The power of analytics for better and faster decisions

Transcription

1 27 June, 2016 WEF Annual Meeting of the New Champions The power of analytics for better and faster decisions

2 s 2016 Global Data and Analytics Survey Big Decisions TM 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 decisionmakers 50+ countries 15 industries 2

3 What is a big decision? 90% of respondents think their next strategic decision will increase shareholder value, ranging up to a 200% increase. 3

4 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? Note: Survey data is still being collected and final results may change. 4

5 Polling question Which of the following best describes decision-making in your organisation? 1. Highly data-driven 2. Somewhat data-driven 3. Rarely data-driven 5

6 The landscape is changing A high percentage of companies consider themselves data-driven Global China Highly data-driven and data-driven companies are making these strategic decisions Developing or launching new products and services Entering new markets Somewhat datadriven Rarely data-driven 6

7 Why look at decision speed and sophistication? Improving both can help maximise return on investment Speed Time to answer question Time to decide action Time to implement and measure Speed Low High Low Sophistication s Decision Sophistication & Speed Matrix (n=# of decisions) High Sophistication Analytics maturity Data breadth and depth Decision approach 7

8 Ambition is high to improve decision speed and sophistication Orange shows today; blue shows where companies want to be by 2020 Global China 8

9 Capabilities vary by country for speed and sophistication Speed Low High Low Sophistication High Speed Speed Low High Low High High Low High Low High Sophistication Sophistication Speed Speed Low High Low High Low High Low High Sophistication Sophistication 9

10 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 Speed Low High Speed Low High Speed Low High Low High Low High Low High Sophistication Sophistication Sophistication 10

11 Polling question What is more critical to you? 1. Improve speed in decision making 2. Improve sophistication of analysis 11

12 Everyone will fall short of their ambition but less so in China Global China Existing Likely in 2020 Needed in

13 A significant role for machines is emerging and companies are taking advantage of what machines offer What will the analysis informing your next decision require? 41% Machine analysis/algorithms 59% Human judgment 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. 13

14 Companies can de-risk decisions by using machines ANALYSIS Machine Algorithms...Human Judgment Global Known Manageable...Unknown, Uncertain RISK Reliance on Judgment vs. Machine Analysis by Risk Profile (n= # of Decisions) 14

15 s Global Data and Analytics Survey 2016: Big Decisions The use of human judgment and machine algorithms varies by country China United States Japan UK Germany 15

16 The survey reinforces that data driven companies are using machine algorithms more pervasively Global Human Judgment Machine Algorithms 16

17 and also shows that data driven companies are much more likely to be using predictive and prescriptive analytics. Global China Predictive Prescriptive Diagnostic Descriptive 17

18 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 18

19 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. 19

20 Thank you For more information visit, Continue the conversation with us online, follow: Dan DiFilippo, Global and US Data and Analytics Advisory 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 PricewaterhouseCoopers LLP. All rights reserved. In this document, refers to PricewaterhouseCoopers LLP which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity. 21