The Cost of Bad Data

Andrea Steffes-Tuttle Data Management

They don’t call it “big data” for nothing. Companies today are struggling to store, manage, and most importantly, use the massive amounts of customer data they collect.

Even worse, bad data is holding them back from tremendous business opportunities. Poor data quality costs the U.S. economy around $3.1 trillion per year, according to IBM. This unreliable data forces everyone in your organization—from decision makers to managers, knowledge workers to data scientists—to double-check its accuracy. Or worse, not trust the data at all. And that’s bad for business.

Eighty-four percent of CEOs, in fact, are concerned about the quality of data they’re basing decisions on1. And with good reason. Insights based on inaccurate data can lead companies to miss out on critical business opportunities their competitors are already acting on. Data entry mistakes made on things like insurance policies, for example, can lead to lost revenue. Today, business decisions are only as good as the data they’re based on.

Smart businesses have already figured out how important a sound data management strategy is to their bottom line. Consider these companies, recently highlighted by Forbes, who are leveraging data analytics—powered by clean data—to improve business value for their customers:

  • Telecommunications giant T-Mobile used data analytics to provide real-time, actionable insights to make significant improvements in its network.
  • Austin, Texas-based Seton Healthcare reduced the number of patients being readmitted to hospitals for congestive heart failure by analyzing data from doctor notes, discharge summaries, and echocardiogram reports.

Still not convinced? Here are some of the more tangible benefits good data quality offers:

  • More confident decision making. The old “garbage in, garbage out” adage still holds true. Relying on good data instead of your gut is a much less risky proposition.
  • Increased productivity. Instead of spending time validating and fixing data errors, your employees can focus on their core strengths that add value to your company.
  • More accurate target marketing. Solid customer information allows you to better understand your prospects and their behaviors and increase sales.

Big Data and business analytics are predicted to grow from $130 billion to $203 billion by 2020, according to research firm IDC. The key to staying competitive—and differentiating yourself in the marketplace—will come down to managing your data efficiently and effectively. After all, if you’re not, your competitors surely will be.

 

Tips for Cleaner Data Management

The cleaner your data, the more accurate your insights. With new technologies like automation, artificial intelligence, and the Internet of Things promising to generate even more information, managing your data has never been more important.

Here are some ways you can begin cleaning up your data right now:

  • Delete the Killer D’s of data: duplicate files, dirty records (wrong data), and dead records (defunct companies).
  • Streamline your data entry systems down to one system to save your employees time.
  • Cut down on data entry errors for company names and use a globally known unique identifier like a URL.

Once your data is in good shape, it’s time to use advanced analytics to make better business decisions. Predictive analytics can help you use data to predict customer behavior, optimize campaign performances, and design your marketing initiatives to show a strong return on investment.

Ready to get more out of your data? Talk to an analyst to learn how Lityx can partner with you to help you assess your data management and leverage advanced analytics to improve your marketing efforts.

 

1 Forbes Insights: The Data Differentiator: How Improving Data Quality Improves Business, 2017.