Overcoming the Barriers: Practical Solutions for Data and Information Literacy

In my last two posts I defined data literacy and information literacy and then reviewed common barriers to literacy. Now, let's look at solutions to overcoming these barriers. These insights are inspired by a recent Data Analytics Network (DAN) panel discussion I participated in, titled "Bridging the Data Literacy Gap." Featuring myself, Reeda Kindred from the National Association of Mutual Insurance Companies, and Wes Trochlil from Effective Database Management, the panel discussion was moderated by Justin Scott, PhD, CAE from the Metals Service Center Institute. 

Barrier 1: Lack of Confidence and Feeling Unqualified

The Challenge: Many staff members feel they lack the technical skills to work with data effectively, believing they need to become data scientists to be valuable contributors.

Solutions:

1. Start with Executive Buy-in and Culture Data literacy and information literacy must be championed from the top. Leadership needs to model data-driven decision making and create an expectation for research-based decisions. As one participant noted from their experience, celebrating data-driven decision making—like hosting a "data science fair"—can make a significant difference in building confidence across the organization.
2. Make Data Literacy Part of Job Descriptions Put data and information literacy expectations directly in job descriptions. Make literacy training part of new employee onboarding. This normalizes data skills as part of everyone's role, not just the domain of technical experts.
3. Embrace AI as an Enabler All three panelists identified AI as the biggest trend affecting data literacy. Natural language querying and analysis capabilities in modern CRM and AMS platforms can make data more accessible to non-technical staff. Encourage conversations with your data—Copilot and new AI features in CRMs can be great for this. However, as one participant wisely cautioned, "Staff still need to be literate enough to tell if the AI is wrong." AI should be viewed as "assisted intelligence" and not a substitute for human intelligence.

Barrier 2: Definitional Confusion

The Challenge: Without clear, shared definitions for basic data concepts, organizations struggle to build trust in their data systems. Different interpretations of seemingly simple questions like "Who is a member?" create inconsistent metrics and undermine confidence.

Solutions:

1. Leverage Documentation and Training Tools Clear documentation is essential for establishing shared definitions. Several participants mentioned excellent tools for improving data literacy:
   • AIIM's certificate courses for foundational information management education
   • Software like Tango.ai and Scribe for recording and documenting how to use systems and analyze data
   • Using native reporting and dashboarding tools in your existing systems
   
   As one panelist emphasized, "Documentation and Training [is] admittedly 'eating your vegetables' work, but essential to success!" This documentation should include clear definitions of all key metrics and data points.
   
   
2. Integrate Data into Daily Operations Make data reports and visualization part of regular staff meetings, but ensure they're conversation starters, not just dull presentations. As one participant noted, "Conversations about the data help you understand what problem you're trying to fix before jumping into solutions mode." Regular conversations help establish and reinforce common definitions.

Barrier 3: Data Hygiene Issues

The Challenge: 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 perfectionist mindset paralyzes data adoption.

Solutions:

1. Make Data Accessible and Self-Service The solution is making data reports and visualizations accessible and easy to run. Self-service capabilities are key. When people can easily access and verify data themselves, they develop a better understanding of data quality issues and are more likely to work with imperfect but useful information.
2. Focus on Cultural Change Address the perfectionist mindset by creating a culture that values actionable insights over perfect data. Leadership must model this by making decisions based on good data rather than waiting for perfect data that may never come.

Barrier 4: Lack of Action on Insights

The Challenge: Organizations invest heavily in data management and analytics but see no action taken from insights. This represents the "last mile" problem—the gap between analysis and action.

Solutions:

1. Bridge the Analysis-to-Action Gap As one participant noted, "the ability to translate business need to data, and data to business need is critical." Create systems and processes that specifically address this translation:
   • Make data reports conversation starters in meetings
   • Require action items to be identified from data insights
   • Document decisions made based on data analysis
   • Follow up on the outcomes of data-driven decisions
2. Measure Success Appropriately How do you know if your data and information literacy initiatives are working? Consider measuring:
   • Staff confidence levels with data
   • Usage of dashboards and reports
   • Time to decision-making
   • Participation in training programs
   • Most importantly: Actions taken based on data insights

Moving Forward

The conversation reinforced something I've long believed: information literacy isn't just about technical skills—it's about creating a culture where information is valued as a strategic asset.

For information management practitioners, the message is clear: prioritize your most valuable data, seek to understand its lifecycle and meaning, and model data-driven decision making. The "last mile" of data literacy and information literacy—where data becomes actionable insight—is where the real transformation happens.

Breaking down these barriers 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.

We need to empower every employee to work confidently and competently with the information that drives their success. 

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.