In my previous post, I defined data literacy and information literacy. In this post, I'll explore the common barriers to data literacy and information literacy.
These are some of my takeaways from a recent Data Analytics Network (DAN) panel discussion I participated in, titled "Bridging the Data Literacy Gap."
Moderated by Justin Scott (Metals Service Center Institute), the panel discussion featured myself, Reeda Kindred (National Association of Mutual Insurance Companies), and Wes Trochlil (Effective Database Management). Through our panel discussion and the chat contributions from participants, several key barriers to data and information literacy emerged:
Many staff members feel they lack the technical skills to work with data effectively. As we discussed, it's important to reframe data literacy—staff don't need to become data scientists. They need to be data curious. As one participant pointed out, "'Data Curiosity' should be a key professional skill."
One of the most fundamental barriers to effective data use is the absence of clear, shared definitions for basic data definitions. Without this foundation, organizations struggle to build trust in their data systems.
Consider a seemingly simple question: "Who is a member?" In associations, this question can yield multiple answers depending on whether you include members in grace periods, suspended members, or lifetime members. Each interpretation produces different metrics, which in turn affects critical business decisions about membership trends, retention rates, and revenue projections.
This definitional confusion creates a cascade of trust issues. When employees encounter inconsistent numbers across different reports or dashboards, they often assume the entire system is unreliable rather than recognizing that different definitions are being applied.
A single data error can undermine confidence in an entire reporting system, leading teams to revert to manual processes or create their own "shadow" datasets.
The tolerance for data imperfection is particularly low in many organizations. Employees who discover even minor discrepancies may dismiss entire dashboards as untrustworthy, rather than understanding that data quality is an ongoing process requiring continuous improvement. This perfectionist mindset paralyzes data adoption and prevents organizations from gaining valuable insights from largely accurate information.
The financial impact is significant. Gartner research from 2020 found that poor data quality costs organizations an average of $12.9 million annually, reflecting not just the direct costs of errors but also the opportunity costs of delayed decisions and reduced productivity when teams can't rely on their data infrastructure.
We've all heard the question: if a tree falls in a forest with no one around, does it make a sound? Organizations face their own version of this paradox in data analysis.
When companies invest heavily in data management, information management, and data analytics but no one uses those insights to make decisions or take action, did we really accomplish anything? As one frustrated participant put it: '[We] work on providing a lot of insightful data and see no action taken from it.' Like the unheard tree, unused data represents wasted potential and questions the very value of the effort.
One of the most compelling concepts we discussed was the "last mile" of data literacy—a term coined by our host Justin Scott. The "last mile" represents the hardest part of the data analysis process: ensuring that data is useful and relevant to the user, communicating the data effectively, making decisions based on it, and documenting the outcomes.
As one participant astutely noted, "the ability to translate business need to data, and data to business need is critical." This translation is where many organizations struggle, despite having access to robust data and analytics tools.
Breaking down these barriers to data and information literacy isn't just a technical challenge—it's a cultural transformation that requires deliberate action at every level of an organization. The path forward involves building confidence through curiosity rather than technical expertise, establishing clear data definitions that everyone understands and trusts, accepting that perfect data is less valuable than actionable insights from good data, and most critically, creating systems that bridge the gap between analysis and action.
Do your people feel empowered to use data? Do they trust it enough to act on it? Do they have the support systems in place to turn insights into impact? That's where true data literacy and information literacy begins, and where competitive advantage is built.
For AIIM+ Pro members looking to deepen their understanding of these essential principles, our new Data Integrity Essentials course explores the often-overlooked aspects of data ethics, accuracy, and responsible data management that form the bedrock of truly effective data literacy initiatives. AIIM+ Pro members can take the Data Integrity Essentials online course on-demand. Not a member? Learn more about AIIM+ Pro membership.