As the end of 2021 approaches, you might be closing your books for this year and get ready for the next year. Hopefully you managed to achieve many of your goals for last year and also set some new challenging ones for next year. Fresh start ahoy! Like many of you, we do the same @Human Inference…

For starters, what are we talking about?

Return on investment, better known as ROI, is a key performance indicator (KPI) that’s often used by businesses to determine profitability of an expenditure. It’s exceptionally useful for measuring success over time and taking the guesswork out of making future business decisions. Talking about Data Quality, Andreas Bitterer, former VP Gartner Research says, ‘’Organizations need accurate and trustworthy data to make intelligent business decisions.’’ It doesn’t matter what the business of a company is, it is always working with data. Therefore, the cost of bad data can be very high. MIT Sloan calculated that business dealing with the consequences of bad data costs a company around 15%-25% of their annual revenue.

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You might wonder, what are those failures and why are the costs so high? Below, we have listed some examples bad data can cause:

·      Added costs for correcting the mistake. If a data failure is made, this failure needs to be solved. This always costs more than if you do it correctly at once. You must pay your personnel for the extra hours they work on the issues. Instead of working on other tasks, they are now working on solving the problem. And if another party is involved in the solving the mishap, that might mean hiring them for extra hours.

·      In case of a data breach, a company risks a huge fine. To ensure that companies are held accountable to keep the data of their customer safe, a new law has been introduced in 2018, named The European Union’s General Data Privacy Regulation (GDPR). If this law is breached by your company, what can happen due to data failures, you can imagine the breach will result in hefty fines.

·      When you don’t have the right customer data, you might target the wrong customer group, and this can lead to less sales then when you had targeted the right group. As specialists in Data Quality, we see this all over when we have our meetings with our customers and prospects. They want to improve their Data Quality, because the costs of doing nothing are very high. Having customer data is also very important to deliver the right service to the right customer. Without these data you do not know what every customer needs and customers will not receive the optimal service. Companies with more, cleaner, accessible data and the means to use that data, will have a competitive advantage.

·      Causing a failure based on bad data, can also lead to image damage.This might be the most abstract one to measure but is of very high value. If you might already have experienced yourself as a customer, it is annoying if something goes wrong. This can be a data breach, but also a failure in the data what leads to a wrong product delivery or service. Your company experiences image loss and customers are more likely to stay away.

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ROI Approach

This is where the concept of the ROI for DQ comes in. The goal is to provide relevant metrics that highlight the more critical data quality issues, and tie those issues to actual business problems. This can either be related to increased costs or with lost opportunities. Calculating the scope of the actual costs of those business problems and then comparing those costs with what it will take to improve data quality provides that elusive ROI model. As KPI’s are always individual, we typically use our approach to  address above-mentioned roadblocks. By providing clearly defined metrics and their actual measurements, and tying them to actual business problems, this ROI model builds the argument for senior-management support of data quality improvement initiatives. And by highlighting the most critical data issues, this model provides a starting point for the improvement process.

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So what to do next?

First of all, think on the investments. The investments in data quality can be divided into three groups, people, process, and technology.

1.      Good personnel. This means the right number of personnel, but also invest in training for them to stay up to date with the latest trends and techniques.

2.     Process. This means investments in changes and enhancements to support new data quality technologies.

3.     Technology. These are solutions introduced to automate improvements in data collection, data integration, data sharing and application, like the ones of Human Inference…

The gains which should be measured include mainly revenue improvements that result from these improvements in data quality, but also the decreased costs due to less data failures and therefore lower operational costs. However, there are also multiple indirect benefits which have a substantial value. For example, benefits that results from an efficient process and improved compliance.

As your gut feeling might already be telling you, in the end the benefits should be higher than the investments to have a positive ROI. So don’t be afraid to invest! If you make sure to invest in the right people, process and technology, you will never do it wrong. And if you need any assistance, don’t hesitate to call us to help you out. If you want to sit with your team and talk about this, we are more than willing to setup a workshop with you on this topic.