Capacities at a university tend to be complex and fairly diffuse. You are a complicated institution, after all. So when it comes to data – and particularly to using data to inform business strategy – it can be difficult to know where to start.

If you’re anything like most institutions, you probably have unimaginable amounts of data stored up in various departments. But data alone is not the same as insight. To make that data actionable, you need a quality framework that solves the complexity problem and drives meaningful outcomes for your institution. That’s why, this week, we’re bringing you a data strategy framework that prioritises developmental outcomes.

Introducing the Value Mode (VM) framework – a conceptual architecture for building data insight into your strategic decision-making. Developed by data consultants John Ladley and Thomas Redman, the value mode framework contextualises data by its outcome potential, allowing you to make smarter decisions about what to do with your resources.

The VM framework is a way of grouping the positive outcomes that could be driven by data insight. It consists of six modes, each of which is explored below in the context of the higher education space. Being outcome-led, the value modes offer you a format for turning existing data into actionable insight as well as identifying data needs and opportunities. Let’s take a look.

This crucial value outcome is central to any HE strategy. Your competitive position is best understood as a product of brand equity, programme demand and the efficacy of your marketing strategy. Each of these factors will require their own analysis to draw insight. However, once you understand your current position in the market – and, crucially, why your position is the way it is – you can begin to build a strategy towards improving your market position. Quantitative data is the go-to insight driver here, but don’t forget the importance of qualitative analysis in understanding the big ‘whys’ of market position.

Insight around new or improved product opportunities is derived from customer and market position data. The challenge is in identifying gaps in the market, trends and growing areas of demand. Internal data sources probably won’t be enough here – you’ll need to source secondary data to provide insight into the market as a whole. Remember that meta factors such as climate change and the COVID-19 pandemic will have a meaningful impact on market opportunities. Armed with this insight, though, you’ll have the chance to create successful new products or innovate on your existing offering.

Often the first port of call for early-phase data strategies, informationalization brings you data on how products and services are being used. In universities, the main challenge is bringing together data from the range of different departments and services you have. That process, though, is crucial to operationalising your data. A simple example could be that, by analysing data from your wellbeing services, you find a higher rate of stress-related enquiries from students on a particular course, suggesting problems with that curriculum. Those kinds of insights can’t be drawn until you have built a cross-departmental view. Remember that greater insight is a value outcome in itself, as well as being the foundation of the other value modes.

This value mode mostly reflects value gained from cost savings such as money, time and resources. However, improved processes can also generate less obvious value outcomes that can impact other value modes, such as your market position. A good example of this might be the success of the switch to digital learning, which has opened up programs to bigger markets for some institutions. Operational efficiencies are usually derived from insight gained during the first phase of informationalization. Remember, the search for operational roadblocks and inefficiencies is an ongoing process. Things can always work better.

Improving human capabilities is a solution-focused process – you’ll need to seek out the challenges and inefficiencies faced by your team and find solutions to them. Qualitative data from your staff is the foundation. Take the considerations of your staff into account and use broader data to see the extent to which the issues highlighted apply on an institutional level. You might end up finding solutions to problems you didn’t even know you had.

All good risk management in the higher education space occurs within a quality risk management framework. Data is the key to filling out that framework with actionable insight that can help you make your organisation safer. Again, though, you’ll need to look beyond your internal data to find the solutions you need. Combine your own historical data with secondary sources to identify likely threats and prevention opportunities. With proper implementation, you can create a safer environment and a more secure business proposition.