Why Data Quality and Data Literacy Matter—For Everyone in Your Organization
By Sally Schulte, Senior Product Marketing Director
In today’s data-driven world, the success of any organization hinges not just on having data, but on having pristine, consistent, traceable data. High-quality, reliable data is no longer a “nice to have.” It’s a strategic asset that powers speed, decision-making, innovation, and growth.
Despite this, many organizations still treat data as a necessary burden to conduct business versus a strategic product that offers a competitive edge. And many employees view data as someone else’s responsibility—something technical and distant from their day-to-day work. But bad data impacts every part of the business, from product development and analytics to automation and efficiency.
That’s why data literacy must extend beyond IT departments and data teams. Everyone in your organization, from marketing and communications to operations and customer service, needs to recognize the value of reliable, accurate, and accessible information—and understand the role they play in ensuring and protecting good data.
Treating Data as a Strategic Product
To succeed today, organizations must adopt a new mindset where data is seen not as a secondary output of operations, but as a core product—deliberately gathered, maintained, and optimized for use. Making this shift requires intentional decisions from business leaders to:
- Assign ownership across the organization
- Enforce standardization to ensure consistency
- Maintain version control and tracking
- Align on how data will be used across the company
In the CIO.com article “5 actions to build an AI-ready data culture,” executives like Mike Kreider, CIO of DHL Supply Chain North America, explain why their organizations have fully embraced this approach. “We don’t want orphaned data products no one feels responsible for,” explains Kreider, because shipment data products support everything from daily operations to business development and also fuel generative AI tools like DHL’s proposal generator. “If the data product doesn’t exist or isn’t clean, the tool won’t work,” he emphasizes.
The Hidden Cost of Poor Data
When data is incomplete, inaccurate, and old, the effects ripple across the organization:
- Inefficiency: Teams spend hours making corrections, chasing down missing information, and entering the same data in multiple systems.
- Flawed decisons: Business strategies get built on erroneous assumptions.
- Eroded trust: Customers feel the pain through incorrect outcomes, delayed service, and irrelevant (and potentially insensitive) messaging.
According to Gartner, bad data can cost organizations as much as $12.9 million per year. That’s not just inefficiency—it’s an alarmingly large, missed opportunity.
While your company deals with databases that don’t reconcile or forms that never made it back from the mailroom, your competitors are making real-time decisions, extending automation, and strategically capitalizing on AI-based initiatives. They’re innovating faster, responding to customers quicker, and identifying trends before you do. In the modern landscape, poor data isn't just a nuisance—it’s a competitive risk.
Outdated Collection Methods Are Holding You Back
Legacy data collection processes—like fillable PDFs, mailed forms, or manually entered spreadsheets—are unreliable and inefficient. They're prone to human error, data loss, and delays. Worse, they create silos that slow down collaboration and decision-making.
Organizations that still rely on these outdated processes are falling behind. Cloud-based digital tools, automated workflows, and real-time data integration have raised the bar. If your team is still waiting on mailed-in forms or deciphering handwritten notes, you’re not just behind the times—you’re making decisions in the dark.
Data Literacy is Everyone’s Job
To unlock the value of high-quality data, organizations must foster a culture of data fluency where every employee understands how data is used, why accuracy matters, and how their work contributes to data quality. This doesn’t mean everyone needs to become a data scientist. It means giving people the tools and training to handle data responsibly and thoughtfully.
From the frontline employee entering customer data, to the manager interpreting reports, to the executive setting strategy—everyone plays a role. One careless entry or one unvalidated data source can ripple across systems and decisions.
The Bottom Line
High-quality data fuels confident decision-making, better customer experiences, and faster innovation. But it doesn’t happen by accident. It requires modern tools, reliable collection processes, and a shared commitment across your organization.
Companies that continue to rely on legacy methods and treat data as someone else’s job will find themselves increasingly outpaced by more agile, data-savvy competitors. In contrast, organizations that prioritize data quality and fluency will be positioned not only to survive—but to lead.
Now’s the time to ask: Is your organization treating data like the critical asset it is?