How data literacy, evidence-backed decision-making, collaboration, and continuous learning are embedded in the organisation's culture.
CUL-CON-01
Personal data literacy
How well equipped do you feel to interpret data correctly in your work — reading charts accurately, understanding measures and their limits, and recognising when a conclusion is not supported by the data?
Maturity level descriptions
No expectation or support exists for business-user data literacy; the Data Consumer’s ability to interpret data is entirely a matter of individual background. Data is interpreted on instinct, with misreadings going unrecognised and uncorrected.
Literacy is recognised as desirable, and some individuals have sought their own development, but nothing is provided or expected organisationally. The Data Consumer has developed interpretive skill through personal effort, and colleagues vary widely with no organisational response.
A defined data literacy expectation and supporting development offering exist for business roles, communicated and accessible. The Data Consumer knows what level of data literacy their role requires and has access to development that addresses it.
Literacy levels are assessed and tracked across the business population, development is targeted at identified gaps, and improvement is reported. The Data Consumer’s literacy has been assessed, development targeted accordingly, and progress reported over time.
Data literacy is a defined organisational capability, continuously developed, embedded in role expectations and performance conversations, and treated as a business outcome. The Data Consumer’s data literacy forms part of role expectations and development conversations, and is sustained through continuous learning.
CUL-CON-02
Participation in literacy programmes
To what extent do you and your colleagues actually participate in and complete the organisation’s data literacy and learning offerings, as opposed to them being available but unused?
Maturity level descriptions
No data literacy programme exists to participate in. No organisational data learning offering is available to the Data Consumer.
A programme exists but participation is negligible and untracked; the Data Consumer may be unaware of it. Material exists somewhere but is rarely used, and no one knows how many business users have engaged with it.
Participation is expected for defined business roles, communicated, and completion is recorded. The Data Consumer knows which learning is expected of them and their completion is recorded.
Participation and completion are measured and reported, with follow-up where completion lags and content adjusted on the basis of feedback. The Data Consumer’s area has visible completion rates, gaps are followed up, and their feedback shapes the content.
Learning is continuous and role-relevant, with participation sustained, effectiveness measured by changed practice rather than completion, and pathways evolving with organisational need. The Data Consumer engages in ongoing learning whose effect is measured by how their work changes, not by attendance.
CUL-CON-03
Evidence-backed decision-making
In your team, how consistently are decisions actually supported by reference to data, as opposed to being made on intuition, precedent or seniority with data cited afterwards if at all?
Maturity level descriptions
Decisions are made without reference to data; the Data Consumer is not expected to bring evidence and none is requested. Business decisions proceed on judgement and precedent alone, with no expectation that data will be consulted.
Data is referenced inconsistently, often selectively to support a position already taken, with no expectation of rigour. The Data Consumer sees data invoked to justify decisions after they are made, rather than to inform them.
A defined expectation exists that significant decisions are supported by data, with the evidence documented in decision records. The Data Consumer prepares supporting data for significant decisions and it is recorded as part of the decision.
Evidence use in decision-making is monitored, decision quality is reviewed against outcomes, and gaps in available evidence are identified and addressed. The Data Consumer’s decisions are reviewed against outcomes, and where evidence was missing that gap is raised and addressed.
Evidence-based decision-making is the established norm, continuously reinforced, with decisions and their outcomes systematically compared to improve both practice and the data available. The Data Consumer works in an environment where decisions are routinely evidenced and outcomes fed back to improve both practice and data provision.
CUL-CON-04
Access to expert help
When you need help with data — interpreting a result, finding the right source, or building an analysis beyond your own capability — how readily can you get expert support?
Maturity level descriptions
No support is available; the Data Consumer manages alone or abandons the question. There is no one to approach for data help, so questions go unanswered and analyses go unattempted.
Help depends on knowing a willing individual; availability is unpredictable and unevenly distributed. The Data Consumer relies on a personal relationship with an analyst, and colleagues without such a contact go without.
A defined support route exists for business users needing data help, is communicated, and is available to all. The Data Consumer knows where to go for data help and can reach it regardless of personal contacts.
Support demand and responsiveness are measured, capacity is matched to demand, and recurring questions inform self-service improvement. The Data Consumer receives help within a stated expectation, and recurring questions lead to better documentation or tooling.
Support is embedded and proactive — data expertise is available within business teams and demand falls as capability and self-service improve. The Data Consumer has data expertise available within or alongside their own team, and needs it less over time as capability grows.
CUL-CON-05
Collaboration with data specialists
How effectively do you and the organisation’s data specialists work together — as genuine collaborators on a shared problem, rather than as separate groups exchanging requests and deliverables?
Maturity level descriptions
Business and data teams operate entirely separately with no meaningful interaction; the Data Consumer has no contact with data specialists. Work is handed over in one direction with no shared understanding of purpose or constraint.
Interaction is transactional and request-based, with limited shared understanding and frequent rework. The Data Consumer submits a request, receives a deliverable that misses the point, and iterates by exchange rather than conversation.
Defined collaboration practices exist — business users are involved in scoping and reviewing analytical work as a documented part of the process. The Data Consumer participates in scoping discussions and reviews outputs before they are finalised.
Collaboration is sustained and measured, with joint working arrangements, shared objectives and rework or satisfaction tracked. The Data Consumer works with data specialists under shared objectives, and the effectiveness of that collaboration is measured.
Business and data expertise are genuinely integrated, with joint accountability for outcomes and continuous refinement of how the two work together. The Data Consumer and data specialists share accountability for business outcomes rather than for their respective deliverables.
CUL-CON-06
Trust in organisational data
How much do you and your colleagues genuinely trust the organisation’s data and reporting when it matters — and is that trust actively understood and managed?
Maturity level descriptions
Organisational data is widely distrusted; the Data Consumer maintains their own figures and treats official reporting as unreliable. Local spreadsheets are regarded as the real numbers, and official reporting is disregarded in significant decisions.
Trust varies unpredictably by source and individual, based on past experience, with no organisational understanding of where confidence is lacking. The Data Consumer trusts some reports and not others for reasons never articulated or examined organisationally.
Trust in data is recognised as an organisational concern, with certification, transparency and communication deliberately used to build it. The Data Consumer can identify which assets are certified and understands why they can be relied upon.
Business-user confidence is measured, reported and acted upon, with declines investigated and addressed. The Data Consumer is periodically asked about their confidence in the data they use, and low confidence prompts investigation.
Trust is high, sustained and continuously managed, with confidence treated as a key measure of the data function’s effectiveness and shadow data practices rare. The Data Consumer relies on official data as a matter of course, and maintaining parallel local figures has largely ceased.
CUL-CON-07
Continuous learning pathways
How well supported is your ongoing development in data skills beyond initial training — keeping pace with new tools, techniques and organisational data practice?
Maturity level descriptions
No ongoing data skills development is available to business users beyond whatever they arrange themselves. The Data Consumer’s data skills remain static, with no development available as tools and practice change.
Development happens occasionally and reactively, typically when a new tool is introduced, with no pathway or continuity. The Data Consumer receives a session when a tool is rolled out and nothing thereafter.
Defined learning pathways exist for business roles, covering progression beyond introductory skills, and are communicated. The Data Consumer can see a pathway describing how to build data capability further and how to access it.
Development participation and capability growth are tracked, pathways are refreshed as tools and practice change, and time to learn is actively supported. The Data Consumer has supported time for development, their progression is tracked, and content stays current.
Learning is continuous and self-directed within a supported framework, with peer learning and communities of practice sustaining capability as practice evolves. The Data Consumer learns continuously through communities of practice and peer exchange, supported rather than directed by the organisation.
CUL-CON-08
Leadership behaviour and reinforcement
How consistently do the leaders you work with model and reinforce data-informed behaviour — asking for evidence, engaging with it seriously, and acting on it even when it is unwelcome?
Maturity level descriptions
Leaders neither request nor engage with data; the Data Consumer observes decisions made without reference to evidence. Bringing data to a leadership discussion has no effect on the outcome and is not expected.
Some leaders engage with data and others do not; behaviour depends entirely on the individual with no organisational expectation. The Data Consumer adapts their approach depending on which leader they are dealing with.
A defined expectation exists that leaders use data in decision-making, communicated and visible in decision forums. The Data Consumer sees leaders consistently asking for supporting evidence in decision forums.
Leadership data behaviour is observed and reinforced through governance and performance mechanisms, with accountability for evidence-based decisions. The Data Consumer observes leaders being held accountable for whether decisions were evidenced, not only for outcomes.
Data-informed leadership is embedded and self-sustaining, with leaders actively seeking disconfirming evidence and visibly changing course on it. The Data Consumer can point to a specific instance of a leader changing a decision because the evidence did not support it.
CUL-CON-09
AI literacy and confidence
How well prepared are you to use AI and GenAI capability appropriately in your work — understanding what it is good at, where it fails, and what your responsibilities are when using it?
Maturity level descriptions
No AI literacy support exists; the Data Consumer’s understanding of AI capability and risk is entirely self-acquired. AI tools are used or avoided based on personal impression, with no organisational understanding of capability or risk.
General awareness messaging has been issued, without practical guidance on effective or responsible use. The Data Consumer has received broad statements about AI without practical guidance for their own work.
Defined AI literacy content exists for business roles, covering capability, limitations and user responsibilities, and is communicated. The Data Consumer has completed practical AI guidance covering what the tools do well, where they fail, and what they must check.
AI literacy is assessed and tracked, content is updated as capability changes, and responsible-use practice is monitored. The Data Consumer’s AI literacy is assessed, kept current as tools change, and their practice is observed and supported.
AI literacy is continuous and embedded in how business users work, with guidance available in context and practice improving measurably as capability evolves. The Data Consumer receives guidance within the tools themselves and their responsible-use practice measurably improves over time.