Separating data culture from data maturity

We often assume that data mature organisations have a positive data culture. This isn’t always the case and it’s important to separate data maturity, data literacy, and data culture from each other to understand the role that each one plays in an organisation’s data journey.

As part of the strategic data use programme I recently ran a workshop with ACF members on fostering a positive data culture in nonprofit organisations. This blog is a write up of some of the key points, but let me know if you’d like to chat about data cultures and approaches for fostering a positive data culture, or if you’re working to improve the data culture in your organisation and you’d like to see the slides from the workshop.

Defining data maturity, data culture, and data literacy

All three of these terms are fairly new (in the grand scheme of things) and so their definitions and usage are still in flux. I think these are the most broadly shared and helpful definitions:

Data maturity is the extent to which an organisation is able to utilise its data to extract meaningful insights that drive decision-making. It’s about the extent (that is frequency, embeddedness) to which ‘accurate’ and actionable insights are used to inform the direction of the organisation.

Data culture is the values, behaviours, and norms shared by most individuals within an organisation with regard to data-related issues1. It’s about the attitudes and expectations regarding data, and it includes a normative element: is data use supported and expected by senior management and others within the organisation?

Data literacy is the ability of individuals to read, interpret, and use, data effectively. It’s about individual skills and capacity. The definition of critical data literacy expands this definition slightly to include the ability to read, work with, argue with, and produce data2. An important addition to this is data infrastructure literacy, which is an understanding of the wider system in which data is produced, analysed, and shared3.

In my opinion, data culture and data literacy are quite closely linked and that they both likely determine how data mature an organisation is (that is, the extent to which decisions are based upon meaningful data insights).

The necessary distinction between data culture and maturity

In working with different organisations on their data use, and in pulling together the material for this workshop, I’ve realised that:

  • When we say data culture, we implicitly mean a positive data culture, but that actually organisational cultures exist on a spectrum and data cultures are no different. A negative data culture isn’t simply the absence of one. An organisation’s data culture is a symptom of its wider learning culture, value sets, and norms. If we don’t discuss what a negative and positive data culture looks like, we won’t be able to identify why people feel like they can’t say they don’t understand the data they’re seeing, or suggest that a service improvement is made based upon the evidence. It’s important to recognise that both negative and positive data cultures exist.
  • We often assume that an organisation which we may call mature in its data use possesses a positive culture. This relationship isn’t guaranteed. An organisation may be quite mature, but have a negative culture; if you use data to drive decisions, but the process behind making these decisions is opaque, staff feel unable to question the data they’re seeing, or the data use is unethical, then they’re sure signs of a negative data culture. The converse is that staff may not have much by way of data literacy skills and few decisions are based on meaningful evidence, but that the senior leadership team is setting out a roadmap for improving data literacy and openly welcomes an environment where staff can being to experiment with data use and use data to question practice.

In order to diagnose issues and realise improvements, it’s important that we distinguish between data maturity and data culture, and recognise that data cultures exist on a spectrum. Whilst all three terms (culture, maturity, and literacy) are both distinct and related, I think it’s the distinction between maturity and culture that is most in need of reinforcement.

Fostering a positive data culture

What a positive data culture looks like will vary by organisation, but I think a positive data culture in nonprofit organisations might look like:

  • A clear commitment to learning from the leadership team, and particularly the use of data in learning and adapting practice
  • All staff feel welcome to engage in evidence and learning activities
  • A culture of supporting each other with data use
  • Engaging with a wide range of evidence (based on different experiences and voices)
  • Sharing learning about the use of data with other organisations

To change culture, we need to change the values, behaviours, and norms observed in organisations. Whilst we can change culture through strategies and frameworks, and changing organisational structures and skillsets, I think changing behaviour represents the fastest route to fostering a positive data culture.

To change behaviours, we have to consider someone’s capability, their motivation, and the opportunities they have for behaving differently.

I’ve pulled together a list of 5 group activities and 6 longer term projects that will help foster a positive data culture by changing behaviour. I’ve mapped them below (in a completely subjective way, and broadly based upon my own experiences!). Please do get in touch if you want to discuss these approaches in more detail.

A few final tips for fostering a positive data culture include:

  • Influence with data you don’t yet have. This is taken from The Service Organisation, where Kate Tarling recommends mocking up data to demonstrate a point, or show what can be done if certain types of data were collected. Of course, be careful not to mislead, but it’s an interesting suggestion.
  • Make the ‘hook’ really clear; be explicit about what’s in it for other members of your organisation. Is it getting to see what data we hold, or a chance to ask questions about datasets they don’t understand?
  • Link the work directly back to strategy. This makes the importance of it hard to refute.
  • Emphasise strategy, sustainability, and stewardship.
  • Appoint a ‘data champion’ at a senior level early on in the process.
  • Draw on learning from other organisations and hook into relevant forums.


I’ve included some resources under the ‘culture’ section of the resources tab. For further resources (including a ‘how to’ guide for the 13 ‘approaches’ mapped out above), please get in touch.

1: Kremser, W., & Brunauer, R. (2019). Do we have a Data Culture?. In Data Science–Analytics and Applications: Proceedings of the 2nd International Data Science Conference–iDSC2019 (pp. 83-87). Springer Fachmedien Wiesbaden.

2: Fotopoulou, A. (2021). Conceptualising critical data literacies for civil society organisations: agency, care, and social responsibility. Information, Communication & Society24(11), 1640-1657.

3: Gray, J., Gerlitz, C., & Bounegru, L. (2018). Data infrastructure literacy. Big Data & Society5(2), 2053951718786316.

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