Understanding Business Data Analytics

1
Table of Contents
• Analytical Challenges
• Imperatives
• Road Map
• Functions
• Data Integration and Validation
• Improvement Cycles
Understanding Business Data Analytics
Prepared by Alejandro Jaramillo Copyright © 2013
www.DataMeans.com

2
www.DataMeans.com

 Vendors
◦ Software BI companies use the term Data Analytics to enhance
the value and outline certain functions and capabilities of their
products.
 Technology
◦ IT organizations relate to Data Analytics through the lens of
enterprise solutions, technology architecture, data
management optimization, business users requirements and
data warehousing.
 Business Analytics
◦ Relate to Data Analytics through data analysis to provide
business insights, value and ongoing support to their business
customers
 Executive Leaders
◦ Relate to Data Analytics through results and insights from data
analysis and reports that helps them gain a competitive edge,
predict, manage and strategize the business
12/15/2016Copyright © 2013 www.DataMeans.com 3

12/15/2016
www.DataMeans.com 4
Executive Leaders
Business
Analytics
Vendors
Technology
Lack of alignment on Data Analytics philosophy , roles and strategy
leads to duplication, increases cost and organizational grid lock
Don’t get the all the
insights that they need
Don’t have accurate access to data,
resources or collaboration to answer
important business questions
Competing roles with Business
Analytics, lack of time and focus to
peel the onion for answers
Solution is not optimized or not well
spec. Not aligned to support clients
business grow. Happy and unhappy
customers
Small analytics convergence=Small Benefits
Lack of Analytics Vision Convergence has a Detrimental Effect

5
Data silos
Hard to get data
Long turn around
times and high
cost
Unable to meet
business needs
on time
Too many cooks
cooking the data
Efficient
Access to
the data
Quick turn
around on
data analysis
Focus on
Answering
business
questions vs
getting and
fulfilling
requirements
and specs
Advanced
Analytics to
Drive
Business
Grow
Build
Efficiencies
and reduced
waste
Build
partnerships
with IT and
business units
Excellent
Business,
technical and
data analytics
skills
Operationalized
analytical
findings
• Too much emphasis
on company data
platform and
adherence to use of
IT tools, policies and
procedures
• Too much reliance
on specs and
requirements
• If it is not in IT scope
of work it won’t
happen
• Every variation of
work is associated
with additional cost
and approvals
Analytics organizations are structured:
• For quick response to the business
• To get the job done independently of tools
or platform
• To adapt to changing business needs
• To address a problem from a business
perspective ©2015 Data Means
Prepared by Alejandro Jaramillo Copyright
© 2013 www.DataMeans.com

Lack of Analytics Vision Convergence Creates
 Unhealthy competition for resources and attention
 Competing visions about data assets management,
technology imperatives and transfer of knowledge
 Lack of unified vision of key business performance
metrics
 Redundancy
 Sprout of data silos
 Struggle for control of data assets
 Hinders collaboration among teams

Good Management of Data Analytics is Paramount
to:
 Impact the Bottom line and sustain business
grow
 Establish consistent versions of business Key
Performance Indicators KPIs
 Build synergies and efficiencies
 Reduce redundancy and cost
Executive Leaders Business Organizations
Technology Organizations Technology Partners
Analytics Driving
Business

8

9
Drive strategic
outcomes,
business insights
and answer
business
questions
Balance analysis
with information
needs to find
opportunities
Develop
sustainable and
transferable
analytical
knowledge
Define
performance
metrics, drive
change &
synergies
Manage change
to increase
efficiencies and
profitability
Manage, recruit &
staff Analytical
organizations.
Develop technical
analytical
capabilities.
Establish a single
representation of
business true
reality.
Integrate data
from multiple
Sources.

10
Building & Management
Analytics Practice
Promotion Response
Models/Predictive Models
Customer Segmentation/Data
Analysis/ROI
Study Design/Pre and Post
Change Management Analytics
Sales Force Effectiveness/Field
Force Expansion/Call Plan
Custom Turnkey Analytical
Solutions
Multi Channel Marketing
Analytical Support
Data Integration, Data Marts,
Automation & Validation
Reporting Solutions / Reports
Automation & Rationalization
Digital Analytics
©2015 Data Means

11

12
TV &
Journal
Ads
Email &
DM
A 360 Degree view of customers is critical for business grow
Sales
Digital
Impressions
Sales Force
Activity
Coupons
&
Vouchers
Costumer
Surveys
Costumer
Master
File
POS
Distributors
Financial
& Cost
©2015 Data Means

13
• Customer satisfaction
• Life Time Value
• Segmentation
• Circle of influences
• Demographics
• Attributes
• Email & DM Campaigns
• Engagement Programs
• Digital Impressions
• Coupons & Vouchers
• Loyalty Programs
TheCustomer• Sales Force Effectiveness
• Call Planning
• Incentive Compensation
• Territory Alignment
• Sampling
• Lunch & Learn
Sales $
Explore
Customer
data to
develop
new
insights
Engage
with the
right
message in
the right
channel
Increase Sales
& Efficiencies
Reduce Cost
©2015 Data Means

Analyze Target
Track Report
Business
Grow
14
Business
Performance
CRM/Customer
Relationship
Management
Recruitment Auxiliary
Business Analytics Support
• Data Mining
• Predictive Modeling
• Decision Support Analysis & Reporting
©2015 Data Means

15

Client has a data
analysis, reporting or
processing critical need
or idea that can not be
met through current
systems or resources
Data
Sources
Efficient
Data
Processing
&
Validation
Process
Final Data
work with client
to come up and
implement the
most efficient and
cost effective
solution for
clients needs
Dynamic &
efficient
process to
conduct data
analysis or
reporting
Analytical Functions
Reporting
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©2015 Data Means

 Defining change objective
◦ Reduce Cost
◦ Improve Profitability
◦ Increase Efficiencies
 Establish a quantifiable baseline
 Develop a change process
 Implement change
 Measure change Impact
 Recalibrate process
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Objective
Baseline
Metrics
Implement
Change
Measure
Impact
Recalibrate
Process
©2015 Data Means

18
 Segmentation
 Response Models
 Sizing
 Expansion
 KPIs and Dashboard Reporting
 Incentive Compensation
 Geo Alignment
 Effectiveness Measurement
 Call Plan design and execution
 Test & Control Geo tests
©2015 Data Means
0
20
40
60
80
100
120
140
160
1 2 3 4 5 6 7 8 9 10
Avg Sales
Calls Activity

1 2 3 4 5 6
Ideas
Information
Data
Understand
the
Problem
Set Goals
Estimate
Opportunity
Build
Consensus
Develop
Program
Get Support
Form Team
Set Work Plan
And
Milestones
Develop
Evaluation
Methodology
Run
Program
Review
Interim
Results
Make
Program
Adjustments
NRx Sales
Productivity
Gains
Adherence
Evaluate
&
Measure
19
Inputs Prepare Execute Output EvaluateDevelop
The Promotional Event Process
Inputs Transformation Output Evaluation
Planning Execution Results
Project Cycle
Analytics Functions Promotion Response
©2015 Data Means

20
Population Of Interest
High Value
Targets
No
Targeted
Targeted
Low Value
Targets
Targeted
No
Targeted
Targeted Shift targeting to
Valuable Targets
• Optimized campaigns by
finding the most
valuable customers
• Redesigning targeting
strategy based on data
• Measuring the impact of
campaign using
appropriate statistical
methodology
• Make recommendations
www.DataMeans.com ©2015 Data Means

Repoder
Groups
Score
Range
#
Subscriber
# Cummulative
Subscriber
#
Responders
# Cumulative
Responders
Cumm %
Subscriber
Cumm %
Responders
1 510-806 5,255 5,255 3,000 3000 10% 22%
2 806-870 4,940 10,195 2,500 5,500 19% 41%
3 870-905 4,519 14,715 2,400 7,900 28% 59%
4 905-928 3,731 18,446 2,000 9,900 35% 74%
5 928-945 3,206 21,651 1,000 10,900 41% 82%
6 945-957 2,680 24,332 776 11,676 46% 87%
7 957-966 2,628 26,959 400 12,076 51% 90%
8 966-973 2,522 29,482 300 12,376 56% 93%
9 973-978 2,417 31,899 200 12,576 61% 94%
10 978-981 2,050 33,949 100 12,676 65% 95%
11 981-985 1,944 35,893 80 12,756 68% 96%
12 985-987 1,944 37,837 90 12,846 72% 96%
13 987-988 1,944 39,782 100 12,946 76% 97%
14 988-990 1,944 41,726 90 13,036 79% 98%
15 990-991 1,944 43,671 80 13,116 83% 98%
16 991-992 1,892 45,563 70 13,186 87% 99%
17 992-993 1,839 47,402 60 13,246 90% 99%
18 993-994 1,787 49,189 50 13,296 94% 100%
19 994-995 1,734 50,923 30 13,326 97% 100%
20 995+ 1,629 52,552 22 13,348 100% 100%
Total 52,552 13,348
Score models are used to predict
the likely hood that a customer will
respond to an offering or event.
The score produced by the model is
used to rank customers.
The lower the score the higher the
likelihood to respond
10%
19%
28%
35%
41%
46%
51%
56%
61%
65%
68%
72%
76%
79%
83%
87%
90%
94%
97%
100%
22%
41%
59%
74%
82%
87% 90% 93% 94% 95% 96% 96% 97% 98% 98% 99% 99% 100%100%100%
0%
20%
40%
60%
80%
100%
120%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Score Targeting strategy
Cumm % Subscriber Cumm % Responders
By targeting 35% of the
subscribers we capture
75% of the responders
With scoring model client
will be reaching about a
more profitable groups of
customers at a lower cost
21
©2015 Data Means

22

23
Business
Intelligence
+
Data
Warehousing
+
Inventory
Management
+
Data
Mining
+
Marketing
Optimization
+
Forecast
+
Marketing
Automation
+
Predictive
Modeling
+
Analytical Evolution
©2015 Data Means

Data Integration & Validation
Analytics &
Reporting
Rx
Data
Calls &
Samples
Alignment
Demographic
Promo &
Third Party
Call
Plan
Automated Data Process
Data Standardization
DataMart
Targeting
Promotion
Response
Samples
Optimization
Segmentation
Customer Life
Time Value
Ad Hoc
Brand
Reviews
Marketin
g
Executiv
e
Manage
ment
Field
Force
Support
Call Plan
The Data
The Data
The Processes
The AnalyticsThe Reports
24www.DataMeans.com ©2015 Data Means

Current
Database
New
Database
Both files
Current and new
matched
It is only in
the current
database
It is only in
the new database
Data Migration Making
Sure that your Data is Right
run freqs on matching variables
List and compare a few raw records form bad files to get an idea of the source of mismatches
For large data warehouses migration validating the data is a daunting process
25
Data Integration & Validation

Data Validation Process
Develop process, for
series of files, in
anticipation of file
delivery.
A batch of
files to be
compared
is
delivered
Run QC
Programs on
the batch
files
Assemble
report on
batch files
(concurrent
w/ run)
QC Programming
Review/ annotate
FAIL
Investigate /
fix action
items
If files are close
user runs reports
with new file and
compares results
Pass
log as
file done
26
©2015 Data Means

27

Excellence on Data analytics is not about
• Getting state of the art technology to harness the value of big
data (Hadoop, Phyton, SAS, R…etc…)
• Data warehousing with the best breed data base platform
• Data mining to uncover unknown relationships hidden in the data
• Contracting with the smartest software vendors, experts or
analytics companies
Excellence on Data Analytics is about
• Building the foundation to gain business insights using the
available data in an accurate and timely fashion
• Applying business knowledge and sound data analysis
expertise to answer specific business question
• Having the rigor and knowledge to systematically manage
data assets and transform insights into actionable results
• Continuous development of collaborative relationships with
the business, IT, Vendors and other partners

30
Analytical Engagement

31
The Big
Picture
Goals &
Resources
How &
When
Improve
Improve
•Integration
•New Products Launch
•Field Force Restructuring
•Hiring Freeze
•Reorganization
•Recruitment
•Documented
•Validated
•Efficient
•On Time
•Within Budget
•Flexible
Improve
•Find
•Screen
•Recruit
•Present
•Engaged
Resources
Needs
©2015 Data Means

Important Elements of a Data Analytics Organization
• Adequate # of Staff
• Analytical Skills (Stats, critical and outside the box
thinking)
• Technical skills (data management, programming skills,
problem solver)
• Availability of appropriate technology tools
• Business knowledge and Excellent communications Skills
• Efficient access to data
• Collaboration
• Clear vision of the future and ability to rally others around
the vision

12/15/2016 33
Analytical Skills Data Accessibility
YES
NO
YES NO NO YES
NO
YES
Collaboration Technical Skills
Adequate # of
Staff
Cross
Functionality
Processes &
Standardization
in Placed
Business
Knowledge
Copyright © 2013 www.DataMeans.com
#1
•Data silos/Managed differently. Some not managed but stored
•Different business rules /Poor documentation
•Data is not normalized
•Manual creation of reports
•Kept in different formats(Excel, Access, SQL server, Oracle, DB2,
Cobol, txt, SAS….etc)
•No efficient data access
•No systematic data QC
#1
•Able to use properly statistical methods to answer a
business question
•Able to create business story from data results
•Draws business implications from data analysis and
reports
•Generates the urgency to react and act based on data
results
#2
•Sound process to standardized,
normalized, aggregate, combined,
validate and QC data at different
levels
•Creation of periodic reports must
be automated
•Centralized analytical data mart
#3
•Understands the business and
market trends
•Knowledge about products and
competitive landscape
•Understand sales and marketing
channel and sale force customer
interactions
#3
•No collaboration with IT partners
•No transfer of knowledge
•No sharing of best practice, tools and lessons
learned
•No responsive to the business partners and
continuous changes of requirements and questions
#4
•Appropriate data analysis and reporting technology platform
•Strong data management and analysis programming skills
•Likes to learn new things and welcomes challenges
•Excellent communications skills
•Team player
•Good management skills
#2
•Lack of
technical,
analytical or
managerial
staff.
•Projects under
staff
•Unable to
maintain
ongoing and
take on new
projects at the
same time
The 3 ChallengesThe 4 Achievements

Optimum
Capabilities
Extremely
Valuable for the
Business
Stagnation/
Knowledge,
Technology and
Process
Dissemination
Middle
Capabilities
Adds Significant
Value to the
Business
Getting loss in
the corporate
organization
shuffle/Opportun
ities to Optimize
Analytics
No
Capabilities
Provides Some
Value to the
Business
Becoming
Irrelevant/Signific
ant Opportunities
to Become a
Shining Star
Value
Risks
Opportunities

Developing and maintaining talent is critical
for an analytics organization
• Have a pipeline for new talent
• Career path and career development for
existing talent
• Encourage Innovation and out of the box
thinking
• Build internal and external partnerships for
talent acquisition and development
Senior
MiddleJunior
Diverse
experience
levels are
important for
success

36
Know
+What…
+When….
Understand
+How….
Optimize Process
+Do it better
+Grow the
market
+Increase sales
Organization’s Analytical Evolution
If organization
knows and
understands, there
is no limit to
improve in making
better business
decisions
©2015 Data Means

Alejandro Jaramillo
732-371-9512
Alexj@datameans.com
37

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