Slides from my coaching presentation to a large governmental regulatory agency as their consultant on the value of "predictive data analytics" to leverage IT resources and make significant gains in organizational efficiency and customer service.

1. DataAnalysis
Lecture 1:What Can Data Do forYou?
MJ Clark
Business Development Coach
Raleigh, NC

2. Basic steps to
building a
predictive
data analysis
model for an
“actionable”
business
problem.
Define
Business
Problem
Collect and
Prepare Data
Train and
test model
Deploy
Model
Monitor
Model’s
Performance
1
2
3
5
4

3. DataTypes
Classifications of
data sources for
analysis.
Interaction Descriptive
Attitudinal Behavioral

4. Interaction
Data
Call Center
Email
Mail Center
Web: Clicks, Downloads, Submissions,
Rejections, Payments
In Person: Submissions and payment
at front desk
Inactivity and Dormancy

5. Descriptive
Data
Demographics
Attributes
Characteristics
Self-declared Information

6. Attitudinal
Data
Opinions
Preferences
Needs and Desires
Survey Results
Social Media

7. Behavioral
Data
Transactions
Orders
Payment History
Usage History

8. Three Pillars of
Predictive
Analytics
1) 1. Customer
2) 2. Operational
3) 3.Threat & Fraud

9. Three Pillars of
Predictive
Analytics
1) 1. Customer
2) 2. Operational
3) 3.Threat & Fraud

10. Three Pillars of
Predictive
Analytics
1) 1. Customer

11. Define our customers
Who are our customers?
Which customers cost us the least to serve?
What are the attributes of our best customers?
Who are our most expensive customers?
Why are certain customers cost-effective while others are more expensive to serve?
1) 1. Customer

12. Follow up questions:
How can our customers be divided into categories, such as by professions, filings,
payment history, business types?
What actions can we take to change customer behavior to gain efficiency? Goals such
as motivating more people to file online, quit calling, file forms correctly the first
time, or to pay by credit card rather then check?
What can be gained from this knowledge?
1) 1. Customer

13. Follow up questions:
Who are our best customers?
Who are our problem customers?
How will the new website affect our users?
Will the new website increase or decrease online filing rates?
How can we turn our largest customers into brand advocates?
1) 1. Customer

14. 1) 2. Operational

15. Define our operational
goals in these areas:
• People
• Process
• Assets
1) 2. Operational

16. Define operational goals
People Allocate the right staff level in the right place at the right time
Increase automation and IT knowledge levels
Process Reduce paper handling times
Realize technology gains in efficiency to reduce costs
Understand what causes failures
Identify data risks and potential fraud patterns
Assets Determine which expenditures will yield better results
1) 2. Operational

17. Threat and Fraud goals
People Train employees in IT, Data Analysis, andThreat and Fraud
Process Identify data risks and potential fraud patterns
Address risk areas
Identify lack of cross-unit coordination and its problems
Assets How much financial fraud or identity theft exists?
Determine which technology expenditures will yield better results
1) 3.Threat and Fraud

18. http://www.pewinternet.org/2017/03/22/what-the-public-knows-about-cybersecurity/
Support IT to
identify and
offer Online
Security
Training and
Data
Protection
Resources

19. Model Building
Examine historical data, such as
rejections, document types,
abandoned entities or
abandoned carts.
Is there something we can learn
about them from their history or
attributes to predict a likely
outcome?
Example: Do certain form
designs yield a higher transaction
success rate?
What historical values will be
useful?

20. Predictive Scoring
Once the model is
developed based on
our hypothesis, the
next step is to
implement
predictive scoring
of new data based
on the model.

21. Implementation
Implement data
governance model
and monitor
model’s
performance.
Adjust resources as
necessary.

22. Data Governance Stakeholders?

23. Obstacles to Data Governance

24. DATA
What can we learn from our data?

25. Sources
&
Credits
1. Brad Hill, Product Marketing Manger for IBM SPSS Modeler
onYouTube
2. TechEd onYouTube
3. Big Data: How Data Analytics isTransforming the World by
Dr.Tim Chartier
4. Pew Research Center, InternetTechnology
5. Fraud Analytics: A Case Study and DecisionTrees by Jigsaw
Academy onYouTube
6. Managing Change for Successful Data Governance by
Dataversity on SlideShare
Thank you to the following: