The document outlines the significant financial impact of maintaining clean data versus the cost of bad records over three years, highlighting that bad data can lead to losses of millions annually. With a proper cleaning strategy, organizations can reduce costs and potentially increase revenue, as clean data can yield up to 70% more revenue. The analysis suggests that by spending on data cleaning, companies not only save money but also enhance their overall revenue generation capabilities.

1. 2015
2016
2017
$2,800,000
$3,920,000
Cost of
Bad Records
$2,000,000
2015
2016
2017
112,000
275,000
Number
of Records
100,000
2015
2016
2017
0
0
Number of
Bad Records
20,000
2015
2016
2017
$0 (possible cost)
$0 (possible cost)
Cost of
Full Cleaning
$200,000
Cost of
Preventing
Bad Records
BAD DATA IS:
THE COST OF
BAD(AND CLEAN)
DATA
SOURCES:
1 Sirius Decisions - The Impact of Bad Data on Demand Creation
2 Small Business Association 2013 Census
3 Ebiz - Integration on the Edge: Data Explosion & Next-Gen Integration
Clean, protect and enhance
your data.
Duplicate
data
Missing
information
Inaccurate
information
Outdated
data
THE COST OF A CRM RECORD
COST OF BAD DATA
THE COST SAVINGS
Prevent
$1 $10
$100
Correct Do Nothing
$100
$80
$60
$40
$20
$0
DEFINING $100 PER BAD RECORD
• Printing and mailing to bad addresses
• Emailing to wrong addresses
• Losing disgruntled customers
• Taking up extra server space with duplicate records
• Sales conflict over the same leads
• Inability to track lead source
• Incorrect marketing segmentation and personalization
• Unnecessary marketing automation & CRM costs for
duplicate records
On average, corporate data grows
at 40% per year.3
Approximately 20% of the average
database is dirty.1
Keeping the $100 per dirty record in mind, and using a 100,000-record
database, here is the astronomical cost of bad data over three years.
There is a tremendous cost savings by
cleaning your data and keeping your
data clean for the long term.
Instead of losing $2 million in 2015,
you’re spending 10% of that. In 2016,
instead of spending $2.8 million,
you’re spending.3%.
Cleaning your data will have a cost of $10 per record
upfront, and keeping it clean is $1 per record.
While your database continues to grow 40% YoY, your
database size goes down with the cleaning (merging
duplicates, archiving inaccurate data, etc.).
Therefore, if you remove 20,000 of 100,000 records in
the first year, then you’re at 80,000 records. With 40%
growth in 2015, you’re at 112,000, and so on.
A strong organization can generate up to 70%1
more revenue than an average organization purely
based on the quality of its data.
The average SMB has $35 million revenue.2
Let’s calculate your new revenue with clean data.
and much, much more.
The $100 per bad record is attributed to impacts such as:
THE COST OF CLEAN DATA
MAKING MONEY WITH CLEAN DATA
2015
2016
2017
$10,000
(covered above)
$15,000
2015 2016 2017
$200,000
$10,000
$15,000
Cost of Cleaning
Bad Records
2015 2016 2017
$2,000,000
$2,800,000
$3,920,000
2015 2016 2017
$1,800,000
$2,790,000
$3,905,000
Annual
SAVINGS
$8,495,000
Cost of
Bad Records
+$35,000,000
Annual Revenue +
$24,500,000
70% more revenue
from clean data
$61,300,000
New Annual Revenue
$1,800,000
Savings from 1
year of clean data
=
3 YEARS
TOTAL
SAVINGS
Number of Bad Records Number of Clean Records
2016 112,00028,000
2017 39,200 1,56,000
2015 80,00020,000