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Data leadership starts with people, not dashboards. The five-step approach associated with The Human Impact of Data Literacy connects a measurable outcome and accountable sponsorship with a realistic assessment of skills, role-appropriate tools, continuous learning, and a culture that regularly checks whether data is improving decisions.
What the 2020 discussion was about
A Data Science Central podcast discussion examined Accenture–Qlik’s The Human Impact of Data Literacy report with Jordan Morrow, then Qlik’s Global Head of Data Literacy. A related Qlik Community announcement scheduled the webinar for March 25, 2020. The episode description presented data literacy as a workforce and leadership issue rather than a software-installation project.
The episode description cited a potential opportunity of up to $500 million from the Data Literacy Index, commissioned by Qlik and conducted by IHS Markit, PSB Research and academics from the Wharton School at the University of Pennsylvania. The description does not state what outcome that figure values, the measurement year or the methodology, so it should be treated as an attributed, qualified estimate—not a forecast of every organization’s return.
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What data literacy means for leadership
In this context, data literacy is the ability to read, interpret, question and use information in a decision. Leadership adds the organizational conditions that make that ability useful: a clear result to pursue, access to relevant information, tools that fit employees’ work, support for learning and permission to act on evidence.
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Buying a business-intelligence platform or sending staff to a single class cannot establish those conditions on its own. A useful program ties learning and technology to actual decisions, assigns ownership and checks whether behavior and results change.
Two related five-step frameworks
The sources present two versions of the sequence. The Kogan Page article, attributing its framework to work with the Data Literacy Project, uses outcome, strategy, tools, learning and culture. Morrow’s 5 Steps for a Human-Centered Data Strategy, published by Government Technology on July 23, 2020, uses a data champion, preparation, suitable tools, education and reassessment. They overlap, but they are not one verbatim list.
| Kogan Page sequence | Morrow’s public-sector wording | Common leadership question |
|---|---|---|
| Set the outcome | Appoint a data champion responsible for tangible results | What decision or result is the program accountable for? |
| Set the strategy | Get prepared by assessing current practice and barriers | What happens today, and what prevents better decisions? |
| Set the tools | Give employees suitable tools | Which access and workflow support each role? |
| Set the learning | Educate employees through ongoing learning | What capability must people build and maintain? |
| Set the culture | Keep reassessing skills, access, tools and opportunities | How will the organization reinforce and adapt the behavior? |
The practical sequence below is an editorial synthesis of those related frameworks, not a quotation or an official replacement for either list.
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The five steps in a human-centered data strategy
1. Define the outcome and assign ownership
Start with a decision that matters: for example, reducing processing delays, improving service allocation or identifying a preventable cost. State the baseline, target, time frame and decision owner. A senior sponsor should remove cross-department barriers, while a named data champion remains responsible for tangible progress.
- Write the outcome in operational terms, not “become data-driven.”
- Name the people who decide, supply data and are affected by the decision.
- Specify how success will be observed and when it will be reviewed.
2. Assess the starting point before prescribing fixes
Map how decisions are actually made. Examine which data employees can access, how reliable and timely it is, what tools they use, and where confidence or skill breaks down. Include role differences: an analyst, frontline worker, manager and executive do not need the same training or interface.
Morrow’s public-sector article reports that 45% of public-sector respondents felt empowered in their organizations to make better decisions with data. It also reports that 45% felt overwhelmed and unhappy at least weekly when reading, working with and analyzing data, while 23% said they had avoided a data task because they felt overwhelmed. These figures are attributed to The Human Impact of Data Literacy as reported by Morrow; the report’s sample size, field dates and full question wording were not available in the cited material. They should not be generalized to all employees or to current conditions.
3. Match access and tools to the work
Provide information and interfaces that are relevant, consumable and embedded in existing practices. A role may need a simple operational view, a guided exploration workflow or governed self-service analysis—not necessarily the most powerful tool available.
- Give each role the minimum data access required for its decisions, with appropriate security and privacy controls.
- Use definitions, ownership and refresh information so users can judge whether a metric is fit for purpose.
- Place analysis where work occurs, such as a case-management or planning workflow, instead of requiring a separate ritual.
- Test whether users can complete a real task; feature count is not evidence of usefulness.
Business-intelligence and data-visualization software can support this step, but no particular vendor is required and software alone does not create data literacy.
4. Build learning into workforce development
Education should be continuous and role-specific. Begin with the decisions identified in the assessment: interpreting measures, spotting misleading comparisons, asking better questions, understanding uncertainty or explaining a recommendation. Reinforce instruction with examples from employees’ own work, coaching and time to practice.
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Morrow’s article reports that 35% of public-sector employees believed data-literacy training would help them be more productive. That is a reported perception from the same 2020 source context, not proof that a particular course or provider produces a measured productivity gain.
- Offer short learning activities at the point of need, supplemented by deeper pathways for specialists.
- Teach managers how to challenge assumptions and discuss evidence, not just how to operate a tool.
- Provide help channels, peer communities and refreshers when definitions, systems or responsibilities change.
5. Reinforce the culture and reassess repeatedly
Culture is visible in routine decisions: whether leaders ask for evidence, whether employees can question a metric safely, and whether teams act on findings. Establish recurring reviews of skills, access, tools, data quality, decision behavior and outcomes. Use the results to adjust sponsorship, permissions, interfaces and learning rather than treating the initial rollout as finished.
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- Review the target outcome and decision process at a scheduled interval.
- Check usage and observed behavior, not only course attendance or login counts.
- Collect examples of decisions improved, delayed or avoided because of data.
- Remove barriers and repeat the assessment when roles, regulations, systems or priorities change.
How to put the framework into practice
Create a one-page charter
Record the outcome, accountable sponsor, data champion, affected roles, decisions in scope, baseline measures, access constraints, learning needs and review date. This keeps a literacy initiative tied to work rather than becoming an abstract skills campaign.
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Run a small decision pilot
Choose one workflow with a visible owner and a manageable number of users. Document the current process, provide the least-complex suitable view or tool, coach users during real decisions and record what changed. A pilot should test access, definitions, workflow fit and confidence together.
Measure capability and results separately
Track outcome indicators such as cycle time, error rate or service quality alongside capability indicators such as successful interpretation, appropriate data use and the ability to explain a decision. Separating the two prevents a completed training module from being mistaken for business impact.
Common failure modes
- Starting with procurement: a platform cannot resolve unclear outcomes, missing ownership or inaccessible data.
- Using one curriculum for everyone: generic instruction ignores role-based decisions and creates avoidable overload.
- Confusing access with empowerment: permission to open a dashboard is not the same as trusted definitions, context and authority to act.
- Declaring success after launch: without reassessment, new systems and changing roles can quickly recreate the original barriers.
- Publishing unsupported precision: historical survey percentages require their source, date and limitations; they are not universal workforce benchmarks.
What the 2020 evidence can—and cannot—establish
The cited materials explain a human-centered framework and report perceptions from a 2020 public-sector discussion. They do not establish present-day workforce conditions, a current vendor offering, a guaranteed return, or a universal benchmark for data-literacy programs. Organizations should use the framework to diagnose their own decisions, capabilities and barriers before selecting training or technology.
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