Most of us agree that data is important in any organisation. We need to collect and analyse data to estimate the demand for our services, understand who our users are, find out which services are working for which people, and much more. In fact, there are few areas of work where the smarter use of data doesn’t make us more effective.

Despite this, much of the data held by local authorities or charities is under-utilised, often because it is inaccessible, messy, or managers and teams do not realise its full potential. Just getting this data together in one place, organising and making sense of it is a challenge in itself, before we even think about sophisticated analytics and data informed decision making.

Exactly how an organisation goes about adopting new data practices and changing the way they work with data is not easy. Like acquiring any new skill, using data better involves phases of progression, starting with the building blocks and moving up to more advanced stages. For an organisation embarking on a data project, understanding where they are in this journey is a helpful starting point which helps to calibrate tasks and objectives so they are both necessary and achievable.

This was the starting point for DataKind UK and Nesta, who both wanted to look at how social sector organisations and local authorities are embracing data, and sketch out what the journey towards data maturity looks like.

We did this through two projects. DataKind UK teamed up with Data Orchard to run the Data Evolution project - supported by Nesta - researching how charities and social enterprises work with data. Meanwhile Nesta ran the Local Datavores project, identifying pioneering uses of data in UK local authorities.

Taking inspiration from data maturity models developed in the private sector, we separately designed data maturity frameworks for our different audiences.

Why develop data maturity frameworks?

Data maturity frameworks are useful for a number of reasons. They:

• enable individual organisations to assess where they are in their data journey and plan the next steps in their evolution;
• ​make it possible for groups of organisations to benchmark their progress against one another;
• provide a way to diagnose the data needs, priorities, and capacities of organisations (which is necessary if, like DataKind UK and Data Orchard, you support charities and social enterprises to use data better);
• build awareness of the factors fundamental to data maturity, and create a shared framework and language to talk about it.

What’s data maturity and what’s a data maturity model?

Data maturity refers to how advanced an organisation is in the way it manages and uses data. For example, a less mature organisation might use data to take a sporadic, retrospective view of its operations and performance. By comparison, an advanced organisation may use real-time data to systematically inform everyday decisions in lots of different contexts.

Data maturity models began appearing a few years ago as companies adopted new data technologies. The models, similar to the one below, tend to show different stages in working with data that an organisation progresses through over time.

While there are data maturity frameworks for things like big data, or open data, we identified a lack of frameworks applicable to councils or charities. The frameworks developed by the Data Evolution and Local Datavores projects delve deeper than the corporate example above and provide detailed descriptions of each stage which are relevant to the charity and council sectors respectively. Both models cover five stages from unaware/nascent to mastering/ datavore, and identify many of the same key factors such as culture, skills and data quality.

Data Evolution’s data maturity framework for social sector organisations

After surveying 200 UK charities and social enterprises on how they use data and conducting in-depth interviews with 47 people from 12 social sector organisations, Data Orchard and DataKind UK developed a detailed data maturity framework. Each of the five stages is broken down according to the following themes: leadership, skills, culture, data, tools, uses and analysis. Below is an example of the stages for ‘Leadership’. See the whole framework here.

Unaware

• Not interested and do not invest in data. 
• Don’t use data for decision making, instead use experience and gut feeling.

Nascent

• Some awareness, don’t see the value. 
• Little investment. 
• Use data about what happened in the past and verbal accounts of what’s happening for decision making.

Learning

• Know data is important, but not entirely convinced. 
• Invest small amounts. 
• Business plan with some defined and measurable targets though data collection/ analysis may not align. 
• Might use past and current data for decision making with some simple trend analysis.

Developing 

• Becoming engaged as a whole.
• Beginning to commit significant investment.  
• Ask the right questions of their data, aligned to overarching business plan and desired impact. 
• Monitor what’s happening in the present plus past trends. Some exploratory forward-looking research and predictions.

Mastering

• Value, plan and prioritise data as a vital organisational resource. 
• Invest substantially in continuously improving data collection and analysis aligned. 
• Fully understand how to use data to improve what the organisation does. 
• Drive questions and influenced by what data tells them. 
• Use past, present and forward looking data for business planning and decision making.

Nesta's data maturity model for local government

The Wise Council report profiled a number of local authorities pioneering new uses of data. We wanted to use our research to understand how they had got more from their data, in order to help other local authorities use their data better. This generated a wealth of information about the different components of data use, working methods, organisational mindsets and the different considerations councils have when maximising the value of their data.

We saw a data maturity framework as a good means of turning these insights into a practical tool. To develop the framework we reviewed a range of data maturity frameworks, and combined this with our research to identify the categories of data maturity that are most applicable for councils. The themes we included are data management, governance, quality, use, skills and capability, and organisational culture towards data. The descriptions for each category and stage were based on what we had seen as best practice in the case study research, along with what could be considered a realistic description of councils mastering their data.

The table below shows the stages of maturity for ‘data management - collection’. The full data maturity framework can be found in the appendix of the Wise Council report.

Nascent

• Data collection is a by-product of operational and service delivery, and driven by central government requirements and key performance indicators.

Basic

• Collection goes beyond operational use and mandatory reporting requirements but there is little strategic purpose behind collection or use

Intermediate

• Data is used well in operational settings and data is sometimes collected for strategic purposes but predominantly there is little strategic rationale for collection and use.

Advanced

• Data is used well in operational settings and other data is collected in line with  broader organisational strategies and decision making.

Datavore

• Data is collected extensively across all services and in-line with organisational strategy. Data can provide a holistic view but data is not collected where the immediate use is not apparent (avoiding data exhaust).
• Data is seen as an organisational asset.

Call for feedback

We recognise that data is just one tool among many that can help an organisation become more effective. However, we believe data is a very powerful tool, and we would encourage staff in social sector organisations and local authorities to ask hard questions about data: What data do we have? Where is it? What do we use it for? Do we have the right skills to analyse it? Are we investing enough in it?

These data maturity frameworks aim to provide a starting point for these kinds of discussions, and we hope they can guide you as you move into the next stage of organsiational data maturity. If you have comments or thoughts on what’s useful here (and what’s not) then we would love to hear more.

Image credit: Kelly, via Flickr (CC license)