Turning information into insight

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1 Turning information into insight Maureen Errity, Director, Center for Corporate Governance, Deloitte LLP John Lucker, Principal, Deloitte Consulting LLP NACD Power Breakfast October 15, 2013

2 Did you know?

3 Every summer Watch out for sharks! It is more likely you will be killed by slipping on a wet floor During a 342 year period from 1670 to 2013 there were 1,022 US shark attacks During the same 342 year period there were only 36 fatal US shark attacks Source: 2 Turning information into insight

4 Analytics help create a more fact based vs. gut based culture The problem is not that baseball professionals are stupid; it is that they are human. Like most people, including experts, they tend to rely on simple rules of thumb, on traditions, on habits, on what other experts seem to believe. Cass Sunstein & Richard Thaler review of Moneyball The most difficult subjects can be explained to the most slow-witted man if he has not formed any idea of them already; but the simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already, without a shadow of doubt, what is laid before him. Leo Tolstoy 3 Turning information into insight

5 Discussion items Define data analytics and BIG data Leading analytic trends The role of the Board: what does it mean to you? Practical examples Closing thoughts 4 Turning information into insight

6 Definitions Define data analytics Define BIG data The effective state

7 What is data analytics? [data] analytics is the process of obtaining an optimal or realistic decision based on existing data. (Wikipedia) [data analytics is]..the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Analytics leverage data in a particular functional process (or application) to enable context-specific insight that is actionable. (Gartner) (Competing on Analytics, Tom Davenport and Jeanne Harris) Data analytics is the use of raw data to produce insights or conclusions that can be acted upon 6 Turning information into insight

8 What is BIG data? Some people think of BIG data as anything that doesn t fit into a spreadsheet! In some simple contexts, BIG data is used to refer to any form of advanced statistical analytics or predictive modeling using diverse internal/external data sources. In many contexts, BIG data is shorthand for the granular and numerous data sources used for analytics projects big, small, old, new, structured, unstructured, etc. In more esoteric data contexts, some examples include geospatial, social/sentiment, audio/video, mobile information, telematics, telephonic, internet searching, web logs, etc. BIG data refers to internal and external data that is multi-structured, generated from diverse sources in real-time and in large volumes making it beyond the ability of traditional technology to capture, manage and process within a tolerable amount of elapsed time 7 Turning information into insight

9 Information Supply Information Demand BIG data, big noise but not enough focus on strategy and value Understanding Big Data as completely as possible and clearly identifying its links to various value levers is critical before designing any solutions Value, Strategy, Decisions, Analytics, Execution, Assets Value Drivers Improvement Levers Value-driven Strategies Value Chain Execution Decisions Insights, Analytics & Measureme nt Governance & Informati Managemen on t Assets Data Warehouse Data Sources 8 Turning information into insight

10 The effective state redefining success 9 Turning information into insight

11 Trends Leading analytic trends

12 Leading analytic trends There are several forces driving a renewed focus on business analytics 5 mega-trends are driving the current focus on Analytics and are already changing the competitive landscape in many sectors 1. Data Volumes & Technology Capacity Data volumes continue to grow exponentially and new computing capacity and analytical tools can meet the challenge. 2. Regulations Regulators are demanding deeper insight into risk, exposure, and public responsiveness from financial, health care, and many other sectors. 3. Profitable Growth The need to remain competitive compels investments in analytics to improve insight. 4. New Signals Holistic signal detection from traditional internal and external structured and unstructured (e.g. voice, , social networks, sensor enabled facilities, etc.) 5. Hidden Insight The growing complexity of global business has raised the stakes at all levels of decision-making. Federal Government Life Sciences Healthcare State Government Banking & Securities Insurance Performance Management Open Government Fraud and Risk Management Physician Targeting Managed Markets Safety Analytics Generic Drug Competition Healthcare Reform Fraud Quality of Care Improvement Compliance Performance Management Cost Reduction Fraud Management Workforce Planning Integrated Enterprise View of Risk & Finance Single View of the Customer Customer transformation Sales Force Effectiveness Distribution Strategy Underwriting and Claims Excellence 11 Turning information into insight

13 Today s competitive market: Realizing value from BIG data and analytics is an end-to-end process 1. Analytics Strategy 6. Performance Management 2. Analytics End-to-End Business Value 5. Org & Change Management 3. Bus. & Ops. Implementation & Integration 4. Technology Integration 12 Turning information into insight

14 The role of the board What does this mean to you?

15 Deloitte governance framework The Deloitte Governance Framework was developed to help boards and executive management assess the effectiveness of the organization s governance programs. It defines board and management activities that support effective governance. Each area of governance can be considered in the context of four attributes: Skills and knowledge Process Information Behavior As used in this document, Deloitte means Deloitte LLP and its subsidiaries. Please see for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. 14 Turning information into insight

16 The people dimension...the soft stuff is really the hard stuff. I used to believe that, in organizational change, issues such as human resources were fluffy and insubstantial, an afterthought. The only things that I believed mattered were technology and hard design. I ve learned that what I considered hard or difficult is, in fact, the easy stuff. The technology issues are the easiest to deal with and don t usually make the most difference. - Dr. Michael Hammer, M.I.T. Professor of Computer Science 15 Turning information into insight

17 Inflection Point From What I used to do to What I need to do to What I need to know Performance Reporting Advanced Audit Analytics Looking back Looking forward Slice & dice Discover & simulate Understand Predict What happened? What could happen? Analyze Optimize Key Performance Indicators (KPI s) Key Risk Indicators & Predictors Information value Hindsight Insight Foresight 16 Turning information into insight

18 What types of questions can analytics answer? Hindsight Insight Foresight What happened? How many, how often, where? Where is the problem? What actions are needed? Why is this happening? What if these trends continue? What could happen next? What might be the best outcome? 17 Turning information into insight

19 The crunchy question approach to realizing analytic value Crunchy questions are practical, detailed inquiries into tough business issues roll-up-your-sleeves questions. Crunchy questions are designed to lay the groundwork for action Why crunchy questions? They crystalize the business challenges/issues that are at the heart of any Business Analytics Initiative Characteristics of crunchy questions Specific Relate to a particular business process and aligned with strategic goals Focus on optimizing or innovating, not merely informing Consider change relative to other indicators or processes If its not at the heart of the business, it doesn t count -Deloitte Analytics professional 18 Turning information into insight

20 ILLUSTRATIVE Analytics roadmap and data governance model Multi-Year Roadmap Building a two to three year roadmap that details the way in which your analytics program will be implemented over time can help stakeholders understand and realize the benefits and objectives. Everyone has the opportunity to share the same vision and have bought in including Internal Audit, IT, business owners and senior leadership. The road map should be designed to evolve and will likely require regular updates and maintenance throughout the multiyear delivery program. Prioritize projects Focus early investments on projects with a high probability of producing business value. Grade yourself There are lots of different maturity models to assess your starting position use as many of them as you can. It s the smart way to get a clear view of the gaps you ll need to close. 19 Turning information into insight

21 ILLUSTRATIVE Analytics roadmap and data governance model (cont.) Fill the cracks Even smart projects in isolation may not cover all the capabilities needed. But if you augment them with investments that span silos such as enterprise data management, information delivery, and visualization tools, you may quickly build the necessary foundation getting even more value from discrete projects. Governance Establishing a program governance model is critical to the success of many large, complex initiatives that span across an organization/years. The governance structure will likely determine the escalation path for projects that have significant risks and have the authority to scope and de-scope each project. 20 Turning information into insight

22 Practical examples

23 Practical examples of data analytics Competitive intelligence Coffee maker manufacturer why their warranty returns were significantly higher than their peers Supply chain Innovative thinking Customer influence Pharmaceutical company how to optimize sales rep office visits Major U.S. airline predicting workplace hazards and employees at risk Credit card issuer how to find new customers in a saturated market First Time Defaulters 22 Turning information into insight

24 Closing thoughts Data analytics requires innovative thinking about sourcing data and identifying risks Data analytics is as much, if not more, about asking the right questions as it is about the mathematical contortions going on behind the scenes 23 Turning information into insight

25 Contacts John Lucker Principal Global Advanced Analytics & Modeling Market Leader Deloitte Consulting LLP Maureen Errity Director U.S. Center for Corporate Governance Deloitte LLP Turning information into insight

26 36 USC Member of Deloitte Touche Tohmatsu Limited This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.

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