In 1965 Gordon Moore – co-founder of Intel – published a paper with the pithy title “Cramming more components onto integrated circuits” in which he observed that the number of transistors you could fit onto a silicon chip was doubling every two years and he saw no reason why this pattern should not continue for the next decade.
By 1970 this observation had gained the label Moore’s Law and, while other measures of IT capability abound, it has become the ubiquitous descriptor for the rate at which IT has progressed over the past half century.
All of this processing power, speed and storage capacity means that we can now process, analyse and share greater volumes of data than ever before – and that is doubling every two years. The average smartphone has as much capacity as the machines that underpinned the sector agencies in the 1990s.
On the face of it, this is all good: we are more connected than ever before; we have access to more information than ever before; information is richer, faster and more personalised than ever before. This is the brave new world, programmed by fellows with compassion and vision. But scratch beneath the surface and there are some real challenges.
We have more data than ever before – apparently the volume of data on the internet doubles every couple of years (sound familiar?) – and with all this processing capability at our fingertips, we can all do big things with big data, whether we understand it or not. Spreadsheets – the ultimate enabler of the masses in this brave new world – can now store huge volumes of data (17.2 billion data items in a single Excel worksheet) and embed macros and algorithms of amazing complexity. Yet research shows that 88 per cent of spreadsheets contain errors; which is not surprising given that a modern spreadsheet can be comparable – in scale and complexity – to a major software development project ten years ago.
Faced with this rapidly changing technology, organisations – and the people that make them up – are struggling to keep up with the volume and velocity of data. The report by CFE to support HEFCEs review of Public Information observed that “more information on a subject does not always lead [to being] more informed.”
The higher education information landscape contains a myriad of data flows both within and between organisations. The HEDIIP Inventory of HE data collections currently lists 523 individual data returns that HE providers collectively have to make; We think there are 93 organisations that collect some sort of student data from the sector every year. This shocking level of duplication, combined with the subtle, and often unnecessary, differences in definitions used in these collections, results in a massive burden on the sector and silos of data that are not comparable.
The situation within universities is more complex and variable. There is no robust information on standards of data management and governance in institutions but plenty of anecdote to suggest that while some institutions are getting to grips with the challenges of understanding and managing their data assets, in others high levels of duplication and low levels of oversight and control are not uncommon.
These issues are not unique to higher education. In its Data Capability Strategy (2013) BIS makes a strong case for the value that can be derived from improving the UKs analytical capabilities and it identified HE as a key provider of data skills. Unfortunately, it said very little about the less-glamorous, but essential, foundational skills of data management and governance. Without these, our analytical edifice is built on sand.
The Higher Education Data & Information Improvement Programme (HEDIIP) has been established to redesign the information landscape in order to achieve a system that reduces the burden on data providers and improves the quality, timeliness and accessibility of data and information about HE. This is no small challenge. The programme has started a number of initiatives to address issues of data standardisation and data capability, as well as research to better understand the opportunities and barriers for driving change in the way we collection, process and manage our data resources.
But the change we need is far greater and more fundamental than these projects alone can deliver. Change on this scale requires collective will based on a shared vision for the future. That is our next step.