Four Ways Bad Data Quality Can Wreck Your Business

If your data isn’t right, it can wreak havoc on your business. Not only is it time-consuming and irritating, but it can seriously impact your bottom line. Think of all the missed opportunities because you didn’t have the right information. Think of all the mistakes that, depending on your industry, could result in wrong customer orders, improperly built products and unfulfillable promises. This could cost your organization a lot of money each year. But if you don’t know where the bad data is, how can you fix it? And how can you know how much it’s costing you?

Let’s examine the four primary ways that poor data quality negatively affects business.

1. Regulatory concerns

Almost no industry is immune to regulation, particularly pertaining to the handling of personal information. In the European Union, GDPR applies to any organization operating within the EU as well as any organizations outside the region that provide goods or services to EU customers. Failure to comply can result in significant fines – up to €20 million or 4% of global turnover of the company’s preceding fiscal year. And a data breach that results from a lack of compliance brings reputational damage along with financial damages.

Bad data impedes compliance in a number of ways. First, inaccurate data can result in breaches and noncompliance. Second, if data isn’t governed properly, there are potential legal issues in store. And third, “bad” data can simply be data that you don’t know the location of; you can’t quickly access it as needed.

2. Process errors

There are many anecdotes of bad data leading to unintended and unfortunate consequences. Maybe someone added an extra zero to an invoice or shipped the wrong item. Maybe a customer’s number changed and you can’t call them at a critical moment. These errors slow down business, but at least someone caught them. But the real ghost in the machine is the loss of efficiency that you only find after the fact. That’s the error that truly hurts your business.

For instance, one company ended up with a huge number of duplicate vendor and customer records because their system was so difficult to navigate that employees created a workaround: just make a new record instead of hunting around for the original. This caused significant business problems because it naturally resulted in more errors in vendor and customer analysis. It also resulted in multiple and inconsistent contact information and invoicing terms, which sabotaged the company’s bottom line.

Issues like this may lead to customers cancelling their subscriptions or returning products. Companies might not be able to credit total order volume to customers so that they miss out on volume discounts. These process errors are expensive and unnecessary today.

3. Disruption and expense

Businesses can’t opt out of digital transformation at this point; it’s table stakes. This, then, involves data transformation. Maybe this occurs due to an infrastructure upgrade, migration to the cloud, consolidation of enterprise resource planning or merger/acquisition activity. In such dynamic situations, data quality and the methodology for migrating data are crucially important.

The more data you have, and the bigger the project is, the more issues you’re likely to see – such as the incompatibility of data structure from the source to the target system. Even the best-intentioned transformation plans can be thrown into chaos by data-related issues. Although some companies may be able to migrate their data to a new system or to the cloud, bad data can still cause serious problems. They’ve just kicked the can of dealing with those problems down the road a bit.

Organizations can be hit with hundreds of thousands of dollars per day due to data-related delays. These include run-on resourcing expenses, lost business value and business process disruptions. There is also the risk to company reputation of a bad migration in the public arena. Businesses will likely always play catch-up when a comprehensive strategy to manage data is not in place from the beginning of a transformation.

4. Poor decision-making

According to a recent survey by HFS research, only five percent of C-level executives surveyed are highly confident in their data. In a Blackline survey, 70% of respondents said they had made important business decisions based on data of low quality. If just one figure on a decision-making document like a balance sheet or a sales forecast can lead to a money-losing decision, what level of investment is necessary to get that figure right?

In addition, HFS found that only 23% of respondents have a data management strategy in place to ensure accuracy and consistency with analytics. It’s almost impossible for data stewards without a clear strategy and organizational alignment to data to obtain the executive buy-in and resources necessary to deliver the data and analytics that the organization can fully trust as they make critical decisions.

Data quality: A business-wide issue

Recognizing that there is a problem is the first step to fixing it. So it is with addressing bad data quality. A business must understand that data quality is not just an IT problem anymore; it is a problem that spans the business – and it requires investment. Data quality improvements can sometimes be difficult to measure, as the greatest benefit often comes from the mistakes that don’t occur. Take a look at these four examples of poor data quality to determine if your organization needs to improve data quality. Then make the changes necessary to safeguard your brand and your profit margin.

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Source : JAXenter