Building on data literacy foundations across a global workforce
Updated: 11 hours ago
How Alirity worked with a global consumer goods business to take its data literacy learning beyond the foundations, with experienced and function-specific pathways built around the dashboards and decisions in each team’s role.
Stakeholder interviews
Leadership across the business shared how their teams use data and where they needed support.
Modules
An experienced tier and function-specific pathways, building on the foundational data literacy learning.
bespoke learning videos
Short videos designed for colleagues to learn in their flow of work.
Copilot agent
Guiding learners to the content best suited to their literacy level and learning objectives.
The Context
Most organisations now hold more data than their people can comfortably use. Dashboards and reporting tools are part of every function, and their value depends on whether people can interpret the figures in front of them and act on what they show.
AI has made this more pressing. The value an organisation gets from AI rests on the quality of its data, which in turn relies on people who understand how that data is recorded and used. Data literacy and sound data practices are now essential for any organisation that wants to use AI well.
A global consumer goods company had already begun building the data capability of its workforce. The foundational tier of its data literacy learning, developed with Alirity, was well received across the business and gave colleagues a common grounding in working with data.
The next step was to take that learning further. Alirity worked with the business to develop an experienced tier and function-specific learning, connecting the skills colleagues had gained to the dashboards and decisions in their own roles.
The Problem
Confidence in interpreting data and turning it into action varied widely across teams. Stakeholders described fragmented reporting and multiple definitions for the same KPIs, and many teams relied on Excel or a small number of “data wizards” to answer their questions. This slowed decisions and reduced trust in the numbers.
Some challenges were specific to parts of the business. In HR and other functions, incomplete system inputs limited what could be reported, making it difficult to answer basic leadership questions consistently. Markets with weaker data availability relied on lagging or partial sources, which made performance harder to explain and led to the same reports being reworked.
Left unaddressed, these issues would continue to cost the business time through duplicated reporting and lead to inconsistent decisions across regions.
The Approach
Alirity began by interviewing 65 stakeholders across functions including HR and Talent, Finance, Marketing and Supply Chain. These conversations identified the moments when people use data in their roles, and the KPIs and dashboards each function relies on most.
These insights informed a pathway model for the learning. The experienced tier builds on the foundational content for colleagues ready to deepen their data skills, moving beyond core concepts to interpreting dashboards and the metrics behind them. Function-specific pathways then apply those skills to use cases drawn from each department’s own work, using the reports and data sources colleagues already rely on. This makes the learning immediately applicable and builds more consistent ways of working with data across the business.
Subject matter experts across the business were involved throughout development, with Alirity managing structured review cycles to keep each pathway accurate as it took shape. Each pathway combines short videos and knowledge articles with quick reference guides and curated links, giving colleagues practical support they can draw on in their day-to-day work. The learning consistently reinforces agreed definitions and a single source of truth, helping colleagues identify which reports and figures to trust and reducing the business’s reliance on a small number of data specialists.
A SharePoint site built by Alirity brings the pathways and supporting resources together, giving colleagues one central place to access their learning. A creative theme drawn from the company’s products runs throughout, and the diagrams and templates were designed in the client brand, maintaining continuity with the foundational tier. A Copilot agent guides learners to the content most relevant to their literacy level and learning objectives, helping each person progress through the programme at their own pace.
The Outcome
The business now has a scalable learning programme that takes colleagues from the foundations of data literacy into the data and decisions specific to their roles. With clearer guidance on interpreting metrics consistently and locating trusted reporting, departments can turn their data into decisions more quickly.
Stakeholders reported stronger agreement across functions on what good looks like, along with less time spent reconciling duplicated reports or waiting on individual specialists for answers. Together, these changes support faster decision-making and greater trust in reporting across the business.
A network of stakeholders is now in place to sustain and expand the programme, supported by reusable templates for future content. The programme has also laid the foundations for a dedicated AI literacy programme developed with Alirity, helping the business realise more value from AI as adoption grows.
Data Literacy FAQs
What is data literacy?
Data literacy describes how well people can make sense of the data they work with and use it to make decisions. In practice, someone with strong data literacy knows how the metrics in their reports are defined and when a figure needs checking before it is acted on. When that understanding is shared across an organisation, teams in different functions can work from the same numbers and reach consistent conclusions.
Why is data literacy important for organisations?
Most organisations rely on data to plan and make decisions, and its value depends on how well people understand it. When data literacy is low, teams often fall back on spreadsheets or a few experts, which weakens trust in reporting. Building data literacy across a workforce gives teams a shared understanding of their numbers and the confidence to act on them.
What are the different levels of data literacy and how do they differ?
Data literacy develops in stages, from a basic understanding of data through to applying it confidently in everyday decisions. The right levels depend on where an organisation is starting from and what its people need to do with data. For the client in this case study, Alirity initally defined two tiers. The foundational tier introduced core concepts and a shared language for working with data, and the experienced tier then built the skills to interpret dashboards and metrics with confidence, supported by function-specific pathways tied to each team’s own KPIs.
What should a data literacy programme include?
The right content depends on the organisation, how it currently uses data and its ambitions for the future. Most programmes cover how to interpret key metrics and where to find trusted reporting, supported by clear definitions and a single source of truth. Self-paced formats such as short videos and quick reference guides let people learn around their roles, and examples drawn from the organisation’s own reports make the learning easier to apply.
Why tailor data literacy training to different departments?
Each department works with its own data and reporting, so the way people need to use data varies across a business. A finance team working with margins and forecasts has different needs from an HR team working with workforce data, and training that reflects those differences is easier to put into practice. Tailoring the learning by department also helps standardise how figures are defined and reported, reducing the inconsistencies that build up when teams work in isolation. In this case study, function-specific pathways built on a shared experienced tier, giving every department the same starting point before moving into its own data
Why is data literacy important for AI adoption?
AI tools rely on data, and the quality of what they produce depends on the data behind them. People with strong data literacy are better placed to judge an AI output and recognise when the underlying data may be incomplete. This is why data literacy is often treated as the first step towards responsible AI adoption.
How do you measure the success of data literacy training?
The clearest measure is whether people use data differently once they have completed the training. Organisations can look for fewer ad hoc report requests to data specialists and more consistent use of agreed KPI definitions across departments. Asking learners to rate their confidence before and after the programme adds another view, and feedback from leaders shows whether the reporting they receive has become more reliable.
Strengthen how your organisation works with data
Data literacy grows fastest when the learning reflects the reports and decisions people work with every day. If you would like to explore what that could look like for your teams, get in touch and we can start with a conversation.
