Best Practices For Predictive Analytics Projects

Best Practices In Predictive
Analytics
Keeping things simple…
June 10, 2014
Ajay Gopikrishnan
Lead Architect – Analytics & Bigdata

2
Business Information Management
Copyright © 2014 Capgemini. All rights reserved.
Best Practices in Predictive Analytics | June 2014
What is Predictive Analytics (PA)?
Definition
Predictive Analytics is the application of statistical
techniques and BI technologies to uncover
relationships and patters from within large volumes of
data that can be used to predict behavior or events of
interest
Source: TDWI.org

3
Predictive Analytics in action…
!  Predictive analytics (PA) is used to address churn
!  PA is used to predict the likelihood of response to a mailer
!  PA is used to predict the risk of default on a credit card
!  PA is used to predict the average time to failure for a particular industrial
heavy machine
Predictive
Analytics is more
forward looking
compared to
regular BI – we
use past events
to anticipate the
future!

4
Why is PA not widely deployed?
!  It is usually complex and calls for a combination of skills
!  The value generated is often under-rated
!  Software is expensive
!  Dependency on good quality data
!  PA is often taken up more as an experiment and not core to function

5
Best practices in Predictive Analytics
Best Practices
Data &
Technology
Organization
Process
Structured
Unstructured /
semi-
structured

6
Impact
Marketing
It is important to measure the RoI of PA projects
simply because organizational resources are
ploughed into action
Mailers are sent
Credit Cards Applications are denied / downgraded
A Canadian bank
uses PA to
increase
campaign
response rates by
600%,
cut customer
acquisition costs
in half, and boost
campaign ROI by
100%. TDWI.ORG

7
Analytical Metrics
It is more important to evaluate PA projects by
using a set of Business metrics rather than
analytical metrics
!  R-square
!  Lift
!  ROC Curve
Business Metrics
!  Response rate
!  Gross sales
!  Net Profit
No one gets a
raise or a bonus
based on R-
square or lift !
- Build business
metrics into the
Analytical Plan

8
Be prepared to
spend a good of
deal of your
project time (75%
sometimes) on
data management
The goal of PA is to isolate the variables from
amongst a large set that can best explain the
event or behavior of interest; normally EDW
tables cannot be used as such
Data jobs
!  Merges & joins
!  Transformations
!  Data Quality
Process jobs
!  Exploratory analysis
using central tendency
measures
!  Detect outliers

9
You are trying to
model human
behaviour so do
not expect a
silver bullet –
expect
incremental
improvements in
current level of
organizational
performance
PA models have a learning curve so be
prepared to stay invested over time to improve
performance and reap benefits

10
Better is usually
measured in
terms of business
metrics
Complex Better
A neural network is not necessarily better than
Linear Regression if the basic assumptions of
Linear regression are being met in the given
business problem

11
PA projects can be outsourced to an expert
agency after suitable due diligence based on
resource availability and comparative cost-
benefit analysis with respect to in-sourcing
Outsourcing models
!  Leading organizations are known to set up captive analytics
service centers in remote locations where skills are available
!  BPOs/KPOs are known to undertake process elements in a
PA project

12
There is a
substantial
shortage of Data
Science skills in
the market;
Companies are
tying up with
academic
institutions for
PA programs
PA project teams comprise multiple skills – most
successful teams branch out as “Information
Management” as a bridge between business
and IT
PA teams
!  Business analyst
!  Quantitative expert
!  Tools expert

13
Let it not be
reduced to a
research effort
by a PA
enthusiast
within a line
function
PA Projects require executive sponsorship; it is
better to adopt a top-down approach, that
originated from the business even if they are
small projects to begin with

14
Cost of
deployment is
high; hence
need to measure
RoI of PA
projects
PA needs to exploit the Enterprise Data
Warehouse and enabling technology like In-
database and In-memory for scale, faster
throughout and a more comprehensive
approach (think enterprise PA!)
Example technology
!  SAS + Teradata
!  SPSS on Netezza
!  Analytical Sandboxes

15
PA is a
combination of
both art and
science!
There is no best software for Predictive
Analytics; the major contribution is efficiency; it
is up to the user to design the project, define
KPIs, evaluate candidate models and choose
the best model appropriate for the problem
PA + Big Data + Cloud
!  PA applications are deployable on the cloud
!  PA vendors are now building compatibility with Hadoop and
related technology to handle unconventional data types

16
The PA Maturity Curve

17
PA can create business value for the organization
Ajay Gopikrishnan
Lead Architect – Big Data & Analytics
ajay.gopikrishnan@capgemini.comom
Thank you!
Reference: TDWI 2010 paper by Thomas Rathburn - 10 mistakes to avoid in predictive analytics

Best Practices For Predictive Analytics Projects

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