The torrents of data pouring into every industry and sector are intimidating in a vacuum. With data intelligence, this transforms from an issue into an opportunity, and nowhere is that clearer than in academia.

As the next generation of leaders are hammered into shape, data intelligence is the driving force behind this. Here’s a look at the different ways this relationship is being embraced and exploited effectively.

Why Data Matters for Academic Excellence?

First it’s useful to establish that there are a number of benefits associated with adopting data intelligence in higher education. These include:

  • The ability to predict student performance, both on a micro and macro level. There are more than 235 million students in higher education globally, with Asia catalyzing this doubling in the past 2 decades. This means colleges and universities need a way to track individual excellence and also prove the effectiveness of their methodologies.
  • A means of gaining unequivocal insights into top-level strategic decisions that have to be made within major institutions. From budgetary considerations to matters of governance and social policy, data intelligence has a hand in steering choices made.
  • The potential to make advanced areas of research less inscrutable. From healthcare to tech and beyond, academic studies into the nitty gritty of any number of subjects can flourish with data intelligence.

Various bodies worldwide have issued formal edicts into this issue. For instance, the US Department of Education published a data strategy document in which clarified its commitment to implementing data effectively.

Gartner’s latest survey of higher education CIO’s paints a vivid picture of how data intelligence is shaping tech adoption trends. Key insights include:

  • 85% see artificial intelligence and machine learning as being a priority to implement in the next 2 years. Both of these are critical to effective data intelligence, both for organizational decision-making and academic implementation.
  • 72% said they were increasing investment into business intelligence and data analytics this year. This means that it is second only to data security as a spending priority.
  • 34% said they were reducing the amount invested in data center tech. This suggests that more universities are seeking to outsource their data intelligence needs to third parties, at least from an infrastructural perspective.

Where Leadership is Being Influenced?

Now we’re up to speed on the significance of data intelligence in academia, it’s time to get specific. Leadership courses aimed at molding the minds and habits of tomorrow’s higher education head honchos are being reconfigured right now. Here are a couple of examples:

St Mary’s University of Minnesota

SMUMN is leading the charge by offering its doctorate in educational leadership online. This is of course a positive step from an accessibility point of view, since anyone can complete the course remotely. It is also a driver of improved outcomes thanks to how data intelligence is applied to the learning experience. This includes:

  • Personalization: Participants in this course can expect to receive leadership education that is tailored to their needs. Because analytical tools are at work behind the scenes, making adjustments is easy – and those who already have a strong research background can blossom.
  • Flexibility: This online Ed.D is adaptable to the needs and ambitions of future leaders who want to enter a range of spheres. This includes taking the reins of academic organizations, healthcare operations, nonprofits and of course businesses. Data intelligence tools ensure skills are taught well, and are eminently transferable.
  • Competency: Ultimately, courses like this need to result in the creation of competent leaders. With the right resources and expert assistance, backed by data-driven decisions, this is more consistently achievable.


While degree-length courses in leadership are available elsewhere, there are also plenty of shorter courses tailored to data intelligence needs.

MIT has a Data Leadership course that fits the bill here, founded on the claim that organizations which use information effectively are more agile. It covers the perks of DevOps and DataOps, as well as must-know aspects like AI and machine learning.

While aimed at existing executives and leaders, it also applies to aspiring professionals in various fields, including education. It additionally emphasizes the importance of basic coding knowledge in participants. This ties in with recent survey data showing that 45% of students are either learning to code or know how to already.

What Challenges Lie Ahead

It is worth touching on the implications of data intelligence and AI for educational leadership from the perspective of the obstacles created.

Essentially, while data intelligence tools and methodologies work behind the scenes, the end goal of leadership training remains the same. That is, to instill the right skills and sense of responsibility in people who intend to take the helm of large organizations.

Automation in this context has advantages, chiefly in terms of taking administrative workloads away from leaders. This lets them get on with the interpersonal parts of their role as a priority.

There is of course the expectation that leaders will be up to speed with data intelligence tools. This will allow them to communicate and collaborate with technical members of their teams without being bamboozled by all the jargon.

So in a sense, this is a self-perpetuating project. Educational institutions adopt data intelligence tools to determine the nature of leadership courses, and participants follow suit in future roles. It’s much the same story for businesses, where data intelligence is augmenting growth.

The Bottom Line

When it comes to data intelligence and its application to leadership training in academia, there’s a lot to take in. The main points are:

  • Data can be used to enhance academic excellence, determine decisions and assist with research.
  • Universities are already implementing data intelligence to manage courses, particularly for specialist areas like healthcare and tech.
  • The road ahead may not be completely clear of obstacles, but data itself holds the answer to many existing concerns.

In short, it’s all but certain that tomorrow’s academic leaders will have engaged with data intelligence during their studies. Whether they do this actively, or are under its passive influence, is beside the point. The significance of this tech will only swell over time as a result of what we’ve discussed.