Measuring Data Quality Return on Investment

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Unlock Potential
William McKnight
President
McKnight Consulting Group
(214) 514-1444
wmcknight@mcknightcg.com
www.mcknightcg.com
@williammcknight
Measuring Data Quality Return on
Investment

Today’s Agenda
How to Define the Quality Expectations
How to Profile Data Against the Defined Expectations
How to Measure Data Quality Impact Across Various Thresholds
How to Improve Quality of Data and Improve the Business
47% of data records had at least one critical error. HBR study 2020.

Theme
• Data Quality is Essential to Business Success
» “Correct” data is a widespread need
• Yet, data quality lacks consistent definition
» You can’t improve what you can’t measure
» Tangible benefits accrue from improved efficacy of the
applications using the data
• To realize value
» define the quality expectations, profile data against the
defined expectations, measure data quality improvement
across various thresholds and improve the quality of data and
improve the business

Data Quality
► A lack of intolerable defects in the data

Agenda
Define the Quality Expectations
Profile Data Against the Defined Expectations
Measure Data Quality Impact Across Various
Thresholds
Improve Quality of Data and Improve the
Business

Consider these business imperatives
Information-based in-store and call center cross- and up-selling
(NEEDS clean customer and product data)
Credit card fraud detection (NEEDS clean customer and
transaction data)
Supply chain efficiencies and just-in-time production capabilities
(NEEDS clean product and location data)
Predictive churn management (NEEDS clean customer and
transaction data)
Many others
These have failed or underperformed because
of incomplete, incorrect, inconsistent data

Investments in Data Quality
Investments Yield “Cleaner” Data
Business objectives cannot be met without
quality data in support
Data Quality Returns are in the improved
efficacy of projects targeting business
objectives
Data Quality should be an integral part of most
projects

Iterate Data Observability Throughout
the Environment
ERP App Call Center App DW Analytical App
CRM
0 3 mos. 6 mos. 9 mos. 12 mos. 15 mos.

Data Quality Rule Categories: 1
Category Example
Referential integrity Order is placed for customer # 123, who exists in
the customer table
Uniqueness Only one customer exists with customer # 123
Cardinality We may expect to find between one and three
addresses for a customer – no more and no less
Subtype/supertype
constructs
A customer can be divorced or not, but only
divorced customers have a value in the ex-spouse
name field
Value reasonability Last-year purchases should not be > $500,000 or < 0

Data Quality Rule Categories: 2
Consistent Value Sets In a Name field, we would not expect to find certain
characters such as % and $
Formatting No period after middle initial
Data derivation While one system may contain discount amount and
unit price, the discount percentage, a simple
division calculation, could be a unrelated calculation
Completeness Sales data covers each day from beginning to
current date
Correctness William mcnight instead of William McKnight
Conformance to a clean
set of values
In the gender column we would expect to find M, F,
U (unknown)

Agenda
Thresholds
Business

Data Profiling Example
Gender Occurrences Overall Pct.
M 42855 43
F 44583 45
U 5986 6
1 4070 4
2 1264 1
Y 986 1
X 254 0
! 1 0

Data Quality Scoring
Scoring defines how well your data meets
business expectations
Adherence
Possibilities
Valid values M, F, U:
43%+45%+8%=94%
i.e., for
Gender:
Multiple rules used to determine overall
system score

Agenda
Thresholds
Business

1. Fewer bad contact data
2. Improved customer segmentation
3. Highest Marketing Initiative ROI reached
4. Cleaner data for other applications
How can Improved DQ Improve Initiatives?
Example: Targeted Marketing

Cost of Poor Data Quality
People X average pay rate for people doing DQ work today
According to research by Sirius Decisions, between 10 percent and 25
percent of B2B marketing database contacts contain critical errors
Records X 20% X $100 = $ Millions/year opportunity
$100 per data record attributed to:
Printing and mailing, emailing bad addresses
Losing disgruntled customers
Taking up extra space with duplicate records
Incorrect marketing segmentation and personalization
Marketing and CRM for duplicate records
On average corporate data grows at 40% per year

Case Example: Office Products
Retailer Marketing to Prospects in
Database
DQ
Score
Prospects
Reached
Return on
Marketing
Avg.
profit
Return
(prospects X
ROM X avg.
profit)
investment
*
ROI (return-
investment/
investment) *
90 110000 0.04 250 1100000 400000 175.00%
85 105000 0.03 250 787500 390000 101.92%
80 100000 0.025 250 625000 380000 64.47%
*considering only outreach costs, not considering DQ improvements
and better targeting
Higher scores means
more contact gets through

Case Example: Where was the DQ
investment to go?
Reverse-append existing customers for
addresses
Reverse-append existing customers for
demographics
Purchase additional prospects
Use of multiple and different third-party data
providers (and corresponding de-duplication of
results)

Agenda
Thresholds
Business

Data Quality Improvement
Four Actions to Perform for Data Quality
Screen Data Entry
Quarantine Data to Prevent Improper Use
Report on Quality Violations to Raise
Awareness
Change or Repair Incorrect Data to Conform,
when possible

Data Collection Systems
Exactly once versus At Least Once
At Least Once
Guarantees Order of Delivery
Apache Kafka/Amazon MSK
Kinesis Data Streams
Does not Guarantee Order of Delivery
SQS in Standard Mode
Kinesis Data Firehose
Exactly Once with Guaranteed Order
SQS in FIFO Mode
Dynamo DB Streams

Cost/Benefit of Adding Data Quality
Efforts
DQ Score investment *
ROI (return-
investment/
investment) *
additional
cost to
improve DQ
to the level
Revised Project ROI
(return-
(investment+DQ
cost)/ investment+
DQ cost)
99 410000 198.02% 300000 72.10%
90 400000 175.00% 200000 83.33%
85 390000 101.92% 75000 69.35%
80** 380000 64.47% 0 64.47%
*without considering costing for DQ improvements **Default score – will happen without data quality improvements
Best
Value
Proposition

Summary of the Steps to
Data Quality Success
1. Define the Quality Expectations
2. Profile Data Against Defined Expectations
3. Measure Data Quality Impact Across Various
Thresholds
4. Improve Quality of Data and Improve the
Business

Recommendations
Data quality can and should have a value proposition
Data quality is never an accident
Consider data quality when considering applications
Measure the level of your data quality
Data quality is a business-driven imperative
Business: definition of quality rules
Business: validation of quality scores
Business: validation of quality actions

Measuring Data Quality Return on
Investment
Presented by:
William McKnight
President
McKnight Consulting Group LLC
(214) 514-1444
wmcknight@mcknightcg.com
www.mcknightcg.com
Twitter @williammcknight

Measuring Data Quality Return on Investment

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