variables that matter to performance

Create and refine a statistical (path) model

One version of key metrics and

information

Evaluate and compare

business units on key metrics

Hypothesis of logical relations among variables

Agree on metrics for non-financial

variables



The Toronto Dominion Story

► Strong focus on customer service post-merger

► Developed proprietary index and scorecard

► Factor analysis identifed four service factors

► Related branch service to branch profit

► Controlled for other branch-level variables

► Explained 19% of branch profitability variance

► Created new incentive plan based on service index

► Found that service increases only worked in the middle of the distribution



The Store24 Story

► Management had “strategy map” hypothesis about entertaining service experience

► Metrics of entertainment by store created► Also employee skill levels

► Analysis by academics found that enter-tainment was negatively related to store profit

► Controlling for population, competition, etc.

► When skill levels were high, entertainment did work as a strategy; when they weren’t it didn’t

► After two years management discontinued the strategy, but a year later than necessary



When It Works

► When you have a relatively local operational unit (store, branch, business, etc.) with clear financial and non-financial metrics

► When you’re comparing like operations using cross-sectional data

► When there is a clear hypothesis about what drives your business results (e.g., the service/profit chain in retail, or uptime in a refinery)

► When there is a senior executive to lead the charge

► When there is a clear relation to action!


What Doesn’t Work

► One big model with all possible explanatory variables

► Aggregation to the level of the large, complex enterprise

► Gathering long time series (because businesses and metrics change)

► Total reliance on somebody else’s data

► Analytics before metrics, e.g., sustainability


What We Still Need

Data . . . . . . . . consensus on nonfinancial metricsEnterprise . . . . . . . . defining performance consistentlyLeadership . . . . . . . . . . . . …a clear leader/owner for EPMTargets . . . . . . . . . . . clarity on what drives the businessAnalysts . . . .analytical people in performance mgmt functions

Documents

The Rise of Analytical Performance Management