Creating a Business Case for Big Data

Big Data Architectural Series:

Creating a Business Case for Big Data

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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.

About Perficient

• Founded in 1997

• Public, NASDAQ: PRFT

• 2013 revenue ~$373 million

• 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

Perficient Profile

BUSINESS SOLUTIONS

Business Intelligence

Business Process Management

Customer Experience and CRM

Enterprise Performance Management

Enterprise Resource Planning

Experience Design (XD)

Management Consulting

TECHNOLOGY SOLUTIONS

Business Integration/SOA

Cloud Services

Commerce

Content Management

Custom Application Development

Education

Information Management

Mobile Platforms

Platform Integration

Portal & Social

Our Solutions Expertise

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

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Agenda

Two Perspectives

Big Data Business Case

Challenges

Four Keys To Big Data

Business Case Building

Do’s and Don’ts

6

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

New Product Design

Customer Experience

Operations

Optimization

Reduce Fraud

Data Monetization

Analytics

Increased

Profitability

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Optimizing

Single

Process

Traditional Analytics

Optimizing

Business

Outcomes

Big Data Analytics

Business Big Data Focus

Business Case Challenge

Which business

process should I

use for the business

case?

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Customer Experience Cycle



10

Big Data Analytics

Decision Making

Brand Management

New Products & Services

Customer Experience

Reducing Operating

Costs

Optimize Supply

Operations

Pricing Optimization



IT Big Data Focus


11

Infrastructure modernization projects

can be expensive

Re-platform legacy systems

Adjunct systems add to Op-Ex

Support an additional system

Address the Who

Layer the Benefits

Link to Strategy

Determine the

Approach

12

Four Keys

Building a Data Business Case

Address the Who

Layer the Benefits

Link to Strategy

Determine the

Approach

13

Four Keys

Determine the Approach

Business or IT Focus?

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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

IT Focus-Quantify Costs and Compare

Options

Business Focused-Quantify Benefits

Determine Your Approach

Direct Evidence

Initiative Optimization

Business Outcome

Corporate Asset

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Direct Evidence

Po

sitiv

es

Ch

alle

ng

es

De

scrip

tio

n

Big

Da

ta A

pp

lica

tio

n16

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

Positiv

es

Ch

alle

ng

es

De

scrip

tio

n

Big

Da

ta A

pp

lica

tio

n


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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

Positiv

es

Ch

alle

ng

es

De

scrip

tio

n

Big

Da

ta A

pp

lica

tio

n

Business Process Improvement

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

Positiv

es

Ch

alle

ng

es

De

scrip

tio

n

Big

Da

ta A

pp

lica

tio

n

Corporate Asset

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

Corporate Asset

Bottom-Line Business Case

20

On average, 6 Data

Scientists will generate

$10M/year ROI from

analyzing Big Data*

*Intel Corporation

Address the Who

Layer the Benefits

Link to Strategy

Determine the

Approach

21

Four Keys

Link to Strategy

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Big Data Analytics

Decision Making

Brand Management

New Products & Services

Customer Experience

Reducing Operating

Costs

Optimize Supply

Operations

Pricing Optimization

Business Case

Quick Review

Linking to Corporate Strategy

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Chose benefits that relate to the corporate strategy.

Consider using combination of Financial and Non-Financial metrics.

Strategic

Imperatives

24

Linking to Corporate Strategy

Increase Gross Margin

Improve Customer

Satisfaction

Meet Website SLAs

Reduce Fuel Expense

Resolve Customer

Complaints more

Quickly

Strategic Imperatives Actions Requiring Decisions

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

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 responsiblecompany.

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Quantifying Benefits

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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

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Current Value

(CVal)

Coefficient of

Improvement

(Cof)

Future State

Benefit

(FVal)


Simplified Model

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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


Determining Coefficients

29

• What: Leveraging Industry research to establish coefficients required to calculate a financial benefit

• Most common and easiest to understandIndustry

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

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

Business CaseSolving the “Which Business Case” Problem

Which business

process should I

use for the business

case?

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Customer Experience Cycle

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Big Data Analytics

Decision Making

Which Business Process

How to Optimize



)

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Average Benefits of Many Use Cases

Current Value

(CVal)

Coefficient of

Improvement

(Cof)

Future State

Benefit

(FVal)

(CValn)(Cofn) = FVal AVG (

Address the Who

Layer the Benefits

Link to Strategy

Determine the

Approach

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Four Keys

Layer the Benefits

Layering Benefits

Increasing Business Case Impact

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Use Additional

Approaches

Evaluate Additional Use-Cases

Include Additional Benefits

Different Approach

Direct Evidence


Business Outcome

Corporate Asset

35

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Additional Use Cases

Sum of Benefits

Current Value

(CVal)

Coefficient of

Improvement

(Cof)

Future State

Benefit

(FVal)

(CValn)(Cofn) = FVal

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.

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Four Keys

Address the Who

Address the Who

Layer the Benefits

Link to Strategy

Determine the

Approach

38

Bottom-Line Corporate Asset

39

On average, 6 Data

Scientists will generate

$10M/year ROI from

analyzing Big Data*

*Intel Corporation

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.

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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

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11/5/2014

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Creating a Business Case for Big Data

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