Why data maturity is crucial to the further education sector

For many working in further education data, the daily struggle is familiar: juggling information from multiple systems, meeting tight reporting deadlines and the age old – “how many learners do we have?” (the answer often depends on the individual’s definition of a learner – more on meta data later). Whilst not a silver bullet to these demands, the Jisc Data Maturity Framework is designed to help you cut through these challenges by giving you the tools to create a clear, practical roadmap to understand your current data environment and identify where improvements can deliver enterprise solutions and long-term benefits.

The aim should always be for FE institutions to manage data as an asset to support the effective delivery of many key priorities. Improving data maturity sounds complicated at first, but the Data Maturity Framework helps you break down your institution’s data culture and practices into manageable, actionable areas. It helps you evaluate key components, broken down by five elements:

Decision making

Decisions are made across the organisation on an almost minute by minute basis. But how informed are the decisions and what evidence does the individual have to ensure it is the correct one? This goes to the very heart of data maturity where an organisation’s culture could either hinder or enhance the operational efficiencies on the ground. By evaluating the data management and culture around data, you can encourage all decision makers to see it as a trusted asset and determine if your data is there to validate or challenge your decisions.

Data Governance

Whilst data governance and data quality go hand in hand, they can be overlooked and seen as just policy guidance or documentation. In practice they are the network and flow of your data through your organisation. Think of it as a bit like the management of water coming from your taps at home. Governance ensures that the right people with the right tools are responsible for maintaining and protecting the network it travels around. Quality ensures it doesn’t get contaminated and that the input is the same quality as the output. Encourage people to see data like any other asset in your business. It needs to be maintained for people to see its value.

Reporting

When you consider the number of both internal and external consumers, it’s easy to understand why there is such complex and diverse demands on the reporting offer. It’s common to see significant challenges around the transformation of the huge volume of raw data available into meaningful information that can be easily understood and utilised by such a broad audience. This often translates into inefficient manual workarounds even if automated reporting exists elsewhere.

Processes & systems

The above puts further pressures on what can sometimes already be outdated and fragmented systems that hinder data flows, leading to, at best, inefficiencies and at times, incorrect information being used to inform critical decision making. To have informed and effective decision making, consumers need confidence in the information being relayed. Can you point to a single source of truth and does your team understand master, reference and meta data and why you need them?

Data strategy

It still feels quite novel to have a defined data strategy in place. It could even be part of your digital strategy. Whether you have one, are thinking about one or not in that space yet, the implementation is just as critical as the definition. It’s common to face difficulties in defining clear objectives whilst ensuring that data initiatives support broader outcomes, notably learner ones. And whilst significant coordination and investment is required, collaboration, skills and knowledge growth are as crucial as any financial commitment.

Next steps and AI

No conversation around data would be complete without touching on AI – data maturity is no different. In fact, it is critical. By understanding and improving your data maturity, you’re not just improving day-to-day operations—you’re laying the groundwork for a transformative future. With reliable, high-quality data as your foundation, providers will be better positioned to adopt advanced technologies like AI, foster data-driven decision-making, and ultimately enhance outcomes for staff and learners alike.

Alongside the data maturity framework, we have also developed a self-assessment which is currently being piloted in both the FE and HE sectors. Whilst the challenges are common, one thing we have seen in the results so far is that maturity levels are slightly higher in FE than HE. There is some way to go with the data and analysis of the pilots, but I think this points to the suggestion that the FE sector is sufficiently dynamic and agile to identify and action some of the key areas of development. This gives me hope and underlines the role data maturity can play in the development and improvement so critical to the long-term future of the sector.

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