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CPA ONE 2016 - Big data: big decisions or big fallacy

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CPA ONE 2016 - Big data: big decisions or big fallacy

  1. 1. Big data: big decisions or big fallacy THE ONE NATIONAL CONFERENCE SEPTEMBER 19-20, 2016 VANCOUVER, BC
  2. 2. 1 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 What is big data? What is the language of big data and analytics? How is it relevant for you? What are the lessons learned so far? Laurie Desautels Director Digital Part of the PwC network 1 2 3 4
  3. 3. Information is the oil of the 21st century and analytics the combustion engine. — Peter Sondergaard, Gartner 2 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016
  4. 4. 3 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016
  5. 5. 4 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Is your organization … Highly data-driven Somewhat data-driven Rarely data-driven 1 2 3
  6. 6. 5 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Source: PwC's Global Data and Analytics Survey 2016 | Canadian insights Organizations are seeking the right mix of mind and machine to leverage data, understand risk, and gain a competitive edge.
  7. 7. 6 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 @SOURCE What is big data? 1
  8. 8. “The techniques and technologies that make handling data at extreme scale affordable” – Forrester “Big data is high volume, high velocity, and high variety information assets requiring new forms of processing” – Gartner 7 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016
  9. 9. 8 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 “Big Data is all about finding correlations, but Small Data is all about finding the causation, the reason why.” – Martin Lindstrom, author of “Small Data: The Tiny Clues That Uncover Huge Trends” @SOURCE
  10. 10. 9 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 @SOURCE and this was from 2012! Everyday, we create 2.5 quintillion bytes of data – so much that 90% of the data in the world today has been created in the last two years alone. Where does big data come from?
  11. 11. 10 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 The nature of the data keeps changing as the software platforms evolve iMessage 2016 @SOURCE: http://www.kpcb.com/internet-trends
  12. 12. 11 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Ellen Degeneres’ tweet from the Oscar’s in 2014 had over 3.3m retweets. @SOURCE Wal-Mart has 100,000,000 customers per week @SOURCE In 2000, Sloan Digital Sky Survey collected more data in its first few weeks than the entire data collection in the history of astronomy. @SOURCE Sequencing the human genome originally took 10 years. An ancestry DNA test can now be purchased for less than $200 and results received within a few weeks. @SOURCE What does big data look like?
  13. 13. The lexicon of big data 12 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 2 Big data has no value without the insights human expertise and analytics can tease out of it. Analytics is the combustion engine of the information age
  14. 14. 13 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Examples Techniques Questions Diagnostic discover & explore Why is it happening? Where is the problem? What are the trends? • Agile Dashboards • Cause and effect • Correlations • Behavioral analytics • Data & text mining • HALO • Risk Analytics • Rapid BI apps • Workforce analytics • Analytical apps Prescriptive anticipative What should I do? What is the next best action? • Optimization • Artificial Intelligence • Machine learning • Simulations • Analytical apps with simulated outcomes Descriptive reporting What happened? What is happening? • Business Reporting • Scorecards • Business Intelligence • HALO • Financial performance results • Staff performance scorecards Predictive forecast What is likely to happen next? • Predictive modeling and statistical analytics • Regression analysis • Forecast modeling • Strategy & growth analytics • Customer analytics • Fraud & Cyber analytics, etc. The increasing value of analytics
  15. 15. 14 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Examples Techniques Questions Diagnostic discover & explore Why is it happening? Where is the problem? What are the trends? • Agile Dashboards • Cause and effect • Correlations • Behavioral analytics • Data & text mining • HALO • Risk Analytics • Rapid BI apps • Workforce analytics • Analytical apps Prescriptive anticipative What should I do? What is the next best action? • Optimization • Artificial Intelligence • Machine learning • Simulations • Analytical apps with simulated outcomes Descriptive reporting What happened? What is happening? • Business Reporting • Scorecards • Business Intelligence • HALO • Financial performance results • Staff performance scorecards Predictive forecast What is likely to happen next? • Predictive modeling and statistical analytics • Regression analysis • Forecast modeling • Strategy & growth analytics • Customer analytics • Fraud & Cyber analytics, etc. The increasing value of analytics
  16. 16. 15 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Active Passive Data has traditionally been actively captured. Today, data is increasingly passively captured.
  17. 17. OT IoT The Industrial Internet 16 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 IT, operational technologies (OT) and the internet of things (IoT) are converging to create the industrial internet (or what PwC calls Industry 4.0) Big data is an output of the industrial internet. Data and analytics are core competencies in this new world of Industry 4.0. IT
  18. 18. 17 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016     Volume GB, TB, PB, EB, ZB     Variety Structured, unstructured, and semi- structured such clickstream, text, image, video, geolocation, …    Velocity Speed In which analysis of data occurs and data is delivered for analysis    Veracity Uncertainty, predictability, and integrity of data The 4Vs of big data
  19. 19. 18 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 data measured in TB data measured in ZB new large scale data that is semi-structured, unstructured, or unproven (with potential value) proven structured and semi- structured data sources Multiple new technologies and the cloud deliver big data capabilities What are the emerging data platforms? NoSQL DB Columnar DB NewSQL DB Big Data Appliances Distributed File System
  20. 20. 19 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 The value of a data lake is in finding clues to help your organization answer high priority questions. @SOURCE Modern data architectures leverage data lakes as a repository for large quantities and varieties of data, both structured and unstructured.
  21. 21. Value is created by using traditional and big data, human and machine learning, BI and analytics Traditional mindset Big data mindset 20 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Reporting Analysis Needs Discovery, predictive Large population with focused needs Audience Small user base with unfocused needs Return on Investment Value for investment Option-creating investments Waterfall Execution Iterative / Agile Model then store Approach Store then model Transactional Sources Interactions Internal Location Outside the company Structured Format Semi-structured and un-structured Business Intelligence Tools Analytics, simulation, visualization SQL Languages MapReduce, Embedded R, etc. Relational Storage Data Lakes (Hadoop, Cassandra, Mongo, etc.) Traditional ETL (Extract, Transform, Load) Integration Data wrangling, late binding BusinessInformationTechnology
  22. 22. 21 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Storytellers, Visualization Specialists, and Business Analysts Information Architects Master Data Management Specialists Data Scientists Data Modelers Data Extraction Specialists Ability to communicate and evangelize. Creative, investigative, analytical minds with Industry or business domain knowledge Information and data architecture, data quality, and master data management skills Statistical programming skills, adept at advanced techniques (algorithms) and languages (R, SAS, etc.) Programming skills and development methodology. Application development and implementation experience. Programming skills with data discovery and mashing/blending large amounts of data skills. DBMS skills, data extraction, transformation, load. Detail oriented to ensure completeness and accuracy. Analytics Applications Implementers The data needs to tell a story, but to get there you need a variety of skillsets
  23. 23. 22 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 @SOURCE A great visualization ... http://www.informationisbeautiful.net/vis ualizations/worlds-biggest-data- breaches-hacks/
  24. 24. What does it mean for you? 23 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 3 @SOURCE: Artwork by David Somerville, based on an original drawing by Hugh McLeod
  25. 25. 24 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 days for finalization of monthly / annual reports Monthly & annual reporting Budgeting Controlling FTEs Business Insights Cost of Finance days to complete the budget less FTEs in Controlling than peers more time spent on data analysis vs. data gathering less cost of Finance than peers +20% -40%-20%304/7 Source: PwC, Finance Effectiveness Benchmark & Digital Controlling Study, 2015 The finance function in best practice companies spend increased time generating insights from data
  26. 26. 25 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Our customers are more sophisticated. How do we provide better value? What drives customer satisfaction in my business? Who from my team is likely to leave and how can we prevent that? Is my sales force behaving with proper conduct? The concept of big data says you don’t know what data to collect because you don’t even know what the questions are, now or in the future. Are you asking the right questions?
  27. 27. 26 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Goal What is the question you are asking? Identify required data Obtain data Prepare data Analyze Data Did we answer the question? Agile Analytics takes a “fast fail” approach to developing analytics solutions
  28. 28. 27 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 A significant role for machines is emerging and companies are taking advantage of what machines offer A machine learning example Source: PwC’s Global Data and Analytics Survey, July 2016. Q: What will the analytis informing your next strategic decision require? Global base: 2,106 senior executives. Machine algorithms Human judgement
  29. 29. 28 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 The new role of finance: Balancing mind and machine A spend-analysis machine (SAM), compiles and classifies millions of financial transactions and gets smarter the more data it processes. SAM finds optimization opportunities and makes timely recommendations—such as how much you could save by taking advantage of volume discounts—enabling you to make decisions on negotiations and spending to realize savings.
  30. 30. 29 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 “One thing is certain: the profession is moving away from the basic bookkeeping chores toward the more sophisticated analytical tasks.” – Monique Morden, Chief Revenue Officer at Lendified in Vancouver Source: “I robot, CPA”, Yan Barlow, CPA Magazine, August 2-016 https://www.cpacanada.ca/en/connecting-and-news/cpa-magazine/articles/2016/august/i-robot-cpa
  31. 31. Do you need a decision diagnostic? What are the lessons learned to date? 30 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 4
  32. 32. 31 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Accelerated agility Master the chess moves Intelligence in the moment Cover the basics Low HighSophistication SpeedLowHigh Decision Archetypes • Data-driven decisions trump intuition • Hindsight & foresight with all available data • Slow consensus driven and analytic decisions • Intuition based decisions – little analysis • Descriptive reporting with internal data • Low frequency data and model refresh • Speedy decisions trump analysis / consensus • Descriptive reporting with internal data • Rapid analyse-decide-act feedback loop • Data & intuition drive decisions • Hindsight & foresight with all available data • Advanced analytics with feedback loop You must apply analytics for your big decisions. For each type of decision, what do you need?
  33. 33. 32 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Improving both speed and sophistication helps maximize the return on investment for data and analytics @SOURCE Increasing sophistication should simplify, not increase complexity Speed is as much about structure as it is about data and analytics
  34. 34. 33 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Enterprise adoption Deliver and scalePilot and proveDue diligenceIdeation Innovation processes The big data value chain intakes rough ideas on how to use information strategically and create actionable insights ITGovernance ITGovernance ITGovernance ITGovernance ITGovernance Investment Investment Investment Investment Investment Refer, Defer, Kill BusinessGovernance BusinessGovernance BusinessGovernance BusinessGovernance BusinessGovernance Refer, Defer, Kill Refer, Defer, Kill
  35. 35. 34 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 Consumer attitudes are hardening as more data is gathered, used, shared, and sold. Lawmakers and regulators will respond.
  36. 36. 35 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 7% 7% 8% 8% 8% 12% 15% 26% 11% 12% 10% 10% 12% 15% 4% 18% Infrastructure and/or architecture Obtaining skills and capabilities needed Funding for Big Data-related initiatives Risk and governance issues Integrating multiple data sources Defining our strategy Understanding what is "Big Data" Determining how to get value from Big Data % of respondents Top challenge 2nd Source: Gartner, Big Data Industry Insights What are the top hurdles or challenges with big data?
  37. 37. As the tools and philosophies of big data spread, they will change long- standing ideas about the value of experience, the nature of expertise, and the practice of management. 36 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016 @SOURCE
  38. 38. If you’re making decisions, trusting data shouldn’t be holding you back. What you should be thinking about is how to frame the problem, how you can take advantage of the available data that’s out there, and what the strengths and weaknesses are of the approaches to use the data. — Dan DiFilippo, Global and U.S. Data & Analytics Leader, PwC 37 Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016
  39. 39. 38 @SOURCE Big data: big decisions or big fallacy, presented September 20 at CPA CANADA THE ONE NATIONAL CONFERENCE 2016
  40. 40. 39 © 2016 PwC. All rights reserved. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details. This content is general information purposes only, and should not be used as a substitute for consultation with professional advisors. Thank you. Laurie Desautels Director Digital Part of the PwC network laurie.desautels@pwc.com www.strategyand.pwc.com

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