Understanding the Transformative Power of Big Data & Predictive Analytics

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September 18, 2014

Understanding the

Transformative Power of Big

Data & Predictive Analytics

Dallas Fort Worth Area IMA

Meeting

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Today’s topics

 Demystify the buzz of ‘big data’ and analytics

 Discuss the relevance of data and analytics to the finance & accounting professional

 Explore value-creating use cases

 Discuss the “DO’s” and “DON’Ts”: final considerations for a data led strategy

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Understanding the buzz around data and analytics

 The practice is not new, what’s new is the combination of components: – Proliferation of data from new sources

• Sensors and tags; social media; loyalty programs and credit cards • Majority generated in last 2 years, doubling in size every 2 years – Reduction in storage expense

– Development of advanced analytical techniques (time and cost)  Data informed strategies are now a business necessity – transcends

public vs. private, small vs. large, B2B vs. B2C, regulated vs. deregulated

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Understanding the jargon

 Information that represents a source for ongoing discovery. Now classified based on size and other factors

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 Datasets that can be manipulated with traditional database software tools

big data  Requires specialized infrastructure to store and analyze. _{Differs from ‘small data’ in extent of volume, velocity and}

variety (structured vs. unstructured)

The value of data is not defined by being ‘big’ or ‘small’ – it is defined by it’s ability to inform business decisions

small data

data

ValueScope, Inc.

unstructured data

 Data that does not reside in a traditional column/row format. Growing faster than structured data

 Examples: text in documents and comments; audio files; videos

 Data created by online activities

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Overview of the types of analytics and their adoption

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Descriptive Predictive Prescriptive

Purpose • Summarizes what happened

• Forecasts what

might happen in the future using a variety of modeling and statistical techniques • Determines likely outcome & prescribes the optimal course of action; uses feedback to re-predict/re-prescribe Example • Profitability by client segment • Identifying

customers who are most likely to churn

• Identifying

intervention models to improve clinical care

Adoption • Common, but often underutilized

• Growing • Rare, burgeoning field

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Practical uses of data and analytics in business

Data and analytics are predominately used to do one or more of the following:

 Understand the customer (increase revenue)

 Optimize operations (reduce cost, reduce time, improve quality, etc.)  Identify and address risk, fraud and/or security

 Create a new asset – for internal benefit or for customers

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Understanding the payoff of data and analytics

5/13/2014 6 2007 2011 2012 2013 “… distinctive business processes

count among the last remaining points of differentiation.

Analytical competitors wring every last drop of value from business processes and key decisions.”

Competing on Analytics: The New Science of Winning. Thomas

Davenport & Jeanne Harris

“…companies that put data at the center of the

marketing and sales decisions improve their marketing return on investment by 15-20%.”

Big Data, Analytics and the Future of Marketing and Sales, Court, et. al., July 2013

“Big data: The next frontier for innovation, competition, and productivity”

McKinsey Global Institute, May 2011

Big Data: The Management Revolution

“…companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.”

Harvard Business Review, October 2012

The analytically elite are “2X more likely to be in the top quartile of financial performance” and “3X more likely to execute decisions as intended” Bain & Company, September 2013

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Today’s topics

 Discuss the relevance of data and analytics to the finance &

accounting professional

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Impact to the role

 Rising expectations for CFO and team

– Pure finance function vs. strategic partner (growth & improvement)

– Historical vs. forward looking  Rising expectations for information

– Exploration of value of many data sources – Higher level analyses

 Changing tools and practices

– SAP HANA: reducing the 10 day financial close – http://www.saphana.com/docs/DOC-1670

5/13/2014 ValueScope, Inc. 8

• 72M monthly users

• Stream 30B ads/month • Measure location, ad

revenue and gross margin by user

• Finance uses gross

margin to inform sales resource deployment

• Predicting users, songs

for 1-5 years for planning purposes

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Understanding why finance & accounting is so critical

 Originators/owners of “primary” data  Understand the scope of data available  Know veracity of data

 Understand stakeholders and agendas within the organization

 Often the source for descriptive analytics - comprehensive view of the business and key drivers

 Bring accuracy and clarity to data as well as the implications of analytics insights – “purveyor of truth”

 Relentless focus on ROI

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Scope of influence in data led strategy formation & execution

5/13/2014 ValueScope, Inc. 10 1. Descriptive analytics 2. Opportunity identification 3. Data vetting & gathering 4. Analysis & interpretation 5. Implementation STEPS 1&3

• Thought partner in identifying,

prioritizing, and understanding implications

• Lead role in descriptive

analytics and data gathering and vetting

STEPS 2 &4 STEP 5

• Owner of controls, reporting and tracking

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Implications for professional service providers

 Need an awareness and understanding of the changing expectations for your client

 Be a source for ideas, best practice sharing and analytical support

 Explore internal data and information that can be used to create value  Use data and analytics to transform your business model (case study

available)

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Today’s topics

 Explore value-creating use cases

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MARKETING: customer acquisition

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OPERATIONS: eliminating waste, addressing outages,

reducing peak load

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INFORMATION & KNOWLEDGE: organize and connect

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HUMAN CAPITAL: talent retention and aging workforce

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CUSTOMER SERVICE: knowing customer’s social influence

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SUPPLY CHAIN: optimizing distribution channels

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EQUIPMENT & DOWN TIME: identification and prevention

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Today’s topics

 Discuss the “DO’s” and “DON’Ts”: final considerations for a data led

strategy

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Data DO’s and DON’Ts

 Today, 70% of the work is in data collection and gathering. Plan with the end game in mind

 Be purposeful (intent vs. found data)

 Adoption and understanding begin by solving a small, discrete problem. From there, momentum and vision build

 Be real - there is no benefit in pursuing analytics for the sake of being or appearing analytical. Adoption requires cultural change and

acceptance, and will impact operations as well as how decisions are made

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Final thoughts

 Empowering better business decisions through the use of analytics is not a trend – it’s a wholesale change in management. Start now

 Companies that become skilled in using data to drive decisions will outpace their peers in market share and financial performance

 Becoming an analytical competitor is a journey, not a destination – Techniques and tools will continue to develop over time

– Business dynamics change, new data will become available

 The finance & accounting professional will play a key lead role in the process, as well as a valuable thought partner in the interpretation and application

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Contact information

Cyndy Carr, Ph.D.

[email protected]

LinkedIn: http://www.linkedin.com/in/cyndycarr/