[IMAGE: GETTY IMAGES]
Data drives so many important decisions in our lives whether we realize it or not. In a world of Big Data, bad data is becoming more and more commonplace. Part of the issue is fuelled by the technology we use to help manage and organize that data. In our rush to be more on-demand, personalized and data-science-powered, we have embraced cloud computing, mobility, social collaboration and enhanced analytics. But what happens when we rely on bad data to make a decision? Is it as simple as arriving late to work as a result of bad directions, or does bad data have a more costly and meaningful impact on our lives?
Erroneous decisions made from bad data are not only inconvenient, but also extremely costly.
Harvard Business Review found that the reason that Big Data is so costly is because many departments within a company are affected by a single source of bad data. Each department within a company has to accommodate the bad data in their everyday work, which is very time-consuming and expensive for a business. Each department must add steps and extra work to accommodate the errors from the bad data they received. When bad data is passed between departments, those errors leak through to other facets of the business. As you can see in the image below it can snowball rather quickly.
Data drives the world these days. From the way people communicate to the way businesses run, data surrounds all aspects. But what happens when data steers the ship in the wrong direction? Not only is it inconvenient, it’s expensive. In 2016, IBM estimated that bad data cost the US $3.1 trillion.
These numbers are huge and shows how big of a threat bad data really is.
Beyond the financial impact of bad data, there are even larger ramifications of bad data that can have a profound impact on our society as a whole. While there may be modern problems, bad data isn’t anything new. The collection and distribution of bad data has been around for thousands of years.
The reason bad data costs so much is that decision makers, managers, knowledge workers, data scientists, and others must accommodate it in their everyday work. And doing so is both time-consuming and expensive. The data they need has plenty of errors, and in the face of a critical deadline, many individuals simply make corrections themselves to complete the task at hand. They don’t think to reach out to the data creator, explain their requirements, and help eliminate root causes.
There is no mystery in reducing the costs of bad data you have to shine a harsh light on those hidden data factories and reduce them as much as possible.
Data continues to be the basis of many top decisions made by every business. By learning by the mistakes other have made in the past, we can help bad data from having a costly impact in the future.