For years, companies have invested in "data literacy training" to help employees become more self-sufficient with data. The idea is good: empower more people to make data-driven decisions. But, judging on the low penetration of self-service BI in general, the execution has often fallen short.
Why? Because most programs have been built around the tools — not the people. Traditional data literacy programs essentially teach business users to think like data engineers. They learn how to navigate schemas, pick the right joins, apply filters, and manipulate metrics in BI tools like Tableau or Power BI.
That approach made sense in a world where BI platforms couldn't interpret business context. But in today's world of NLP-powered, conversational analytics, this model is outdated. It's time to rethink data literacy from the ground up.
The Old Model: Training People to Adapt to the Tool
Traditional BI tools are powerful but rigid. To get meaningful insights, users had to learn the rules of the system:
- How to select fields correctly.
- How to understand hierarchies and joins.
- How to build dashboards that reflected business questions.
In essence, the users need to learn how to translate a business question into a series of "how to" steps to ensure the correct data is included in the report or dashboard to support the area of insight exploration desired.
Even if the BI tool is based on a semantic layer, this approach shifts the burden to the user. Instead of focusing on business outcomes, they were trained to handle data plumbing.
Problem: Data literacy became synonymous with technical training — not decision empowerment.
The New Model: Data Literacy in a Conversational World
Conversational analytics, powered by NLP and intelligent, context aware semantic layers, flips the script. Now, instead of learning how to think like an engineer, users can think like what they already are: decision-makers.
Here's how literacy changes:
From tool-centric skills → context-centric skills Users don't need to know how to join tables. They need to understand what "customer churn" means in their business, or how "net revenue" differs from "gross revenue."
From data manipulation → business questioning The key literacy skill is learning how to ask good questions, explore follow-ups, and validate insights.
From accuracy obsession → insight emphasis Accuracy still matters, but the focus shifts toward drawing business meaning from the insights and applying them in decisions.
From human-only analysis → human + AI collaboration Literacy now includes knowing how to work with an intelligent query agent: refining prompts, validating answers, and iterating to get to the heart of a business challenge.
Context Fluency: The New Core of Literacy
The critical skill for the future isn't "dashboard building" — it's context fluency.
Employees need to:
- Understand key business terms and metrics.
- Know which questions matter most to their function.
- Trust the system to handle the technical complexity, while they bring the judgment and domain expertise.
This is a seismic shift. Instead of dragging people to the data, conversational analytics brings the data to the people — in language they understand.
Business Example: From Engineer-Like to Decision-Ready
Old Model: A marketing manager spends hours learning how to build a funnel report in Tableau, figuring out which fields map to "leads," "MQLs," and "conversions."
New Model: The same manager simply asks: "Show me the conversion rates from MQL to customer by channel last quarter." The system knows what "MQL" means, applies the right rules, and delivers a clean, accurate answer.
Which manager is truly more "data literate"? The one learning to click the right joins, or the one driving strategy with data-powered insights?
The Future of Data Enablement
Organizations that cling to old-style literacy programs will struggle to scale insight. Training everyone to be a part-time data engineer is inefficient and unrealistic. Survey results from IBM, BARC and others seem to indicate that adoption of BI remained well below 30% despite heavy investment in tech and enablement. Compare that to adoption of tools like Slack, Google Search and lately of course ChatGPT.
The future is about enabling decision-makers, not tool operators. Conversational, NLP-driven analytics lets companies shift literacy efforts toward:
- Understanding context and definitions.
- Asking better questions.
- Trusting, interpreting, and applying insights.
- Storytelling with data in business terms.
Codd AI POV
Codd AI's platform is at the heart of enabling this new conversational style of analytics. The majority of semantic layers today in BI tools, data platforms and even stand alone semantic layers focus on translating the database complexity into business user friendly concepts - i.e. shield the user from the database complexity.
At Codd AI, we believe the semantic layer must contain both technical (database) metadata as well as the business logic, rules and know-how. The semantic layer is not just about being able to generate the correct SQL statement, but to use the full context of technical as well as business knowledge to interpret the business intent of a question correctly, turning it into instructions that then can be encoded into SQL to get the right data. And then to apply the business knowledge on the results to extract key insights and recommend possible follow-up questions.
To learn about Codd AI, schedule a demo discussion with our team.
Closing Thought
Data literacy is not dead — but it's evolving. In the era of conversational analytics, literacy is less about learning the tool and more about understanding the business.
With intelligent semantic layers powering NLP interactions, companies no longer need armies of "accidental analysts." They need empowered decision-makers who can ask smart questions, trust the answers, and act.
That's real data literacy for the age of AI.