Data quality and the uses to which the data are put impact the ROI you will observe on the data available. Use these questions to reassess how your...
Four Ways Bad Data Quality Hurts the Business Bottom Line
With so many ways for data quality to impact a business' bottom line, you must realize data quality is an issue in order to fix the bad data hurting your business.
Whether it’s a wrong mailing address, an inventory error, or an inconsistent payment term, coming across incorrect data that disrupts business workflow can be infuriating. While we can all recognize the inconvenience of locating bad data and having to fix it, how confident would you feel in identifying the economic, social, environmental, and reputational consequences of that incorrect record, or of all the incorrect data in your organization? It’s a bigger issue than you may think.
In the US economy alone, according to Harvard Business Review, bad data is estimated to cost organizations, an incredible $3.1 trillion* annually (about 6% of the total economy). Just imagine, if 6% of your organization’s turnover was lost because of bad data? But how would we know where those costs are coming from? And how would we identify what that cost is?
In a recent webinar: “The Cost of Bad Data and How to Stop Paying,” I was taken by Syniti’s Head of Solutioning in EMEA, Will Hiley’s breakdown that there are effectively four ways that bad data quality impacts businesses. To highlight these points, I thought it would be valuable to dig a little deeper into those four categories.
Bad Data Quality Cost #1 – Data Migration Inefficiency and Cost Overruns
To stay competitive, most large enterprises are in an ongoing state of digital - and therefore data - transformation. Whether that’s a move to the cloud, a merger or acquisition assimilation, an ERP consolidation, or an infrastructure upgrade, these projects are initiated by the business to unlock growth, increase agility, or streamline costs. In these dynamic digital transformation initiatives, data quality and the methodology for migrating data plays a pivotal role. The larger and more complex the project, the bigger headache data is likely to be with duplicate records, limited business partner harmonization, and data structure incompatibility between source and target systems being just some of the regular problems we help customers with on a consistent basis.
All these data issues cause disruption and can play havoc with even the best-laid transformation plans. While some organizations with an existing data problem may charge ahead with a data migration or move to the cloud (see this fantastic infoworld article on the dangers of rushing a move to the cloud), the pain resulting from bad data will only be delayed. At some point, the piper must be paid.
Data-caused go-live delays can cost organizations hundreds of thousands of dollars a day through run-on resourcing costs, continued business process disruption, and lost business value, let alone the reputational damage from getting a migration wrong in the public sphere. At Syniti, it’s clear from our experience, that 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.
Bad Data Quality Cost #2 – Consequences of Regulatory Issues
While some industries are more used to regulatory issues than others, all industries are now being familiarized with the increased rules on personal information, with regulations such as GDPR in the EU. Breaches in any of these regulations can yield substantial monetary fines for organizations, which should be enough to make a business sit up and take notice, but they can also inflict severe reputational damage.
Data comes into play in a number of ways with regulatory concerns. Firstly, in terms of the inaccuracy of data, which can lead to accidental breaches and missteps around certain legislation. Secondly, there’s the inaccessibility of data and the challenge of not knowing where relevant data is cataloged. And finally, there are issues surrounding the governance of data, which is crucial in understanding potential legal issues in the lineage of data.
Gaps in any of these introduce a range of costs that can easily rise into millions of dollars with certain legislation. And all that because the data was inaccurate, inaccessible or inappropriately governed.
Bad Data Quality Cost #3 – Decision Making Made on Inaccurate Analytics
According to a recent study conducted by HFS Research, only 5% of interviewed C-Level executives have a high level of confidence in their data; in another, conducted by Blackline, 70% of business leaders claimed to have made a significant business decision based off bad data. With business-critical decisions being made by leaders like these every day, that’s an incredibly concerning statistic. 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/resource is enough to ensure you get that number right?
In the same HFS study, it was discovered that only 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 it craves to make crucial organization-defining decisions.
Bad Data Quality Cost #4 – Day-to-day Process Loss
I’m sure we can all point to a story in our respective organizations, in which incorrect data caused some painful consequence. 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 in all those examples they were caught by somebody.
VP of Business Transformation at Syniti, Nate LeFerle, said in the webinar, “We can all see if there’s a data quality issue in a missed delivery for example, but it’s the phantom challenges, the efficiency losses that can only be identified forensically through the data that really hurt businesses.”
A recent client of Syniti’s told me of a situation where they had an infuriating amount of duplicate customer and vendor records that were causing some serious business issues. The reason for these duplicates stemmed from a system that was overly cumbersome to navigate, so much so, that the default user behavior had become to create a whole new record rather than find the original as it saved the user time. This workaround, while reasonable from the user perspective, created significant negative ramifications for the wider organization, with increased errors in customer/vendor analysis and inconsistent addresses/ contacts/ invoicing terms that cost the business significant amounts in cash flow.
These small breaks in data creation or data governance process quickly mount up and can lead to massive consequences if left unchecked.
The Cure for Bad Data Quality
As it’s often said, the first step to solving a problem is appreciating that one exists, and tackling bad data quality is no different. In many respects, the first hurdle a business must appreciate is that data is not just an IT issue anymore, it’s a business-wide issue and needs investment to manage such a significant, business-critical asset.
At Syniti, our view, is that trusted and understood data is the world’s most valuable asset, but we know it’s often not trusted by organizations. This is something we’re working to fix. With Syniti Data Jumpstart, our rapid data quality assessment, we quickly highlight why data is mission-critical to your bottom-line, identify opportunities and build your business case for improved data quality and master data in just 3 weeks. With more than 450 vetted, pre-built data quality reports and unique dashboards that identify cost-savings, cash flow, and P&L improvements, we help you get things rolling, fast. If you’d like to learn more about Syniti Data Jumpstart, you can do so here.
If you’re unsure whether investing in improved data quality is a worthwhile activity, we’ll leave you with this quote from Nate: “Sometimes, data quality improvement can be really hard to measure… because the biggest benefit is often what doesn’t happen.”
To read more news and thought leadership from Syniti visit our blog at blog.syniti.com.