PwC presentation at the Chief Analytics Officer, Fall 2016

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The Chief Analytics Officer, Fall event brought together over 200+ Chief Analytics Officers, Data Leaders, Senior Analytics Experts and Innovators in New York on October 5-7. The three day conference was filled with networking, high level insight and discussion, addressing the hottest topics and challenges faced by CAOs and Senior Data & Analytics professionals.

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PwC presentation at the Chief Analytics Officer, Fall 2016

  1. 1. Don’t Be Data Rich & Decision Poor Insights from PwC’s Big DecisionsTM Research CAO Forum Fall - NYC October 2016
  2. 2. PwC Decision making models….. 2 “Guitar groups are on the way out.” Dick Rowe, Decca Records executive, 1962 I’m bringing you into the decision making process Ruggles, here – flip this coin! Problem Solving / Decision Making
  3. 3. PwC 3 Think of a bad decision your company has made? Think of a good decision your company has made? What was the difference?
  4. 4. PwC 4 Mind vs. Machine 10,000 Brains Anywhere, Anytime To Trust or Not to Trust Data Ecosystems 4 V’s of Data Show Me a Picture, Please What will you do differently? PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™
  5. 5. PwC PwC’s Global Data and Analytics Survey 2016: Big DecisionsTM 5 Why • Strategic decisions create value for an organisation. • Decision-makers are now face-to-face with an opportunity to learn from massive amounts of data. • How can we apply data analytics to create greater value? What • What types of decisions will you need to make between now and 2020? • What types of data and analytics do these decisions require? • What is the role of machines in decision making? • What’s your ambition for improving your company’s decision speed and sophistication to make these decisions? Who • 2,100+ seniordecision-makers • 50+ countries • 15 industries PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™
  6. 6. PwC 0% 5% 10% 15% 20% 25% 30% 35% Developing or launching new products or services Entering new markets with existing products or services Developing Partnerships Investment in IT Change to business operations Corporate restructuring or outsourcing Entering a new industry or starting a new business Shrinking existing business Other Decision Which one of the following best describes this key strategic decision? Global The leading “big decision” across Global Markets is “developing or launching new products” followed by “entering markets” and “investment in IT ”, all projected to increase shareholder value Most Important Strategic Decisions& Impact Across all strategic decision types, on average 90% of leadership thinks their strategic decision will increase shareholder value, with the majority estimating 5-50% increase and 1/3rd estimating 50- 200% increase PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ *n = total # of the topkey coming strategic decisions 6
  7. 7. PwC Organizations are in different stages in their approach to using data and analytics to support strategic decision making Evolving capabilities,finding their way….. Reconciling how to integrate “gut based” approach and avoid bias….. Hampered by structure….. Somewhat detailed incorporating lengthy periods for reflection and refinement. Hierarchical validation within a fragmented decision making structure We are growingour useof complex data sets and relying more and more on external market data to make decisions. Generally analytics arerarely relied upon…fromabusinessperspective data does not drive ourdecisions. We make decisionsand then find supportingdata tojustify them. Fragmented & ad hoc Especially manual data processing(low speed and small amount of data that we can handle at on-time) …improving...data is becoming more andmore key in decision making It is patchy. There is still a noticeable reliance on gut based on what has been experienced in the past. Continuingtoevolve; have recently implemented big data effort/strategy to enhance use The use of comprehensive analytics to inform pro- active decision making 7 PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™
  8. 8. PwC 3 9 % 5 3% 8% Highly data-driven Somewhat data-driven Rarely data-driven Most companies are not “highly data driven” and rely on descriptive and diagnostic analytics the most Global Which of the following best describes decision-making in your organization? Majority Aren’t Highly Data Driven.. …Or Using Predictive or Prescriptive Similar pattern across industries 8 PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ *n = # of type of data-driven organization *n = # of type of data-driven organization by type of analytical technique applied 0% 5% 10% 15% 20% 25% 30% 35% Descriptive (What has happened?) Diagnostic (Why did it happen?) Predictive (What will/could happen?) Prescriptive (What should happen and how?) The use of analytics in your organization is mostly… Highly data-driven Somewhat data-driven Rarely data driven Global
  9. 9. PwC The new order will change the balance of algorithms and human judgment used in decision making and make “unknown” risks “known” 9 Reliance on Judgment vs. MachineAnalysis by Risk Profile (n= # of Decisions) • Complement human judgment with machine algorithms (i.e. AI) • Continuously improve algorithms Strike the right balance of mind & machine…. • Know somethingyour competitors don’t • Be the first to react to emerging, latent demand • Migrate from “beta” to “alpha” Address risks by making them known…. PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Opportunities Mind&&Machine –Therightbalance Known Manageable…...….RISK…….….Unknown, Uncertain MachineAlgorithms....ANALYSIS…..HumanJudgement Make Unknown Risks Known
  10. 10. PwC The satellite selected has a spectral resolution of a 31cm per pixel, the highest commercially available, for the analysis Collect data in novel ways… Perform market sizing analysis in emerging markets… Use Satellite Imagery to Size Markets PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ 10 Finding the right mix of “mind and machine”…..
  11. 11. PwC Finding the right mix of “mind and machine”….. Use Drone Imagery to Assess Capital Projects Identify likely safety and code violations Reduce schedule overruns… PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Key: ConcreteBackground Steel Reinforcement Wooden Boards 11
  12. 12. PwC Use of human judgment and machine algorithms varies by industry across decisions that involve know and unknown risks X-Axis(EachGraph) MachineAlgorithmsvs.HumanJudgment Y-Axis (Each Graph) Known vs. Unknown Risks Health Services Pharma & Life Sciences Technology Communications Entertainment & Media Retail & Consumer Energy, Mining, Utilities Industrial Products Insurance Banking & Cap Markets Reliance on Judgment vs. MachineAnalysis by Decision Risk (n= # of Decisions) 12 PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™
  13. 13. PwC Companies are at different levels of maturity in decision making “speed” and “sophistication” to create value…… Speed • Time to answer question • Time to decide action • Time to implement / measure Sophistication •Analytics maturity •Data breadth & depth •Decision approach PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Sophistication Accelerated Agility Master the Chess Mov es Intelligence in the Mom ent Cov er the Basics Low High LowHigh Speed Increasing sophistication should simplify, not increase complexity Speed is as much about structure as it is about data & analytics PwC’s Decision Sophistication& Speed Matrix (n=# of decisions) 13
  14. 14. PwC Increase “speed” and “sophistication”….. PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Simulate Adoption of Autonomous Personal Mobility Solutions More Speed….. More Sophistication….. • Quickly analyze and adapt go-to-market approaches based on in market feedback • Simulate a million ‘consumer’ agents and their purchase choices based on causal reasoning • Run over 200K + go-to- market scenarios to prescribe the right city, pricing, and # of vehicles Modeling demand for vehicle miles travelled Simulating demand, charging and utilization by geographyDriverless&Electric Vehicles 14
  15. 15. PwC Increase “speed” and “sophistication”….. PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Simulate Adoption of Autonomous Personal Mobility Solutions More Speed….. More Sophistication….. • Faster tracking of frequency of movement and use of space • Complete dangerous inspections • 2-D images are converted to 3-D digital models • Automate inspection and visual analysis with deep learning models Simulating demand, chargingand utilizationby geography Identify & analyze physical objects to deliver new insights 15
  16. 16. PwC Increase “speed” and “sophistication” PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Machine Learning/NLP: Modeling Willingness to Pay More Speed….. More Sophistication….. • Reduce time of market research • Implement targeted outbound campaign messaging • Leverage Word2Vec NLP techniques to go beyond “positive / negative sentiment” • Design more targeted price points 16
  17. 17. PwC Ambition is high to improve decision speed and sophistication Orange shows today; blue shows where companies want to be by 2020 17 Global PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ LowHigh Speed Low High Sophistication 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 United States
  18. 18. PwC 18 Each decision type requires a focused approach for improvement Focus may require improving speed, sophistication or both. Developing/Launching New Products PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Entering New Markets Improve Operations Investment in IT
  19. 19. PwC Organizations view leadership courage, budgetary constraints, and resource availability as barriers data driven decision making… 19 Barriers to Decisions TheC-Suite is marginally more confident in leadershipcourage, with it’s top two concerns being #1 Budgetary considerations and #2 Available resource/manpo wer PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Leadership courage Budgetary considerations Availability of resource/manpower Operational capacity Policy regulations Issues with implementation Poor market response Ability to analyse data Data limitations TheDecision will likely belimited by… Global - Total *n = top decision by top limitation
  20. 20. PwC 20 PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Improved decision making with Data & Analytics requires overcoming common decision traps Anchoring Trap Overconfidence Trap Status-Quo/Sunk Cost Trap Confirming- Evidence Trap (Confirmation Bias) Framing Trap Availability Bias (Rush to Solve) Disproportionate weight to first information received Overestimate judgment and predictions; remember success, forget errors Perpetuates the current state or past decisions; risk-averse mindset Seek supporting information; avoid contradictory information How a problem is framedinfluences the decisions made Rely on information that is most readily available Show options & present range of facts Use gaming Simulate and quantify risk of status quo Leverage benchmarks Use different framings (competitor, customer, employee) Create Comprehensive Decision Support Systems Decision Trap DescriptionMaximizeD&A Impact
  21. 21. PwC 21 PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™ Effectively making decisions with D&A requires tailoring the approach and benefits to the decision makers style Controller SkepticFollowerCharismatic Thinker The Skeptic (Larry Ellison, Steve Case) • Decisions made on gut feeling • Challenges every data point Applying D&A • Co-present with trusted advisor • Emphasize credibility of D&A data sources • Arguments grounded in reality • Presentation capitulates to skeptic leaders’ ego The Charismatic (Richard Branson, Marc Benioff) • Easily enthralled, but uses balanced approach • Emphasize bottom line results The Controller (Martha Stewart, Ross Perot) • Unemotional and analytical • Only implements own ideas The Follower (Peter Coors, Carly Fiorina) • Relies on others’ past decisions to make current choices • Late adopter The Thinker (Bill Gates, Michael Dell) • Toughest to persuade • Risk-averse • Attention to detail
  22. 22. PwC 22 Key findings from Big Decisions survey PwC‘s Global Dataand AnalyticsSurvey2016: Big Decisions™  More organizations are taking a data-drivenapproach to making strategic decisions. Are you?  Data-driven organizationsare using machines to de-risk theirdecisions.  Executives have great ambitionto increase decision speed and sophistication, but everyone expects to fall shortof their ambition. What’s your expectation?  Organizations face many limitationsin their decisionmaking, howeverdata and the ability to analyze data are the least of their concerns.
  23. 23. PwC This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the information contained in this publication w ithout obtaining specific professional advice. No representation or w arranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law , Pricew aterhouseCoopers LLP, its members, employees and agents do not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it. © 2016 Pricew aterhouseCoopers LLP. All rights reserved. In this document, “Pw C” refers to Pricew aterhouseCoopers LLP w hich is a member firm of Pricew aterhouseCoopers International Limited, each member firm of w hich is a separate legal entity. Thank you For more information visit, www.pwc.com/bigdecisions Continue the conversation with us online, follow: PwC Advisory Services, @PwCAdvisory Paul Blase, Global and US Data and Analytics Consulting Leader, @paulblase 23

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