The power of analytics for better and faster decisions 27 June, 2016 WEF Annual Meeting of the New Champions The power of analytics for better and faster decisions

PwC’s 2016 Global Data and Analytics Survey Big DecisionsTM 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+ senior decision-makers 50+ countries 15 industries

What is a big decision? 90% of respondents think their next strategic decision will increase shareholder value, ranging up to a 200% increase.

Approximately 1/3 of business leaders plan to make decisions around the development of a new product or service by 2020 Which one of the following best describes this key strategic decision? Survey question: Which one of the following best describes this key strategic decision? Note: Survey data is still being collected and final results may change.

Polling question Which of the following best describes decision-making in your organisation? Highly data-driven Somewhat data-driven Rarely data-driven Survey question: Which of the following best describes decision-making in your organisation?  PwC

The landscape is changing A high percentage of companies consider themselves data-driven… Global China Highly data-driven Somewhat data-driven Rarely data-driven … and data-driven companies are making these strategic decisions Survey questions: Which of the following best describes decision-making in your organisation?  Which one of the following best describes this key strategic decision? Developing or launching new products and services Entering new markets

PwC’s Decision Sophistication & Speed Matrix Why look at decision speed and sophistication? Improving both can help maximise return on investment Sophistication Low High Speed Speed Time to answer question Time to decide action Time to implement and measure Sophistication Analytics maturity Data breadth and depth Decision approach PwC’s Decision Sophistication & Speed Matrix (n=# of decisions)

Ambition is high to improve decision speed and sophistication Orange shows today; blue shows where companies want to be by 2020 Global China

Capabilities vary by country for speed and sophistication… Low High Speed Sophistication High Low

Government and Public Sector … and the same is true for industries The Insurance industry is known for advances in analytics. Compared with other sectors, they give today’s capabilities only modest marks. Government and Public Sector Technology Insurance Low High Speed Sophistication Low High Speed Sophistication Low High Speed Sophistication

Polling question What is more critical to you? Improve speed in decision making Improve sophistication of analysis PwC

Everyone will fall short of their ambition – but less so in China Global China Add dots here; add global & china; medians & 3 dots Existing Likely in 2020 Needed in 2020

A significant role for machines is emerging and companies are taking advantage of what machines offer Why? Machines don't replace human judgment but the right mix of mind and machine can reduce the impact of human bias, yield more accurate answers and de-risk the decision - even for complex problems. What will the analysis informing your next decision require? 41% Machine analysis/algorithms 59% Human judgment

Companies can de-risk decisions by using machines Reliance on Judgment vs. Machine Analysis by Risk Profile (n= # of Decisions) Known Manageable...Unknown, Uncertain RISK Machine Algorithms....Human Judgment Global ANALYSIS How we tested the concept 2,100+ decisions 2 dimensions x axis: Risks are known and manageable or unknown and uncertain y axis: Analysis relies on human judgment or machine algorithms

The use of human judgment and machine algorithms varies by country United States China Japan UK Germany

The survey reinforces that data driven companies are using machine algorithms more pervasively… Global Human Judgment Machine Algorithms

…and also shows that data driven companies are much more likely to be using predictive and prescriptive analytics. Global China Predictive Prescriptive Diagnostic Descriptive

What limits decision-making What limits decision-making? Decision-makers say it’s not data or the ability to analyse it Decision-makers feel least constrained by… Ability to analyse data Data limitations These areas hold them back more… Availability of resources Budgetary considerations Issues with implementation Leadership courage Operational capacity to act Policy constraints/regulation of data Poor market response Survey question: This decision will likely be limited by…

What we’ve learned More and more organisations are taking a data-driven approach to making strategic decisions. Are you? Data-driven organisations are using machines to de-risk their decisions. Executives have great ambition to increase decision speed and sophistication. But, everyone expects to fall short of their ambition. What’s your expectation? Organisations face many limitations in their decision making, however data and the ability to analyse data are the least of their concerns.

Thank you For more information visit, www. pwc Thank you For more information visit, www.pwc.com/bigdecisions Continue the conversation with us online, follow: Dan DiFilippo, Global and US Data and Analytics Leader, @DanDGlobal PwC Advisory Services, @PwCAdvisory 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 without obtaining specific professional advice. No representation or warranty (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, PricewaterhouseCoopers 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 PricewaterhouseCoopers LLP. All rights reserved. In this document, “PwC” refers to PricewaterhouseCoopers LLP which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity.