Robert Brooks, PwC
- 1. Using big data and predictive analysis Robert Brooks & Matthew Tomlinson www.pwc.com
- 2. PwC Agenda The Background The Key Requirements What is it? What is needed to make it work? The Application How have we used in the past?
- 3. PwC Background What is Data and Predictive Analytics? Data mining Future probabilities and trends 3
- 4. PwC Data, analytics, technology and information management are all evolving at a rapid pace that is set to accelerate in the future… and it will spare no industry 1980’s 1990’s 2000’s 2010+ 1970’s Reports OLTP Punchcards Data Processing Decision Support Websites Audio Finance Management Analyst Model f(x) X1 X2 X3 Y1 Y2 Multivariate Analysis Business Intelligence Predictive Modeling Information Worker Simulation & Visualization Social Media The Data Scientist Embedded Analytics Mobile The Data Warehouse The Data Warehouse Appliance Big Data RDBMS Smart Phones & Tablets Increasing pace of evolution Background Advances in Data & Analytics over time Access to a large wealth of modelling algorithms and techniques Cheap(er) storage and computing power (e.g. cloud based solutions) Exponential development of data available (internal and external to organisations) A significant change in paradigm: 4
- 5. PwC Background Policing data 5 of staff records 1,000s of addresses millions of victims millions of ANPR hits billions of vehicle records 100s of phone records 100,000s of financial records 100,000s of offender records 100,000s of witness statements millions of intelligence reports 100,000s of calls millions of crime reports millions
- 6. PwC Background Internet of Things Converging and connected technology… 6 Smart devices Sensors Biometrics Wireless Connectivity Nanotechnology Analytics Robotics • A multi-trillion dollar emerging industry • 50 billion connected devices by 2020, generating 40k exabytes of data • 54% of global top performing companies are investing more in sensor technologies • Identified by WEF as a phenomenon that will dramatically transform economic activity (including insurance) Wearables Sources: PwC Digital IQ survey, IDC, Business Insider, World Economic Forum Data storage
- 7. PwC Background Creating the internet of…everything! 7 *50 billion connected devices by 2020, generating 40k exabytes of data Smart sensors & connected devices everywhere*
- 8. PwC Background What is predictive modelling? • Using past data to find patterns • Most well known applications is credit scoring • Statistical models used to segment areas to together • Principally using GLM (generalised linear modelling) • Evolving data science towards algorithmic Machine Learning • Who • When • What • To which group should we … 8 Predictive models Questions
- 9. PwC Background Types of machine learning 9 Supervised Learning: pre-labelled data trains a model to predict new outcomes Example: Sorting LEGO blocks by matching them with the colour of the bags Unsupervised Learning: Non-labelled data self organises to predict new outcomes (e.g. clustering) Reinforcement Learning: feedback to algorithm when it does something right or wrong Example: Child gets feedback ‘on the job’ when it does something right or wrong
- 11. PwC Key requirements What is needed to make it work? The question you are try to answer Data Tools and systems 11
- 12. PwC People Culture Senior buy-in and support Ensure clear communication Ensure outputs are simple and easy to interpret Skillset Processes Identifying the right individuals Establish training Collaboration including experts in other areas The Key Requirements Systems
- 13. PwC Response Integrate with existing processes Keep the output simple Understand the limitations Calculation Key variables and correlation Business and expert judgement and challenge Ethics on using personal data The Key Requirements People Processes Systems
- 14. PwC Software Consider users Start with a proof of concept Consider open-source Data Merging multiple datasets Align with other analytics/ business intelligence Consider sources: Direct, Indirect and External The Key Requirements People Processes SystemsPeople
- 15. PwC The Application How have we used in the past? 15
- 16. PwC The Application Predictive models: Professional Gamblers What’s the problem? Tighter regulation and smaller profit margins require betting companies to be more selective about their customers. How we helped? 16 Identify the customer Determine the cut-off Understand the customer
- 17. PwC The Application Predictive models: Predictive Asset Maintenance What’s the problem? A power company needs to reduce the amount of network downtime from assets that fail. How we helped? 17 Highlight assets with a high risk of failure Integrate with existing maintenance schedule Use real-time data feeds
- 18. PwC The Application Predictive models: Talent retention What’s the problem? A media company wanted to understand and manage the loss of talent in the organisation. How we helped? 18 Predict those at high risk of leaving New performance management system Targeted interventions
- 20. Questions? 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. Robert Brooks T: 020 7212 2311 M: 07725 706822 robert.j.brooks@pwc.com Rob Brooks FIA Associate Director, Actuarial Services Matthew Tomlinson T: 0117 309 2538 M: 07843 372011 matthew.x.Tomlinson@pwc.com Matthew Tomlinson Senior Associate, Data Assurance & Analytics