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The Hidden Costs of Bad Data

Data drives so many important decisions in our lives whether we realize it or not.  From the way we communicate, to the way businesses are run, to the maps we use to get from place to place. Data is all around us and has a profound impact on our daily lives. 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. IBM looked at poor data quality costs in the United States and estimated that decisions made from bad data cost the US economy roughly $3.1 trillion dollars each year.

Research from Experian Data Quality also found that bad data has a direct impact on the bottom line of 88% of all American companies.  The averages losses from bad data was 12% of the company’s overall revenue. A Gartner Reporter also found that 27% of data in the World’s top companies is flawed.

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.  This may sound extreme, but there are countless examples that show how bad data and the spread of misinformation have shaped who we are as a society.

Utopia Inc, has curated a list of famous examples of how bad data has changed the course of history. The list provides a snapshot of both historical and recent events that have happened as a result of bad data.  They also explore how these mistakes could have been prevented and the lessons we can learn from them.

There are also a few non-financial impacts of bad data that are often overlooked including:

  • Incorrect strategic decisions made as a result of bad data
  • Loss of credibility for your business
  • Time lost in productivity as a result of bad data.

So now that we’ve looked at the impact of bad data, let’s explore how can the costs of bad data be reduced?

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.

 

Figuring out who is responsible for data quality is an important first step in reducing the costs associated with bad data. Reducing these hidden causes of bad data is crucial to stop the negative impact of bad data. The data quality in your organization is ultimately everyone’s business regardless of whether or not they have direct oversight over the data.  Data scientists, business managers, and knowledge workers all have a responsibility to fix the false data as they come across it.

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.

About the Author

Arvind J. Singh is CEO, Chairman, and Co-Founder of Utopia, Inc. He is a serial entrepreneur with experience in building global businesses for the last 20 years. He believes in boot strapping start-ups and has done that successfully twice with no external funding.

 

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Comments

  1. JD Donnelly says:

    Hello, I am seeing if you would be able to provide the reference to the following sentences:
    “Research from Experian Data Quality also found that bad data has a direct impact on the bottom line of 88% of all American companies. The averages losses from bad data was 12% of the company’s overall revenue.”

    Thank you

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