Using Predictive Analytics to Increase Profitability - Part I

1. Introduction to Predictive Analytics – Part I
Jay Roy
Chief Strategy Officer
May 2011 | Dallas, TX

2. Table of Contents …
 Definition of Analytics and Predictive Analytics
 How Analytics and Predictive Analytics Compare
 Defining Business Intelligence “BI” and its Relationship to Predictive Analytics
Business Intelligence’s Evolution & its Organizational Impact
The Importance of Communication Skills & Predictive Analytics
The Business Case for Predictive Analytics
 Conclusion and Key Takeaways

3. Definition of Analytics & Predictive Analytics

4. What is Analytics?
Using analytics is like driving your car but watching traffic through the rear-
view mirror, not seeing what’s ahead and thereby in danger of crashing
“… the application of computer technology,
operations research and statistics to solve
problems in business and industry. Analytics is
carried out within an information system.”
“… the application of computer technology,
operations research and statistics to solve
problems in business and industry. Analytics is
carried out within an information system.”
Tom Davenport
noted author

5. What is Predictive Analytics?
Using predictive analytics is like driving your car and watching traffic through
the front windshield, anticipating traffic, making course corrections to avoid
traffic jams and getting there faster and safer
“predictive models exploit patterns found in historical
and transactional data to identify risks and
opportunities. Models capture relationships among
many factors to allow assessment of risk or potential
associated with a particular set of conditions, guiding
decision making for candidate transactions.”
“Any solution that supports the identification of
meaningful patterns and correlations among
variables in complex, structured and unstructured,
historical, and potential future data sets for the
purposes of predicting future events and assessing
the attractiveness of various courses of action.”

6. How Analytics & Predictive Analytics Compare

7. How Analytics and Predictive Analytics Compare
 Predictive Analytics are more sophisticated analytics that
“forward thinking” in nature
Analytics is the understanding of existing (retrospective)
data with the goal of understanding trends via comparison
 Developing analytics is the first step towards deriving
predictive analytics
 They used for gaining insights from mathematical and/or
financial modeling by enhancing understanding, interpretation
and judgment for the purpose of good decision making

8. How Analytics and Predictive Analytics Compare
Attribute Analytics Predictive Analytics
Purpose:
Understand the Past
Observe Trends
Catalyst for Discussion
Gain Insights
Make Decisions
Take Action
View: Historical and Current Future Oriented
Metrics Type: Lagging Indicators Leading Indicators
Data Used: Raw & Compiled Information
Data Type: Structured Structured and Unstructured
Users: Middle & Senior Mgt
Analysts, End Users
C-Level & Senior Mgt
Strategists, Analysts, Mgrs
Benefits: Gaining an understanding of data
Productivity Improvements
Gaining Information & Insights
Process Improvements

9. Benefits of Analytics and Predictive Analytics
 Benefits of analytics: productivity gains through improved
data-gathering processes results in less time required for
producing reports and metrics
 Takeaway: Both types of gains are beneficial but
improvements in analytics are NOT as scalable as to
the benefits in predictive analytics which are
repeatable, virtuous and scalable
 Benefits of predictive analytics: process improvement gains
through improve revenue generation & cost structures leading
to enhanced decision making

10. Defining Business Intelligence “BI” &
its Relationship to Predictive Analytics

11. Defining Business Intelligence & its Relationship to Predictive Analytics
Unfortunately, the human & business strategy elements are
often overlooked and forgotten but are key ingredients to the
success of BI
“… computer-based techniques used in
identifying, extracting and analyzing business
data … aims to support better business decision-
making … BI technologies provide historical,
current and predictive views of business
operations.”
 BI is typically thought of in terms of technology inclusive of data management
practices, data warehouses, ETL processes, etc.
 Predictive Analytics are a sub-set of Analytics and a branch of BI which is
the least understood and underestimated

12. Defining Business Intelligence & its Relationship to Predictive Analytics
Analytics serves as the “glue” in aligning the key elements of business
Analytics provide the feedback to business people signaling success or
failure of their strategy and business model
 Business Intelligence = Business + Intelligence
Business = The Strategy + Business Model + Infrastructure + Technology
+ + +

13. Defining Business Intelligence & its Relationship to Predictive Analytics
 People create information for the organization in order to gain understanding of its
customers, competitors and ecosystem
 Business Intelligence is a process of generating insights and or knowledge
(predictive analytics) through people and technologies in order to execute their
strategy
 This process needs to be leveraged into a core competency, a unique and virtuous
process to differentiate the business in a world of “me-too” organizations & strategies
Intelligence = People + Processes + Analytics
+ +=

14. Business Intelligence’s Evolution & its Organizational Impact

15. BI’s Evolution and its Organizational Impact
The most important part of BI is the
human element and achieving
people’s business and personal goals
 Most businesses organize their BI activities and professionals under the IT function
under the Enterprise 1.0 model
 With advances in technology and social media, the Enterprise 1.0 model, is not the
most efficient, scalable, and collaborative way to execute your business strategy
especially from a human resourcing perspective
 With globalization, advances in internet technologies and social media, we have
advanced to the era of Enterprise 2.5
 As a result of Enterprise 2.5, changes in business require evolution in BI

16. BI’s Evolution & its Organizational (Design) Impacts
 In the era of Enterprise 2.5, BI is readily
becoming a distinctive capability & asset
for organizations
 If BI is deemed strategic, this function
should be realigned to fall under the
direction of the CEO or Office of Strategy
Management (OSM)
 Implementing a new organizational
structure will encounter language and
communication challenges between
business and BI professionals
CEO
CIO
Business Intelligence Group
CEO
COO
CIO
Office of Strategy Management &
Business Intelligence Group
Old Model – “Enterprise 1.0”
New Model – “Enterprise 2.5”

17. The Importance of Communication Skills & Predictive Analytics

18. The Importance of Communication Skills & Predictive Analytics
 The purpose of predictive analytics is to help organizations see relationships
between business elements so senior management may craft targeted business
strategies and exploit opportunities on a timely basis with a focus on the future
 In order to benefit from predictive analytics, people across the organization must
communicate and understand with one another but language often becomes a barrier
 BI professionals often think language is SQL (Structured Query Language) and
business people often think language is reports, metrics and meetings
 IT & BI professionals need to understand the language of strategy, business
models and performance while solving business not technology problems
SQL vs

19. The Importance of Communication Skills & Predictive Analytics
Need market
segmentation report,
now!
OK, what are the
parameters and
how do you want
it rendered?
CEO/Business People BI People
Conversations @ Work

20. The Importance of Communication Skills & Predictive Analytics
Huh? What is he
asking me?
Just need my report!
CEO/Business People
Huh? What is he
asking me?
Market
Segmentation?
BI People
Conversations @ Work

21. The Importance of Communication Skills & Predictive Analytics
Takeaway: Business professionals need to appreciate the role of technology as an
enabler and they need to lead and determine where & how IT/BI infrastructure
should be deployed to improve decision making
 Takeaway: It is not enough to have state of the art in
BI technologies, without having a common
understanding and a common language between the
business people and BI professionals, otherwise BI
efforts will fall short of desired results
Takeaway: IT & BI professionals need to understand the language of
strategy, business models and performance while solving business NOT
technology problems

22. The Business Case for Predictive Analytics

23. The Business Case for Predictive Analytics – Macro level
 On a macro level, organizations need predictive analytics for:
 Strategic Planning
 Financial Planning
 Focusing on Priorities
 Competitive Analyses
 Achieving Profit and Revenue Targets
 Developing Competitive Advantages and Differentiation
 Predictive analytics can provide timely feedback to executives on their strategic
initiatives – without feedback course corrections may be too late
 Predictive analytics provide leading indicators and insight to assist in planning for
answering the big question: What should we do next? – next quarter, next year etc.

24. The Business Case for Predictive Analytics – Micro level
 On a micro level, organizations need predictive analytics for:
 Improving business processes
 Doing more with less budget (working smarter not harder!)
 Allocating resources appropriately
 Understanding correlations and sensitivities with customer segments
 To ensure long term financial resources are available to run the business
 Developing Competitive Advantages and Differentiation
 Q: Why do most organizations struggle with Analytics and especially Predictive
Analytics?
 A: Organizations fail to recognize and misunderstand the necessary and intangible
elements of people, skills, and corporate culture and tying these elements back to
their analytics, business model and strategies – Caution: this is a long-term fix

25. Conclusion & Key Takeaways

26. Conclusion & Key Takeaways
Takeaway: Predictive Analytics is the analytical ability to
see relationships between business drivers and performance
and the ability to model these relationships performed by
people to improve organizational visibility
 Conclusion: Business Intelligence begins with your
organization’s strategy and business model and only then
should performance metrics and analytics be appropriately
conceived and deployed
Takeaway: It is not enough to have state of the art in
BI technologies, without having a common
understanding and a common language between the
business people and BI professionals, otherwise BI
efforts will fall short of desired results

27. Conclusion & Key Takeaways
Takeaway: IT & BI professionals need to understand the
language of strategy, business models and performance
while solving business not technology problems
 Takeaway: IT & BI professionals need to understand the
language of strategy, business models and performance while
solving business not technology problems
Takeaway: Business professionals need to appreciate
the role of technology as an enabler and they need to
lead and determine where & how IT/BI infrastructure
should be deployed to improve decision making

28. Sources, References, and Trade Marks
 www.wikipedia.org
 Competing on Analytics, 2007, Thomas H. Davenport
 www.forrester.com
The Lego Minifigure is a trade mark of The Lego Group
Clipart provided by OCAL and www.clker.com

29. Introduction to Predictive Analytics – Part I
Jay Roy, Chief Strategy Officer
www.predictivedashboards.com
jay.roy@predictivedashboards.com
T:214-621-7612