If your data is not correct, it can wreak havoc on your business. Not only is it time-consuming and irritating, it can also have a serious impact on your results. Think about all the missed opportunities because you didn’t have the right information. Think about all the mistakes that, depending on your industry, could lead to bad customer orders, poorly built products, and broken 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 do you know how much it costs you?

Let’s look at the four main ways that poor data quality negatively affects businesses.

1. Regulatory Concerns

Almost no industry is immune to regulation, especially when it comes to the handling of personal information. In the European Union, the GDPR applies to any organization operating within the EU as well as any organization outside the region that provides goods or services to EU customers. Failure to comply can lead to significant fines – up to €20 million or 4% of the company’s overall turnover for the previous financial year. And a data breach resulting from a lack of compliance leads to reputational damage as well as financial damage.

Bad data hinders compliance in several ways. First, inaccurate data can lead to breaches and non-compliances. Second, if the data is not handled properly, potential legal issues can arise. And third, “bad” data can simply be data whose location you don’t know; you can’t quickly access it when needed.

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2. Processing 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 has changed and you can’t call them at a critical time. These errors slow down business, but at least someone caught them. But the real ghost of the machine is the loss of efficiency that is only found after the fact. This is the mistake that really hurts your business.

For example, a company ended up with a large number of duplicate supplier and customer records because its system was so difficult to navigate that employees created a workaround: just create a new record instead of find the original. This caused significant business issues as it naturally led to more errors in supplier and customer analysis. It also resulted in multiple and inconsistent contact information and billing terms, which sabotaged the company’s bottom line.

Issues like this can cause customers to cancel subscriptions or return products. Businesses may not be able to credit full order volume to customers so they do not receive volume discounts. These process errors are costly and unnecessary today.

3. Disruption and expenses

Businesses cannot opt ​​out of digital transformation at this point; these are table stakes. This therefore implies a transformation of the data. This can happen due to an infrastructure upgrade, cloud migration, enterprise resource planning consolidation, or merger/acquisition activity. In such dynamic situations, data quality and data migration methodology are of crucial importance.

The more data you have and the larger the project, the more likely you are to encounter problems, such as incompatibility of the data structure between the source system and the target system. Even the best-intentioned transformation plans can be disrupted by data 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 just kicked the box to fix those issues a bit later.

Organizations can be hit with hundreds of thousands of dollars a day due to data-related delays. These include recurring resource expenditures, loss of business value, and disruptions to business processes. There is also the risk for the reputation of the company of a bad migration in the public space. Companies will likely always play catch-up when a comprehensive data management strategy is not in place from the start of a transformation.

4. Poor Decision Making

According to a recent survey by HFS Search, only 5% of senior executives surveyed are very confident in their data. In one black line survey, 70% of respondents said they made important business decisions based on low-quality data. If a single figure on a decision-making document like a balance sheet or a sales forecast can lead to a loss-making decision, what level of investment is needed to obtain this figure?

Additionally, HFS found that only 23% of respondents have a data management strategy in place to ensure accuracy and consistency with analytics. It is nearly impossible for data stewards without a clear strategy and organizational alignment on data to gain management buy-in and the resources to deliver the data and analytics the organization can fully trust when she makes critical decisions.

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Data quality: an enterprise-wide issue

Acknowledging that there is a problem is the first step to solving it. The same goes for resolving poor data quality. A business must understand that data quality is no longer just an IT issue; it’s an enterprise-wide problem – and one that requires investment. Improvements in data quality can sometimes be difficult to measure, as the greatest benefit often comes from errors that do not occur. Review these four examples of poor data quality to determine if your organization needs to improve data quality. Then make the necessary changes to safeguard your brand and profit margin.