A presentation delivered by Robert Brooks at the Police Foundation's annual conference 'Policing and Justice for a Digital Age' (December 2016) on using big data and predictive analysis.

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

10. PwC
Model
Testing !
Outcome
Action
Background
General process

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

19. PwC
The Application
Policing

20. Questions?
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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