6 Reasons Companies Ignore Data Quality

Issues

Why Do They Do It?

When data quality issues occur, managers may be tempted simply to:

Leverage existing tools to meet the perceived data cleansing needs

Reinforce existing business processes

Educate users in support of good data

These practices on their own will not solve the problem

The following list showcases why businesses have stuck their heads in the proverbial data quality ‘sand’

Reason 1

“We don’t need it. We just need to reinforce the business rules”

Reinforcing business rules and standards won’t prevent all of your problems

Not all data quality errors are attributable to lazy or untrained employees

Consider nicknames, multiple legitimate addresses and variations on foreign spellings

While its good to get your process and team in line, you still have to clean up your mess!

Reason 2

“We already have it. We just need to use it”

Many companies have used up valuable time, funds and resources attempting to create a sufficient data quality solution out of an existing software tool

Stakeholders mistakenly think that data quality tools are inherent in existing applications, or are a modular function that can be added on

Managers with sophisticated CRM or ERP tools in place may find it hard to believe that their expensive investment doesn’t account for data quality

Reason 3

“We have no resources”

When company resources are maxed out (human, IT, financial), the idea of data quality initiatives can seem foolhardy

Most business don’t have a clear approach to their own requirements

Procrastination of data quality issues isn’t the answer!

The time it takes staff to navigate data with inherent problems, can take a serious toll on efficiency

Reason 4

“Nobody cares about data quality”

At times there is only one lone voice advocating for data quality

Find the people who get it!

They can usually be found in the trenches, attempting to work with the data.

They are not empowered to resolve the data quality issues, but they care because it impacts their job

In trials, the matchIT API found 80% more accurate matches to the MPS file than Experian Intact’s Bureau

Reason 5

“It’s in the queue”

There are those businesses that recognize the importance of data quality, but they place it behind larger projects

Data quality should be implemented before records move to a new environment

Garbage in = garbage out

The unfamiliarity of a new system or process often negates the challenge of cleansing data errors that have migrated from the old system

Reason 6

“I can’t justify the cost”

This is one of the biggest data quality challenges

Just because you can’t capture the cost of bad data in a single number doesn’t mean its not affecting your bottom line

If you can’t justify the cost for fixing the problem, try to justify the cost of doing absolutely nothing

Turn your argument around to get things moving in a new direction

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