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Turning data and AI investment into business value takes more than platforms and models: people need enough literacy to understand, question, and apply them in the decisions they make at work. That is the central argument in Bill Schmarzo’s framework. The 2023 NewVantage Partners executive survey he cites points to a persistent people-and-culture challenge, but it does not prove that literacy training alone creates business value.
Why literacy belongs in a data-to-value strategy
Organizations can modernize data systems, build AI models, and improve data quality without changing how decisions get made. A data-to-value effort therefore depends on people being able to interpret evidence, understand the limits of analytical tools, and connect insights to choices in their own roles.
Schmarzo’s argument is that AI and data literacy should be a starting condition for that effort—not a substitute for technology, governance, or business strategy. Literacy is broader than learning software: it helps employees understand what data and AI can do, when their outputs may be unreliable, and how a proposed use might create value for different stakeholders.
The evidence is suggestive rather than causal. The NewVantage survey asked data leaders about their organizations and their perceptions; it did not test training programs or show that literacy by itself improves financial results.
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What the 2023 NewVantage survey reported
In its November 2023 article, Schmarzo reported several findings from NewVantage Partners’ 2023 Data and Analytics Leadership Annual Executive Survey. Wavestone’s January 2023 announcement says the survey covered data leaders at 116 Fortune 1000 companies or organizations during 2022. The figures below are historical, executive-reported results—not current measurements of all businesses.
| Finding reported from the 2023 survey | What it suggests—and what it does not establish |
|---|---|
| 79.8% cited cultural issues as the greatest barriers to realizing business value, as reported by Bill Schmarzo and also by Randy Bean. | Respondents saw culture as a major obstacle; this does not identify a single cause or prove that a particular intervention will remove it. |
| 23.9% characterized their companies as data-driven, as reported by Schmarzo and Bean. | This records organizations’ self-description, not an independently measured maturity score. |
| 20.6% reported successfully implementing a data culture, as reported by Schmarzo and Bean. | It indicates a perceived implementation gap, not the share of organizations that would meet a common external definition. |
| 82.6% reported appointing a CDO or CDAO; 40.5% said the role was well understood, and 35.5% said it was successful and well established, as reported by Schmarzo and Bean. | Leadership appointments were more common than respondents’ reports of role clarity or success. |
| 1.6% ranked data literacy among CDO investment priorities, as reported by Schmarzo. | This exact ranking is reported in Schmarzo’s article; it should not be treated as independently verified against the original report table. |
Wavestone’s release says 84.6% of respondents held a CDO, CDAO, or the most senior data leadership role. That context matters: the survey captures leaders’ views, not a representative poll of every employee or organization. Bean and Thomas H. Davenport summarized the challenge in the release: “The human side of data continues to challenge companies, and data leaders and the organizations that they serve appear reluctant to change their paradigms.”
Schmarzo also reproduces a survey passage that asks whether data executives are focusing too heavily on technical issues while giving too little priority to literacy. Because the passage is not attributed to an individual speaker, it is best understood as a survey passage reproduced in his article, rather than a quotation from a named executive.
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Seven areas that make up AI and data literacy
Schmarzo’s framework, described in his book AI & Data Literacy: Empowering Citizens of Data Science, treats literacy as a combination of understanding, judgment, and organizational support.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute1. Data and privacy awareness
People need to understand how data is collected and used, what personal privacy means in a given context, and how safeguards can reduce misuse. This is essential whenever a decision involves personal or sensitive information.
2. AI and analytical techniques
Employees should be able to distinguish the kinds of questions different analytical methods can address and understand, at an appropriate level, how a model works. They also need to consider the intended use of a system and risks such as confirmation bias, unintended consequences, false positives, and false negatives.
3. Informed decision-making
Data and model outputs are inputs to decisions, not decisions in themselves. Basic problem-solving and decision frameworks can help people identify assumptions, compare options, and avoid common judgment traps.
4. Predictions and statistics
Understanding probability, averages, variance, and confidence levels helps people interpret uncertainty. A prediction is not a guarantee, and a single average can hide important variation across people, time, or cases.
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5. Value engineering
A data-to-value initiative needs an explicit account of how the organization creates value. That means identifying which outcomes matter and choosing measures that reflect the interests of different stakeholders—not assuming that one metric captures the whole result.
6. AI ethics
Ethical considerations belong in the design of AI systems and in the objectives used to evaluate them. A model can perform as specified and still create unacceptable effects if its goals, data, or deployment context are poorly chosen.
7. Cultural empowerment
People and teams need the confidence and practical understanding to explore where data and AI could help their work. Empowerment is not unrestricted access: it should operate alongside clear permissions, privacy protections, and accountability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to connect literacy to business value
The framework is most useful when it is connected to real work rather than delivered as abstract tool training. The sources do not establish a proven rollout method, but they do support a practical planning principle: tie learning to roles, decisions, access rules, and organizational processes.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Start with a decision or use case. Identify a recurring decision, problem, or opportunity where better use of data might help. Name the people involved and the choice they need to make.
- Define value for the affected stakeholders. State what improvement would count, who benefits, and how it will be measured. Include possible costs or harms alongside expected gains.
- Set the literacy needed for each role. A frontline employee may need to interpret a prediction and know when to escalate it; a manager may need to question assumptions and balance outcomes; a data specialist may need deeper statistical, technical, and ethical expertise.
- Make safe use part of the learning. Explain what data people may access, how privacy is protected, what a model can and cannot conclude, and who is accountable when a result is challenged.
- Build the capability into normal processes. Connect learning to team discussions, decision reviews, and the systems people already use. Treat literacy as an ongoing organizational capability, not a one-time course completion.
- Review both adoption and outcomes. Check whether people can use the information appropriately and whether the chosen measures show the intended value. Revise the use case, process, or learning when evidence points to a problem.
What to look for in a literacy program
There is no program comparison or measured training outcome established by the cited sources, so the framework is more useful as a set of selection criteria than as a vendor ranking. When evaluating a course or internal initiative, ask whether it:
- Fits the learner’s role and the decisions that role actually makes.
- Covers privacy, responsible use, AI risks, and ethics—not just tool operation.
- Builds practical skill in interpreting data, statistics, and uncertainty.
- Connects analysis to use cases, organizational value, and stakeholder measures.
- Supports adoption through leadership behavior and workplace processes.
Further reading
Schmarzo identifies AI & Data Literacy: Empowering Citizens of Data Science as the basis for the framework. His article links to an Amazon listing, but current price, stock, formats, and other marketplace details are not established here.
Quick Recap
- Bill Schmarzo’s article on AI Digital News
- Wavestone’s announcement of the 2023 executive survey
- Randy Bean’s commentary on the 2023 survey
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
