introduction to predictive analytics part i
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Using Predictive Analytics to Increase Profitability - Part ITRANSCRIPT
Introduction to Predictive Analytics – Part I
Jay RoyChief Strategy Officer
May 2011 | Dallas, TX
© 2011 Predictive Dashboards LLC
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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
Definition of Analytics & Predictive Analytics
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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 Davenportnoted author
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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.”
How Analytics & Predictive Analytics Compare
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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
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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
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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
Defining Business Intelligence “BI” & its Relationship to Predictive Analytics
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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
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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
+ + +
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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
+ +=
Business Intelligence’s Evolution & its Organizational Impact
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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
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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”
The Importance of Communication Skills & Predictive Analytics
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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
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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
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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
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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
The Business Case for Predictive Analytics
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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.
© 2011 Predictive Dashboards LLC
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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
Conclusion & Key Takeaways
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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
© 2011 Predictive Dashboards LLC
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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
© 2011 Predictive Dashboards LLC
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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
Introduction to Predictive Analytics – Part I
Jay Roy, Chief Strategy Officer
www.predictivedashboards.com
T:214-621-7612