‘So what?’: 3 reasons why businesses don’t buy-in to data quality initiatives
James Blackburn
By James Blackburn

It’s hard to question the importance of data. Permeating every corridor of modern life, data is fundamental, especially to the efficient functioning of business processes. Without it, we could not transact with our suppliers, reach our customers, improve our processes or evolve our products.


Despite this, data governance practitioners, whose job it is to govern and control operational data processes and ensure these yield a high standard of data quality, have a hard time gaining buy-in for their initiatives and demonstrating the value of their results. Like other projects, data governance initiatives typically require business sponsorship to achieve the capital and resource allocation required to make a difference. The problem for advocates of data governance initiatives is that the relationship between data quality and tangibles, such as operational costs or turnover is not straightforward.

 So why do data governance professionals find this problem so difficult to solve?

1.   The impact of poor data quality is often removed from the core business operations

Take the example of an engineer performing scheduled maintenance operations on a piece of machinery and recording this on a hand-held device. The engineer may perform the operation successfully but record incomplete or incorrect information on the hand-held device.

In this scenario, the impact of poor data quality is felt by those processes downstream of the core business operation. For example, on those trying to produce accurate reporting on the maintenance operations and on those trying to demonstrate compliance and good practice to regulators and auditors.

2.   Poor data quality often reflects  risk which is not always materialised

Take the example of a saleable good with an incorrect or missing commodity code. In this scenario, the organisation may never export the material or, when it does, the error may never be discovered by customs.

In this scenario, though there are clear business risks such as regulatory fines, destruction of goods and supply chain delays, the business may not be impacted by any of these at the time.

3.   The impact of poor data may be felt amongst disparate groups of stakeholders

Take the example of a VAT number held against a vendor master record. As VAT numbers are typically provided on a vendor invoice, why should it be important to hold it on the vendor master record. In this situation there may be no single compelling reason but, as suggested below, a series of reasons, which adds up to a compelling case for storing accurate VAT numbers in the vendor master record.

  • For some countries and billing agreements, the tax authority requires that the vendor’s VAT number (or equivalent) is held on file.
  • In migrating data from legacy systems the VAT number is used to avoid duplicating vendors in the productive system.
  • In BAU activities the VAT number is used to identify duplicates in the productive system.
  • In automated invoice scanning, the software may use VAT number to confirm the vendor’s identify as part of an automated invoice payment process.

Companies that need to improve the quality of their data must keep in mind that many different stakeholders need to commit fully to the initiative. Data governance practitioners must focus on the benefits it brings in terms of better decision-making, identifying new growth opportunities, as well spotting inefficiencies. Analysts suggest that on average data management projects can save a business 5 times what it spends over the following 5-year period. The more evidence that can be presented as part of a business case will make it hard for stakeholders and budget holders to ignore.

For more information on how to initiate a successful data quality programme check out James' latest ebook:How to initiate a successful data quality programme  


James Blackburn
By James Blackburn

James is a Principal Consultant with a specialism in Enterprise Information Management and Business Intelligence. He is passionate about helping companies improve performance via data-driven solutions and has recently lead a series of data quality projects across a number of global companies.

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