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Davenport Webinar Predictive Analytics
- 1. Learn More About Predictive Analytics and SAP
Additional information
SAP.com/PredictiveAnalytics
Or email us @ PredictiveAnalytics@sap.com
Online community & discussion board
“SAP Predictive Analytics”
Upcoming webinars
Run Better with Predict Analytics & In-Memory Technology
Dr. David Ginsberg – December 6th, 2011
SAP Webcast Series: Unwire Your Enterprise
Discuss the importance and growth of mobile technology and to explore how your organization can leverage powerful
mobility solutions to capitalize on the immense rewards offered by developing and executing against a comprehensive
mobility strategy.
© 2011 SAP AG. All rights reserved. 1
- 2. Predictive Analytics: Gaining Advantage
by Using Analytics to Predict the Future
Tom Davenport
President’s Distinguished Professor of Management
and Information Technology
Babson College
October 3, 2011 Brought to you by
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- 3. Predictive
Analytics at Work
Tom Davenport
Babson College
Harvard Business Review Webcast
October 3, 2011
What Are Analytics?
Optimization “What’s the best that can happen?”
Predictive Modeling/ “What will happen next?” Predictive and
Forecasting
Prescriptive
Randomized Testing “What happens if we try this?”
Analytics
Degree Statistical analysis “Why is this happening?”
(the “so what”)
of Intelligence
Alerts “What actions are needed?”
Query/drill down “What exactly is the problem?” Descriptive
Analytics
Ad hoc reports “How many, how often, where?”
(the “what”)
Standard Reports “What happened?”
4 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 4. Types of Analytics
Timeframe
Past Present Future
Information
What is happening
What happened? What will happen?
now?
(Reporting) (Prediction)
Content Type
(Alerts)
What’s the best
How and why did It What’s the next best
that can happen?
Insight
happen? action?
(Optimization/simulatio
(Modeling, testing) (Recommendation)
n)
5 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
Applications of Predictive
Analytics
What offers will customers
accept?
What price will they pay?
Which recruit will become a
high performer?
How likely is it that this
customer will leave?
Which supplier is most likely to
fail to deliver?
6 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 5. Levels of Analytical Capability
Stage 5
Analytical
Competitors
Stage 4
Analytical Companies
Stage 3
Analytical Aspirations
Stage 2
Localized Analytics
Stage 1
Analytically Impaired
Thomas H. Davenport – Predictive Analytics
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Masters of Prediction
Marriott — optimal pricing
Nextel—customer attrition
Cisco—forecasting
Tesco—offers
eBay—web site testing
Netflix—movies you’ll like
Zappos—shoes you’ll like
Google—page rank, advertising, HR
8 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 6. The Analytical DELTA
Data . . . . . . . . . . breadth, integration, quality, technology
Enterprise . . . . . . . . . .approach to managing analytics
Leadership . . . . . . . . . . . . . . . passion and commitment
Targets . . . . . . . . . . . . . first deep, then broad
Analysts . . . . . professionals and amateurs
9 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
Data
The prerequisite for everything analytical
Clean, common, integrated
Accessible in a warehouse
Measuring something new and important
10 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 7. New Metrics / Data
Wine Chemistry Defensive moves Smile Frequency
11 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
Some Current Data and
Technology Dilemmas
Analytics on premise, private cloud, public cloud?
Different tools for “big data”?
Is a data warehouse still necessary?
Will “analytical apps” take off?
How can analytics be embedded?
12 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 8. The Changing World of Analytics
Analyst
Multi- Old BI Sandbox
Purpose
Application Breadth
Single- Analytical Embedded
Purpose Apps Analytics
Business Professional
Users Analysts
Primary Users
13 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
Some Actual Analytical Apps
Spend analysis in life sciences
Aftermarket services revenue growth for
equipment manufacturers
Analyzing mortgage portfolios
Financial planning and modeling in the public
sector
Enterprise risk and solvency management for
insurance
Contract compliance in transportation
Nursing productivity in health care
Field sales hiring analysis in pharma
Employee attrition analysis in telecom
Employee satisfaction and store performance
analysis in retail
14 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 9. Linking Data and Decisions
Thomas H. Davenport – Predictive Analytics
Embedding Analytics in Processes
Defection Risk
Creation
Purchase Order “What is the customer status?”
Creation
Request Global ATP Inventory Forecast
Sales Order “Will this be back in inventory?”
Global ATP Check
Fulfillment Request
Creation &
Release Delivery
Request
Returns per Customer
“What is the customer history?” CLTV
“Does this order justify extra
Delivery
Execution efforts?”
Update
Update
Releases ASN Inventory
Inventory
Accounting
Delivery Performance
Receives ASN “How effective is our fulfillment
process?”
Source: SAP AG 2006
16 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 10. Enterprise
If you’re competing on analytics, it doesn’t make
sense to manage them locally
No fiefdoms of data, technology, or organization
A centralized organization or CoE is increasingly
common
P&G, Caesars, Walmart, etc.
17 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
Under Enterprise Management
=
Predictive + +
HR analytics Actuarial + Enterprise
Analytics!
Web analytics + Marketing + Supply chain/OR
Thomas H. Davenport – Predictive Analytics
9
Copyright © 2011, SAS Institute Inc. All rights reserved.
- 11. Leadership
CEOs—Google, Netflix, Capital One
CFOs—Caesars, Humana
CIOs—P&G, Schneider
COOs—Ebay, Chicos
19 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
The Best Targets…
Support a key strategic capability
Engage top management commitment
Create momentum for analytics across the
enterprise
Have ambitious, yet pragmatic scope
Are data rich — or have the potential to be
Dramatically improve effectiveness of asset and/or
labor-intensive activities
Have broad implications across functions,
processes, geographies, or business units
20 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 12. Are You Ready for Prediction/Optimization?
Real-Time Optimization
Optimal
response
embedded in
real-time
process Institutional Action
Prediction and
differentiated
action
Predictive Action embedded in
process
Predictions of
response by
target/ segment
Differentiated Action
Different
approaches for
different
targets/
Key Targets/Segments segments
Key targets and
segments
defined
Data in Order
Well-defined,
common, clean,
and integrated
data
21 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
Analysts
Analytical Champions--Own
1%
Lead analytical initiatives
“Data Scientists”—Own/Rent
5-10% Can create new algorithms
Analytical Semi-Professionals—Own/Rent
15-20% Can use visual and basic statistical tools,
create simple models
Analytical Amateurs--Own
Can use spreadsheets, use
70-80% analytical transactions
* percentages will vary based upon industry and strategy
22 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
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Copyright © 2011, SAS Institute Inc. All rights reserved.
- 13. Roles for IT and CIOs in All This
Restructure the entire IT organization to emphasize
decision-making
e.g., P&G’s “Information and Decision Solutions”
Establish a COE, competency center, or consulting
group around analysis and decisions
e.g, Kimberly-Clark’s BICC
Include analytics and decision processes in the
broader information provision process
E.g., Cisco Advanced Services “Production Analytics”
Thomas H. Davenport – Predictive Analytics
Keep in Mind
► Five levels, five factors for building
predictive analytical capability
► Data and leadership are the most
important prerequisites
► Make sure your targets are strategic
► Tie all your predictive analytics work to
specific decisions
► This is not business as usual—there is a
historic opportunity to transform your
industry!
24 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics
12
Copyright © 2011, SAS Institute Inc. All rights reserved.
- 14. Questions?
To ask a question … click on the “question icon” in
the upper-left corner of your screen.
Type your question and name, and additional
information if you wish, and click on the send
button.
Brought to you by
Thank you for participating
This presentation was made possible by the
generous support of SAP.
Learn more at SAP.com/PredictiveAnalytics
Brought to you by
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Copyright © 2011, SAS Institute Inc. All rights reserved.