It is essential that organizations assess the need for improved data quality and make the necessary changes to save not only their reputation but the bottom line.
Incorrect data can interrupt the flow of business. Yes, it’s inconvenient and frustrating, but bad data can go far beyond inconvenience. Bad data quality impacts the bottom line. A study cited in the Harvard Business Review estimated that bad data costs U.S. organizations $3.1 trillion annually (about 6% of the total economy). Just imagine losing 6% of your organization’s turnover due to bad data. But how would we know where those costs are coming from? And how would we identify what that cost is? How confident would you feel in your ability to identify the economic, social, environmental, and reputational consequences of all the incorrect data in your organization?
There are essentially four ways that bad data quality impacts businesses. Let’s dig into those four categories.
1) Process loss day by day
Everyone has a story about how incorrect data caused some painful consequences. While issues such as an incorrect delivery address, an order of a wrong part, or a rogue zero added to an invoice do cause disruption, at least they were caught by somebody. But it’s the phantom challenges, the efficiency losses that can only be identified forensically through the data, that really hurt businesses.
One such example is a company that had a ridiculous number of duplicate customer and vendor records, which were causing serious business issues. These duplicates stemmed from a system that was overly cumbersome to navigate, so much so that the default user behavior was to create a whole new record rather than find the original. This workaround, while reasonable from the user perspective, increased errors in customer/vendor analysis and inconsistent addresses/ contacts/invoicing terms that cost the business significant amounts of money.
Those costs can show up in poor customer satisfaction scores that result in product or subscription cancellations or in a company’s inability to account for discounts in purchasing based on total vendor spend. The numbers are substantial if you know where to look.
See also: 7 Steps for Consistently Delivering High-Quality Data
2) Data disruption and high costs
Digital transformation – and, therefore, data transformation – is a business necessity today. This could take the form of a move to the cloud, a merger or acquisition assimilation, an enterprise resource planning (ERP) consolidation, or an infrastructure upgrade. In these dynamic digital transformation initiatives, data quality and the methodology for migrating data play a pivotal role. The larger and more complex the project, the bigger headache data is likely to be with duplicate records, inconsistent master data harmonization, and data structure incompatibility between source and target systems.
These data-related problems can throw a huge monkey wrench into the most well-intentioned transformation plans. While some organizations with an existing data problem may charge ahead with a data migration or move to the cloud, the pain resulting from bad data will only be delayed. At some point, you have to pay the piper.
Run-on resourcing costs, continued business process disruption, and lost business value are some of the data-caused go-live delays that can cost organizations hundreds of thousands of dollars a day. Then there’s the reputational damage from getting a migration wrong in the public sphere. Unless a comprehensive strategy is in place to manage data from the outset of a transformation, it’s likely businesses will always be playing catch-up and counting the cost of underestimating the data problem.
See also: 6 Rules for Energizing the Data Behind Industrial AI
3) Compliance issues and fines
With regulations such as GDPR in the European Union, or CCPA in the United States, all industries are becoming acquainted with the increased rules on personal information. Breaches in any of these regulations can yield substantial monetary fines, which should be enough to make a business sit up and take notice, but they can also inflict severe reputational damage.
There are multiple ways that data affects compliance with regulations. The first is in terms of the inaccuracy of data, which can lead to accidental breaches and missteps around certain legislation. Second, there’s the inaccessibility of data and the challenge of not knowing where relevant data is stored and subsequently cataloged. And finally, there are issues surrounding the governance of data, which is crucial in understanding potential legal issues in the lineage of data. Failure at any of these data points can lead to fines up to millions of dollars.
4) Decisions made with bad analytics
Only 5% of surveyed C-level executives have a high level of confidence in their data, according to a recent study conducted by HFS Research. In another survey, conducted by Blackline, 70% of business leaders claimed to have made a significant business decision based on bad data. When just one number on a balance sheet, market research report, or sales forecast can divert your course as an organization, what level of investment is enough to ensure you get that number right?
The HFS study also found that a mere 23% of respondents currently have an aligned data management strategy to help them keep on top of, among many things, analytics accuracy. Without a strategy and clear organizational alignment around data, it’s going to be very difficult for data stewards to acquire investment and resources to deliver the trustworthy data and analytics the organization needs to make crucial decisions.
Address your data quality
The first thing necessary to address a problem is acknowledging that a problem exists. Addressing bad data quality is no different. In many respects, the first hurdle a business must appreciate is that data isn’t just an IT issue anymore; it’s a business-wide issue that needs investment to manage such a significant, business-critical asset. Understand that sometimes, data quality improvement can be hard to measure because the biggest benefit is often what doesn’t happen. Use the four bad data quality examples above to assess your organization’s need for improved data quality and make the necessary changes to save not only your reputation but your bottom line.
