Pearson Five actions for data-led transformation in Higher Education

Five actions for data-led transformation in Higher Education

There’s little argument that data analytics can help higher education institutions to support their students and function more effectively. 

Used correctly, it’s possible to use data to monitor and boost engagement, identify students who are struggling with their studies and their mental health, and even manage buildings and faculty resources more effectively. 

But it’s also true that the advanced data analysis that can lead to this level of transformation is a significant and complex undertaking. The wealth of data available can be a barrier in itself – the paradox of too much information being as difficult to interpret as too little – while the time required to review and act on any insights is seen as yet another role for already-stretched educators and academic support services.  

Many institutions throughout the world are winning, however – and it’s these that McKinsey recently interviewed in depth for its report, How higher education institutions can transform themselves using advanced analytics – uncovering the challenges and pitfalls they have faced in building their analytics capabilities, as well as these five actions areas that can foster success:  

  1. Articulate an analytics mandate that goes beyond compliance. In other words, signal that analytics is a strategic priority and not just a box-ticking exercise. For the best results, McKinsey says, data analysis should be viewed as a source of innovation and an economic engine for an institution (and this should be articulated by senior leadership.) 
  2. Establish a central analytics team with direct reporting lines to executive leaders. To mitigate issues around data siloes or decentralised resources, the requisite funding and people should be put in place to oversee and manage the use of analytics across an institution. Not only can centralising data resources into a single platform ensure a single source of truth, it can actually reduce the number of systems and tools required by staff. 
  3. Win analytics buy-in from the front line and create a culture of data-driven decision making. To overcome cultural resistance to data sharing, the analytics team should take the lead on building meaningful conversations about analytics across departments. Formal and frequent meetings between the central analytics team and individual teams can help – as too, can having analysts sit alongside data users to facilitate sharing and decision making. Use cases that yield meaningful results quickly, McKinsey says, can be highly persuasive in bringing less enthusiastic parties on board. 
  4. Strengthen in-house analytical capabilities. A skills gap is an obvious roadblock to institutions’ switching to an analytics-led culture, and though outsourcing can be a short-term answer, the need for greater strength and depth in advanced analytics should form part of future talent acquisition planning.
  5. ‘Don’t let great be the enemy of good’. Launching a successful analytics programme takes time and experimentation. Sometimes the data isn’t available. Other projects may not yield results. So start with areas with clear problems and good data. Analyse, make changes, collect feedback, change again and iterate the process. ‘Test-and-learn’ initiatives can help to demonstrate the impact of analytics, to generate buy-in. 


Is it worth it? Yes, McKinsey says, and the benefits of what’s possible now, barely scratch the surface of how data analytics could drive the student experience in the future. We can look forward to bespoke, personalised education, with teaching catered to individual learning styles and competency levels – and even skills development in line with students’ future career aspirations (Alison Watson, Head of the School of Leadership & Management at Arden University explains how algorithmic-led qualifications could work in her WONKHE article on ‘Netflix and Skill’.) 

The good news for institutions who are early in their data analytics journey, is that there are many tools available that can help to plug data gaps with little or no added effort. 

In the new report, ‘Crunching the numbers: the state of student data in Higher Education’ Pearson examines these tools, shares examples of best practice and assesses how universities can access the right data and use it in the right way. 

The report can be downloaded here 

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