You would be hard-pressed to find any business person today who would argue that data has no value or that the quality of an organization’s data has no impact on its ultimate bottom-line profitability.
The reality is that, for many years, mid- and enterprise-level organizations have been collecting data that requires considerable work to integrate, cleanse, verify, prepare, and analyze. As these organizations try to move toward data-driven decision-making, the quality of the information at their disposal becomes ever more important.
Why data quality matters
There’s good data and there’s bad data, and the worst of it costs organizations big money every year. Discussing how bad data can cause poor decision-making at every level of a company, Neil Patel, co-founder of Crazy Egg, Hello Bar, and Kissmetrics notes, “When departments are clamoring for numbers despite the inaccuracies, it leads to a ripple effect of poor decisions based on those errors.”
Gartner found that poor data quality costs organizations an average of $15 million annually in losses, and IBM estimated that poor data quality costs U.S. businesses a whopping $3.1 trillion per year. It’s not just money that’s at stake, either. Poor data quality carries heavy costs in missed opportunities, reduced productivity, and reputational damage. ibi’s own “Data and analytics trends and directions 2021 report” revealed that data quality and data security were tied at the top spot of issues that keep survey respondents up at night (at 72 percent).
As companies move toward a more strategic analysis of all the data they’re capturing, it’s clear that data quality will become even more important in guiding key business imperatives.
What is data quality?
The definition of data quality can vary by company. Simply put, when data quality is high, it is useful to your organization. Inconsistent and ambiguous data is common as you embark on a data quality initiative. However, resolving inconsistencies and ambiguities supports your effort to create a single source of truth for decision-making, something that should be high on your list of strategic priorities.
Data quality best practices
Organizations seeking to improve data quality can make headway by following these four best practices:
- Conduct data discovery by analyzing the current state of your data. What’s wrong with the data you’ve captured? Where does it reside? Do end users have access, and does the data integrate well?
- Define data quality rules by determining how you want to use your data. Note that this process should be geared toward business use cases, and with this in mind, it should be a collaborative process that extends beyond IT. Consider what decisions the data could fuel. To reach your goals, will you need to cleanse or depopulate it? What data standardization roles need to apply? Is old data muddying up your data lake, and should it be discarded?
- Implement the rules you have established. Integrate these governance models in a phased approach across all relevant silos. Following this best practice will help you remediate data quality throughout the entire organization.
- Monitor and govern the data as an ongoing process. Data is like the body’s circulatory system, passing information back and forth along the information arteries in your organization. That makes data governance particularly important as part of an ongoing data quality initiative.
The problem and imperative of data quality isn’t just an IT issue. Data quality affects the entire organization. The consistency and integrity of your data matters, which is why ibi is devoted to embedding quality data into all of your business processes. To find out more, download a complimentary copy of our ebook, “The business value of trusted data.” In it, you will learn how to build the business case for trusted data, from quantifying costs to garnering executive support.
For additional information about data trends to help you build your case, take a look at our latest research report, “Data and analytics trends and directions 2021.”