Data as an asset

Data is an abstract representation of something else and, unlike physical assets, can be copied and distributed widely. It also has to be processed into information in order to add value and without it, organisations struggle to articulate and meet their strategic aims. For example, if a university has an ambition to grow their number of students by X amount, this can only be achieved with action tied into insight:

• How do you know which programmes are good candidates for growth and which need to shrink or close?
• How do you understand the characteristics of prospective students to target recruitment activities?
• How do you understand your strengths and weaknesses to try to improve league table performance?

All of these questions require data to understand what the right decision is, but how can you be confident in the quality of your decisions if you don’t understand the quality of the data you’re using to make them?

Just as you would with physical assets, there are steps you can take to ensure your data is fit for purpose. For example, if you are buying a computer, you would expect a technical specification to ensure it meets requirements – but you can similarly create specifications for your data and put processes in place to monitor that those standards are being met. All too often however, this is left to chance and organisations cannot make well-informed decisions about what data, and data processes, are worth investing in - or will add the most value - because they don’t have a sufficiently detailed understanding of what and where their data is, and who is responsible for it.

Poor data management means that system implementation is much harder; just as a building has a blueprint, so should the data landscape. It takes more time and resources to build something without this blueprint, and the implementation of a new system is a huge task which can be made easier if processes are already mapped out, data architecture has been documented, and staff are well-versed in data use and business rules. Architecture has to be documented in order to be understood and useful - you cannot architect after implementation, just as you wouldn’t build the top of a skyscraper before laying the foundations.

As well as the risk to operational efficiencies and strategic insights, businesses have the added hurdle of meeting external regulatory data requirements. Poor quality data returns by universities can result in audit, fines, and, in extreme cases, removal of degree awarding powers. In addition, incorrect student data can result in incorrect funding levels, leading to claw back of funds by the Office for Students. Poor quality student data can also decrease league table positions and in the worst cases cause reputational damage.

Well managed data also gives a level of assurance in terms of cyber security, knowing where your sensitive data is held and that it is being managed properly. When data quality is enhanced, you know that the information you are being provided via business intelligence is trustworthy and you can gain more interesting and timely insights. The experience of staff is improved via more automated business processes that remove the manual data entry that takes up too much time, and access to the reports and systems that provide the data they need to do their jobs well. Data management is not just about security, or quality or accessibility, it also ensures that the foundations are in place so that when you want to innovate with data you can, anything from investigating new market areas and creating new products to fulfil customer demand.

But if data is an asset, how do we quantify its value? The practice of giving a real monetary value to data is still in its infancy, not least because most organisations do not understand what they are spending in terms of time or technology to enter, process, store and gain insight from their data.

If we were to think about the value of student data as an example: a student cannot participate without a student record, so at a minimum it is worth a student’s fees, but the creation and maintenance of that record requires input from many different departments, and from the student themselves. Beyond these operational realities lie questions of the strategic value of data – what would be the financial impact to a university if it only grew by a quarter or a half of the targets that have been set?

A data management strategy will set out the starting point for improvement activities and can cover many themes such as data governance, data quality, technology and data culture. This guidance should be the cornerstone of a digital organisation. For universities in particular, this strategy should also be created with regard to many of the Jisc data management activities recommended in the framework for digital transformation in HE.

An initial activity recommended by Jisc is to develop a digital/data strategy, which is a great starting point, but it is important to note that the strategy should not be a one-off document, but a pattern of ongoing guidance which is revisited and iterated as data maturity progresses. Jisc also highlights the need for a coherent business intelligence strategy which ties into the data strategy.

Having appropriate governance and data responsibilities embedded in the organisation is highlighted as a key area of maturity for a data-driven business. Alongside this there is a focus on working with an enterprise architecture mindset, and having robust data architecture including models, policies, rules and standards. Finally, it is noted in the Jisc framework that universities need to enable recruitment, development and retention of staff with appropriate data skills for all of the above across the organisation. Data is often talked about in terms of the tools and technologies used to house it, however without skilled people, it becomes an exercise in gathering and storing data rather than using it, much like building a new computer lab which then sits empty.

As stated by Jisc, these are the activities that enhance the data maturity of a university and contribute towards treating data as a strategic asset. But many of these activities also apply to businesses within other sectors. Without significant investment in the management of data, organisations will find the path to their strategic objectives much slower and more difficult. With the help of a strong data strategy, they can begin to make these strides towards their targets by becoming more data driven.

If you want to find out more about how your organisation can better collect, understand and use its data, get in touch at info@waterstons.com