Creating a Business Case for Big Data
- 1. Big Data Architectural Series:
Creating a Business Case for Big Data
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- 2. About Perficient
Perficient is a leading information technology consulting firm serving clients throughout
North America.
We help clients implement business-driven technology solutions that integrate business
processes, improve worker productivity, increase customer loyalty and create a more agile
enterprise to better respond to new business opportunities.
- 3. • Founded in 1997
• Public, NASDAQ: PRFT
• 2013 revenue ~$373 million
Perficient Profile
• Major market locations throughout North America
• Allentown, Atlanta, Boston, Charlotte, Chicago, Cincinnati,
Columbus, Dallas, Denver, Detroit, Fairfax, Houston,
Indianapolis, Minneapolis, New York City, Northern
California, Oxford (UK), Philadelphia, Southern California,
St. Louis, Toronto, Washington, D.C.
• Global delivery centers in China, Europe and India
• >2,200 colleagues
• Dedicated solution practices
• ~85% repeat business rate
• Alliance partnerships with major technology vendors
• Multiple vendor/industry technology and growth awards
- 4. BUSINESS SOLUTIONS
Business Intelligence
Business Process Management
Customer Experience and CRM
Enterprise Performance Management
Enterprise Resource Planning
Experience Design (XD)
Management Consulting
Our Solutions Expertise
TECHNOLOGY SOLUTIONS
Business Integration/SOA
Cloud Services
Commerce
Content Management
Custom Application Development
Education
Information Management
Mobile Platforms
Platform Integration
Portal & Social
- 5. Our Speaker
Bill Busch
Sr. Solutions Architect, Enterprise Information Solutions, Perficient
• Leads Perficient's enterprise data practice
• Specializes in business-enabling BI solutions that enable the agile
enterprise
• Responsible for executive data strategy, roadmap development, and
the delivery of high-impact solutions that enable organizations to
leverage enterprise data
• Bill has over 15 years of experience in executive leadership, business
intelligence, data warehousing, data governance, master data
management, information/data architecture and analytics
5
- 6. Agenda
Two Perspectives
Big Data Business Case
Challenges
Four Keys To Big Data
Business Case Building
Do’s and Don’ts
6
- 7. Big Data Business Case
Two Perspectives
7
Big Data
Information
Technology
Infrastructure
Data Integration
Data Hub
Data Lake
Appliances
Clusters
Data Warehouse
Cost Out
Business
Stakeholders
Analytics
New Product Design
Customer Experience
Operations
Optimization
Reduce Fraud
Data Monetization
Increased
Profitability
- 8. 8
Optimizing
Single
Process
Traditional Analytics
Business Big Data Focus
Business Case Challenge
Optimizing
Business
Outcomes
Big Data Analytics
- 9. Which business
process should I
use for the business
case?
9
Business Big Data Focus
Business Case Challenge
Customer Experience Cycle
- 10. 10
Big Data
Analytics
Business Big Data Focus
Business Case Challenge
Decision
Making
Brand
Management
New Products
& Services
Customer
Experience
Reducing
Operating
Costs
Optimize
Supply
Operations
Pricing
Optimization
- 11. IT Big Data Focus
Business Case Challenge
11
Infrastructure modernization projects
can be expensive
Re-platform legacy systems
Adjunct systems add to Op-Ex
Support an additional system
- 12. Address
the Who
Layer the
Benefits
Link to
Strategy
Determine
the
Approach
12
Four Keys
Building a Data Business Case
- 13. Address
the Who
Layer the
Benefits
Link to
Strategy
Determine
the
Approach
13
Four Keys
Determine the Approach
- 14. Business or IT Focus?
14
Executive Summary
Introduction
Background
Business Drivers
Scope
Key Financial Metrics
Analysis
Assumptions
Business Process Changes
Costs
Benefits
Financial
Non-Financial
Cash Flow Statement (NPV)
Risk
Strategic Options
Opportunity Costs
Conclusion & Recommendation,
and Next Steps
Appendix – Supporting Information
Business Focused
-Quantify Benefits
IT Focus
-Quantify Costs and Compare
Options
- 16. Direct Evidence
Positives
Challenges Description
Big Data Application
16
Proof from an
experiment or actual
implementation
resulting in the claimed
benefit
Typically pilot or test &
learn project
With Big Data, direct
evidence is the result of
a business-focused
proof-of-concept
o Most convincing
o Opportunity to
become familiarly
with Big Data
Technologies
o Can be used to
“test” which
business cases to
pursue
o May need a
business case to get
to a pilot
o If the business case
is not proven,
program may be at
risk
- 17. Positives
Challenges Description
Initiative Optimization
Big Data Application
17
This business case type
leverages a
transformational
program to fund
optimization activities
driven by analytics.
Most common funding
mechanism. Include
description how the
analytical capability
supports the program’s
goals. Consider
presenting Big Data as
an option compared to
traditional solutions.
o Easiest to create
o Does not require a
separate benefits
analysis
o Good way to fund
architecture
modernization
o May require a
business case if
added to scope
o Internal competition
for funding
o Corporate culture
will determine the
success of this
approach
- 18. Positives
Challenges Description
Business Process Improvement
Big Data Application
18
Leverages improvement
in one or more business
process without the
benefit of piggy-backing
on a transformational
project
Can require significant
work since more than
one business case will
need to be analyzed
o Best used to support
the Big Data
Analytics initiative
o Information is readily
available
o May require a
business case if
added to scope after
program
commences
o Funding may be put
at risk if program
goes over budget
- 19. Positives
Challenges Description
Corporate Asset
Big Data Application
19
Approaches the
business case that the
company requires Big
Data Analytics as a
means to either remain
competitive or lead its
industry.
Numerous industry
benchmarks exist on
the value of analytics.
These can be used to
build a financial
business case for Big
Data.
o Best used to support
the Big Data
Analytics initiative
o Information is readily
available
o Generally the
hardest to defend
o Can result in un-realistic
numbers
o Requires executive
support or directive
- 20. Corporate Asset
Bottom-Line Business Case
20
On average, 6 Data
Scientists will generate
$10M/year ROI from
analyzing Big Data*
*Intel Corporation
- 21. Address
the Who
Layer the
Benefits
Link to
Strategy
Determine
the
Approach
21
Four Keys
Link to Strategy
- 22. 22
Big Data
Analytics
Decision
Making
Business Case
Quick Review
Brand
Management
New Products
& Services
Customer
Experience
Reducing
Operating
Costs
Optimize
Supply
Operations
Pricing
Optimization
- 23. Linking to Corporate Strategy
23
Chose benefits that relate to the corporate strategy.
Consider using combination of Financial and Non-Financial metrics.
Strategic
Imperatives
- 24. 24
Linking to Corporate Strategy
Strategic Imperatives Actions Requiring Decisions
Increase Gross Margin
Reduce Fuel Expense
Meet Website SLAs
Improve Customer
Satisfaction
Resolve Customer
Complaints more
Quickly
Run Maintenance cycle on
Diesel Generators
Frequency & Length of idle
cycle
Analytical Contribution
Identify and prioritize actions
Prescribe prioritized corrective
actions
Model asset optimization
capability
System-wide understanding of
causal factors
Performance / health within time
period
Assign a confidence factor to
predictions
Identify casual factors impacting
equipment life
Early detection of equipment
failure
Supply-chain optimization
Better root-cause identification
Minimize decision time
- 25. Tell the Story
Data Scientists in the
Network Operations
Department will use the Big
Data Analytics capability to
analyze generator run logs,
weather data, fuel costs to
identify, prioritize actions to
reduce diesel generator idle
time. These actions will
reduce fuel costs and
increasing gross margin.
Furthermore, this will reduce
our carbon footprint and
enhance our brand image as a
socially responsible
company.
25
- 26. Quantifying Benefits
26
Most challenging
portion of developing
business case
Not all benefits are
quantifiable
Both financial and
non-financial can be
quantified
PoC may be required
to either quantify or
validate benefits
- 27. 27
Current Value
(CVal)
Coefficient of
Improvement
(Cof)
Quantifying Benefits
Simplified Model
Future State
Benefit
(FVal)
- 28. 28
Quantifying Benefits
Sum of Decisions
Current Value
(CVal)
Coefficient of
Improvement
(Cof)
Future State
Benefit
(FVal)
n(CVal)(Cof) = FVal
(CValn)(Cofn) = FVal
Same
Coefficient
Different
Coefficients
- 29. Quantifying Benefits
Determining Coefficients
• What: Leveraging Industry research to establish coefficients
required to calculate a financial benefit
• Most common and easiest to understand Industry
Research
• What: Leveraging the opinion of subject matter experts with
unique knowledge to provide the coefficients required to
calculate a benefit
• A Delphi method can be used to utilize the opinion of many
experts for a more accurate/substantiated result
29
Expert
Estimation
• What: Evaluating a group of like decisions before and after a
process change and identifying the standard error before the
and after a process change. The reduction of the standard
deviation is then quantified to arrive at a financial benefit.
• Hardest to understand
Decision
Risk
Valuation
- 30. Business Case
Solving the “Which Business Case” Problem
Which business
process should I
use for the business
case?
30
Customer Experience Cycle
- 31. 31
Big Data
Analytics
Business Big Data Focus
Business Case Challenge
Decision
Making
Which
Business
Process
How to
Optimize
- 32. )
32
Quantifying Benefits
Average Benefits of Many Use Cases
Current Value
(CVal)
Coefficient of
Improvement
(Cof)
Future State
Benefit
(FVal)
AVG ((CValn)(Cofn) = FVal
- 33. Address
the Who
Layer the
Benefits
Link to
Strategy
Determine
the
Approach
33
Four Keys
Layer the Benefits
- 34. Layering Benefits
Increasing Business Case Impact
34
Use
Additional
Approaches
Evaluate
Additional
Use-Cases
Include
Additional
Benefits
- 36. 36
Additional Use Cases
Sum of Benefits
Current Value
(CVal)
Coefficient of
Improvement
(Cof)
Future State
Benefit
(FVal)
(CValn)(Cofn) = FVal
- 37. Tell the Story
Example
Data Scientists in the Network
Operations Department will
use the Big Data Analytics
capability to analyze generator
run logs, weather data, fuel
costs to identify, prioritize
actions to reduce diesel
generator idle time. These
actions will reduce fuel costs
and increasing gross margin.
Furthermore, this will reduce
our carbon footprint and
enhance our brand image as
a socially responsible
company.
37
- 38. Four Keys
Address the Who
Address
the Who
Layer the
Benefits
Link to
Strategy
Determine
the
Approach
38
- 39. Bottom-Line Corporate Asset
39
On average, 6 Data
Scientists will generate
$10M/year ROI from
analyzing Big Data*
*Intel Corporation
- 40. Addressing the Who
Leveraging Big Data
Analytics requires Data
Scientists to use the
system
Companies may not have
enough Data Scientists
on staff to obtain the
purported benefits
Include staffing and
training Data Scientist in
the business case
Each Data Scientist can
generate $1.6Million in
annualized ROI.
If a business case has a
$20Million annual ROI this
suggests that 12 Data
Scientists are required.
40
- 41. Final Thoughts
Do’s
• Chose the appropriate approach(s)
• Tell how solution supports the corporate
strategy
• Quantify benefits in dollars where
possible
• Use the business case to setup the PoC
• Include Data Scientist staffing and
training in business case costs
Don’ts
• Select an infrastructure only use case
• Create a build it and they will approach
• Single-use case business case
• Use an inappropriate level of detail for
the company culture
41
- 43. As a reminder, please submit your
questions in the chat box.
We will get to as many as possible.
11/5/2014
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