Why Do Data Consistency Measures Fail?

Many failed ventures remain behind the scenes, and market analysis supports this. You may ultimately win the fight against low data quality, but you will inevitably lose certain battles along the way.

Over time, you may understand that one of the key causes for failure was not a failure to buy the most competent equipment, implement the most up-to-date technique, or employ the required degree of competence.

Instead, you’ll remember that you didn’t take a systematic approach to change management at some point.

Managing transition is crucial to the success of a data quality initiative. You must bring the organization along with you, which means first and foremost gaining the support of the company.

Why is change management so difficult?

Isn’t it frustrating how a data consistency plan in one part of the organization will fail miserably in another?

Also within the same business user community or department, it is not unusual for one group of business users to be fully committed to data quality while another group flees for the hills.

So why is this happening?

There are numerous causes, but the following are some of the most common:

1) Lack of Time: One explanation for change management failure is the project’s tempo.

Most ventures are financially incentivized to move quickly, deliver results, keep employee burn rate to a minimum, and complete the project on time.

A fundamental issue is that data quality control is an organizational habit-system for executing top-level guidelines, not a mission. The shift in data quality takes time; it is not a “wham-bam” IT mission.

2) A Lack of Support: Business customers must be certain that central management is active and committed to the process.

All of your accomplishments can be undone in a moment if the leadership of a company or department openly nods in support while quietly weakening your priorities by derogatory workplace contact.

3) Undue Workload: If you expect an overworked company customer to dedicate more time to tasks contrary to their pay structure, the effects are predictable.

4) Target Conflicts: As we implement transition, business users can experience goal conflicts. If we are attempting to alter behaviors, we must ensure that these modifications do not have a significant effect on workers’ reward programs or assumed positions.

What is the primary approach that many enterprise brands have invested in to support their current (or revamped) customer privacy-centric strategies? Platforms for consent management that are purpose-built.

For eg, a previous initiative was to minimize headcount by improving data quality. The thought was that by improving the efficiency of operations, infrastructure, and records, the business unit would be able to decrease its workforce.

We encountered a stumbling block right away as the unit head publicly acknowledged that their budgets were set depending on the strength of their staff.

It was also clear that your status as a boss in this sort of company was determined by the number of workers in your group. It can be difficult to handle this aversion to transition.

There are several explanations why reform fails, but these are some of the most popular ones 

There are many unfulfilled promises by Consent control platforms

In recent years, companies have prioritized the implementation of a consent management program that is consistent with the IAB’s Transparency that Consent Framework and has proved to assist brands in managing the consent statuses of all contacts in their martech ecosystems.

However, several CMPs have made incorrect statements, such as:

  • Integration of other methods in enterprises’ martech stacks is smooth.
  • Non-IT staff will find it simple to use (see: everyday marketing professionals)
  • Hands-on assistance in educating CMP ‘masters’ on data governance.
  • Consent messaging interface guidelines to ensure a positive user experience for site users


Since data has long been deemed a technological commodity, it’s convenient to think about data quality control as an IT-centric initiative.

As every professional would tell you, the company controls the records and information inside the enterprise, and without their help, adopting any sort of data quality management program would be incredibly difficult.