6 questions to help you quantify the business case for data quality

Whether you’re investing in data governance, prepping for a data migration, or looking to leverage data-driven insights to fuel decision-making, one thing is clear: underpinning the success of your business initiatives is the quality of data collected. Bad data in results in bad data out. Fixing your data issues—and most importantly, the root cause—so you have confidence in your data requires an investment in a permanent, enterprise-wide data quality program to standardize and validate the information in your databases.

But, how do you get executives to view data quality as an integral component of an ongoing data management program? This can often be half the battle. Getting executive buy-in starts with putting a dollar amount on bad data and speaking to the areas they care about most: time, money, resources, process, strategy, and risk.

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Can your data quality issues be linked to wasted time?

As they say, time is money. Consider the time taken to discover data quality issues. Getting to the root cause of data problems often requires a manual analysis of data, which can often take weeks to diagnose, and meanwhile, your data issues can snowball into a critical state. And then there’s the time needed to fix the issue. To quantify time wasted on remediating data quality issues, measure the time taken in inefficient data processes. Can this be reduced by implementing changes?

Do your data quality issues directly impact the resources who work with data?

Fixing data quality issues often requires additional resources and specialist skills. Who is impacted by bad data? Who’s involved in this process of remediating bad data? Is this work taking away from their core responsibilities supporting priority initiatives? Map the resources that are either required to manage data quality or are dependent on reliable and accurate data to correct quality problems.

Do your data quality issues cost the business today? Do they have the potential to do so down the line?

Remediating data quality issues often requires additional costs that were not previously budgeted. How much are you spending on data fixes today? What is the negative impact that current data quality processes (or the lack of) have on your bottom line? Work with your finance department to forecast the estimated annual or long-term impact to the business. This should go a long way in proving ROI by investing in a permanent data quality program.

Do your data quality issues make business processes inefficient? Unachievable?

Poor quality data can slow down or even stop critical business transactions, requiring complex workarounds that make the business inefficient. For example, customer order data that fails to meet requisite data checks can require manual workarounds to understand what is missing and how to fix them reactively. Map data quality issues to your business process, identifying specific quick fixes that are costing the business.

Can your data quality issues prevent the start of or completion of strategic projects the business has planned or already begun?

Inaccurate or incomplete data can result in a failed implementation of business transformation projects that are driven by data. For example, the digitization of the customer experience can be hampered by the inability to link customer records across channels. Map data quality issues that may directly impact the success criteria of any strategic initiative, either directly or indirectly related to data assets held by the business.

Can your data quality issues increase risk to the business or negatively impact brand value?

Poor quality data does not allow users to deliver critical data that is expected by external regulators, resulting in fines due to inadequate or delayed deliverables, such as those incurred in the financial services sector for incomplete information. This increases your regulatory risk and chance of incurring fines. Map data quality issues that may negatively impact the risks to the organization either directly or indirectly related to data assets held by the business.

Putting a dollar amount on bad data and demonstrating its negative impact on the business, including bogging down resources, deterring key initiatives, increasing risk of compliance penalties, and lost business opportunities, is your key to getting buy-in for an ongoing and permanent data quality program.