Analytics ROI Best Practices

Copyright © 2022 McKnight Consulting Group, LLC All Rights Reserved Slide 1
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Analytics ROI Best Practices

1 | © 2022 Alation, Inc. – All Rights Reserved.
Data Intelligence + Human Brilliance™
Self Service BI in Support
of Advanced Analytics

Have You Had This Problem?
● You have a big initiative
᠆ Digitizing and personalizing a traditional grocery
store flyer that is schedule to drop in four days
● To do this, you need to:
᠆ Find the right customer data
᠆ Understand and trust the algorithms
᠆ Maximize the contribution of an off-shore team
with speed and collaboration
● The clock was ticking and you have no idea
where to start
● You are told the data could be found in “RTPP”
in Teradata.
● You don’t even know what RTTP stands for

● So the data scientist leader typed RTTP into the data catalog and
searched for the term
● They quickly understand the meaning and associated data definitions
● They also were able to get related tables and queries, and
information on table columns, common filters and joins on the table
● Next they needed to simplify the process for the data analysts in
California to pass their work to their peers in the Philippines and
vice versa
● This enabled the project to be worked 16 hours a day
● Conversations were done in the catalog rather than email so all
project information was right there next to the article and connected
to the data
● The conversations and articles also become part of the metadata
which is searchable so there is no need to reinvent the wheel
How Catalog Solved the Problem

Data Intelligence + Human Brilliance™ 5 | © 2022 Alation, Inc. – All Rights Reserved.
“80% of analysts’ time
is spent simply discovering
and preparing data.”
What’s Your Data Strategy, Thomas Davenport, HBR 2017

● A survey by Crowdflower found that 19%
of a data scientist time was spent
collecting data sets and 60% of their time
was spent cleaning and organizing data
● Kirk Boone asked whether data scientists
could be better called data plumbers
Why Making Data Better Matters

Shorter time to value
Lower business costs
Business agility
Faster/better decision making + innovation
Transformation at scale + Data driven processes
Business Outcomes From Self Serve

Fix End to
End Data
Value Chain
Principles to Creating Effective Self Service
Make Data
Easier
to Acquire
Quicken
Determination
of Data
Trustworthiness
Focus Data
Engineers on
Creating New
Data Assets

Self Service BI Requirements
Business Requirements
• Data is accessible without an analytics
tool or a programmer
• Data discovered is current and ready
• Business analysts and data engineers
aren’t overwhelmed
• Inquiries for new reports, analysis, and
data models
• Good, bad. or ungoverned data is
known
How Alation Delivers
• Natural language makes data
available to everyone regardless of
data literacy
• Behavioral Analysis Engine surfaces
data popularity and usage
• TrustCheck delivers data health flags
so users avoid data misuse and
compliance
• Pre-built connectors enable access to
data with minimum configuration
• Integration to data quality tools +
collaboration allows users to self
assess data trustworthiness

Presentation Objectives
At the end of this hour, you should be able to
Navigate an analytic justification
Distinguish between tangible and intangible benefits
Present an itemized ROI
Articulate the value of analytics
Adapt a methodology that includes ROI attainment
and measurement

ARCHITECTURE BUSINESS
Sample Analytic Projects Justified
Healthcare
• Claims Routing
• $45M
Financial
• Loan Origination
• $72M
Pharmaceutical:
• Offer Analysis
• $35M
Telecommunications:
• Full CDR Analysis
• $22M
Retail:
• Offer Optimization
• $35M

Analytics Defined
Analytics is the process of utilizing data to enhance
business processes.
Analytics is deeper than simple knowledge; it has depth.
There’s Analytic Projects and…
There’s Analytics Added to projects

Measuring is Important to Business
“The essence of a corporate culture is the firm’s
measurement system. It is the lens through
which reality is perceived and acted on.”
Paul A. Strassman, “The Business Value of Computers, 1990”

Acknowledging the Strategic
A Place for Learning and Innovation
The Unknown Upside
The intuitive thinking employed by HiPPOs
(highest-paid person’s opinions)

Workloads

Prioritizing Efforts
Ease to Do
Prerequisites First
ROI

Ordered Benefits: What Makes it
Difficult
Benefits can be direct or indirect
First order benefits have a direct relationship to the
bottom line
Second order benefits have an indirect relationship
The system will enable an activity that in turn provides the
benefit
Third order benefits have a transitive relationship
The system enables an activity that allows performance of
another activity which actually provides the benefit

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Types of Justification:
Program vs. Project

Analytics Programs and Projects
Justifying an Analytics Project
Analytics seen as a stand alone project
Built for one application
Justifying an Analytics Program
Analytics store enterprise data
Inclusion of data is based on governance
The goal of the program is to enable the component of
the applications it supports

What is being justified?
An information management program which will
store data for several projects
Why use a data warehouse architecture versus
independent data marts?
A project which will use a data store to store
data
Why do this project?
The inclusion of new projects into an existing
data store program
Why architect this project into the data store instead of
building an independent data store?

Return on Investment
(Returns - Investment)/Investment
ROI should always be supported with a time
period (i.e. 326% return in 3 years)
ROI should be presented with assumptions and
risks and be itemized
By source system, subject area, business problem
solved, users, levels of summary, and/or amount of
history data
Add the possibilities! But don’t oversell
Used for Predicting and Measuring

Self-Service
It’s not just TCO
Users have limited window to “analyze” which
dictates depth of analysis
Depth is based on
Quality of architecture
Degree of self-service enablement

Typical Approach by Domain
Domain Approach
Data Warehouse ROI or TCO
Data Lake ROI or TCO
Master Data Management TCO foremost
Analytics ROI
Customer Relationship Management ROI or TCO
Data Integration TCO

TCO Examples
Is the Cloud Worth It?
Should I do a data mesh?
What should I use for Storage Layer/Platform?
Should I use a Big Data (Cloud Storage, NoSQL)
solution or RDBMS?
Is Master Data Management Worth It?

Variations on the ROI Theme
Payback Period Analysis
Return on Investment
Net Present Value
Internal Rate of Return

Presenting the ROI Possibilities
For each suggested business project, present at
least 3 possible scenarios along with the odds of
that scenario happening, forming a probability
distribution
Best Case
Little Goes Wrong
Worst Case
Most everything goes
wrong
Planned Case

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Tangible vs. Intangible Metrics

Tangible versus Intangible Returns
Tangible Returns - Returns you decide to
measure
More activities have a measurable return than you
may think
Usually 1-2 returns are reasonable to measure for
each project
Intangible Returns - Returns you decide not to
measure
These will not justify analytics efforts

Intangible Returns… Keep Going!
Clean data
“Single version of the
truth”
Improved customer
satisfaction
Enhanced corporate
image
More time for staff
development
Faster response to
customers
Get a “data mesh”

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Return on Investment Examples

Tangible Returns
Increase in sales
Efficiencies in processes
Reduction in inventory
Reduction in returns
Reduction in fraud
Meet Service Level Agreements

How Analytics Increases Sales
Predict and prepare for likely sales
Identify customer-product likelihood to purchase
Retain customers trending to attrition
Support customer engagement activities
Segment the customer base actionably
Optimize pricing

Tangible Returns
Procurement savings
Savings in material costs for processing
Savings from volume purchasing
Predictive replacement
Avoiding regulatory fines
Maintenance savings based on actual usage
Rightsize costs to demand

Other Examples
Increased revenue per customer
Increased customer acquisition
Reduced cost of marketing campaigns
Decrease in customer attrition
Improved employee productivity
Reduced IT spend
Reduced cost to recover from a breach
Improved efficiency in fraud management
Reduced cost of infrastructure

Healthcare Example
COST REDUCTION Year 1 Year 2 Year 3
Claims Saved 0 100 150
Avg Cost/Claim $25,000 $25,000 $25,000
Total Claim Cost Savings - $2,500,000 $3,750,000
Total Impact - $3,125,000 $4,500,000
REVENUE GENERATING Year 1 Year 2 Year 3
# Customers added or retained 0 25 30
Average Customer Premium $125,000 $125,000 $125,000
% Gross Profit 20% 20% 20%
Total Customer Benefits - $625,000 $750,000

Abstracted ROI
Impossibly measurable at the individual level
Must measure at large group impact level
Like Customer Lifetime Value/Spend Decile
Use indirect measure like NPS

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Summary

ROI Methodology Summary
Cost Analysis
Tangible Benefits Analysis
Cash Flow Analysis
Probability Distribution
Intangible Benefits Analysis
Prioritization and Planning Decisions

Presentation Summary
Target the business deliverable of the project
Use a repeatable, consistent process using governance for project
justification
ROI is an important component of justification, but not the only one
Use lower TCO for Program Justification
Isolate project benefits and costs
If it’s important, you can measure it and you can improve it, the
project will almost always pay for itself
Judgment is essential

Analytics ROI Best Practices
Presented by:
William McKnight
President
McKnight Consulting Group
(214) 514-1444
wmcknight@mcknightcg.com
www.mcknightcg.com
William McKnight
The Savvy Manager’s Guide
The
Savvy
Manager’s
Guide
Information
Management
Information Management
Strategies for Gaining a
Competitive Advantage with Data

Analytics ROI Best Practices

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