Smart Data Analytics: A Competitive Advantage

Business schools can successfully transition to a virtual environment by using smart data analytics.

COVID-19 HAS THROWN many business schools over the cliff into a still largely unexplored world of online education. They have mostly managed the situation well. Entire faculties have moved into online learning with surprising ease, and sector leaders have supplied a vast amount of free resources to help business schools navigate through the thicket of technology solutions and pedagogical tools. But despite the abundance of positive news in this difficult period, we should not overlook some of the more sobering takeaways.

For more than a decade, business schools have explored the idea of providing online degree programs, but for the most part they have treated the topic as a “gray rhino”—i.e., an inevitable development they could safely ignore for the time being. COVID-19 has dropped this issue firmly at the doorsteps of business schools around the globe. They are forced to chart their way forward without a solid understanding of this new environment and back up their plans with tangible resource commitments. As Tim Westerbeck notes in “It’s Time to Enter the Data-Driven Age,” they have underinvested in data as a strategic resource and, as a consequence, are struggling to underpin developmental planning with sound business intelligence.

Business schools must address this deficiency with urgency. But they should not stop there. Once the pandemic is contained, business schools can’t simply keep their existing programs online. To successfully transition to a virtual environment, they must determine who might take their courses, how these students want to learn, and how much delivery flexibility is desirable. And the best way to do that is to use smart data analytics.


Many schools use data-driven business intelligence to analyze their markets and their competition, but they could do so much more with data. Smart data analytics combines an in- and outward focus of analysis as well as a top-down and bottom-up approach in strategic decision-making. Such an approach also helps schools find the right balance between data-driven decision making and wayfinding underpinned by qualitative heuristics.

Nottingham Trent University’s Nottingham Business School in the U.K. provides an excellent example of the benefits of smart data analytics. The school tracked its development trajectory with performance indicators that allowed it to benchmark itself against competitors and external quality validation frameworks. Administrators furthermore built up the school’s organizational capabilities to improve external recognition and created a culture of developmental experimentation to minimize the risk of mimicry and isomorphism.


When schools underappreciate data as a strategic resource, they cannot engage in smart data analytics. In fact, many business schools still struggle with the basics of data management. For instance, data may be accumulating in quality assurance units to be mainly used for compliance purposes and external reporting. Alternatively, it may be distributed across the entire organization in loosely connected silos, possibly in combination with a fit-for-purpose information system that has never been properly deployed. Data is often underutilized during periods of rapid growth when administrative attention is drawn toward the scale-up of front-end operations while back office projects get stuck in the tracks.

Data management requires staff training to make sure that existing systems are properly utilized, and that data is not siphoned off into “shadow” information systems. If IT-related projects fail, and many do, it is because the people dimension is ignored. Symptoms of poorly utilized data are budget overruns, long times to completion, and subsequent cost increases as information flows become more complex.

Data quality and availability are often compromised when data management becomes an arena of political power play—i.e., “whoever holds the data makes the rules.” Two of the authors consulted at a top-ranked business school where the admissions department refused to share data on the throughput of applicants, which made it essentially impossible to generate reliable predictions about next year’s enrollments.


Smart data analytics requires a combination of top-down and bottom-up approaches. Top management is needed to ensure that there is the proper budget for smart data analytics, even during periods of market downturn. School leaders also play key roles in breaking down institutional resistance and overcoming procrastination. At the same time, deans and administrators must realize they generally are not fully informed about deficiencies in the way data is collected and handled. If given the opportunity, staff members can help them close the awareness gap. The message to deans is: Lead and be led!

Following a “Team of Teams” approach can be particularly effective as all faculty and staff members will gain a broader understanding of how smart data analytics can help the school achieve its strategic objectives. For instance, a Swiss client of ACADEM by RimaOne has implemented a broad portfolio of communication measures to bring everybody on board. These measures include biannual town hall gatherings, regular coverage in department meetings, and “coffee, tea and data” events that allow faculty and staff to learn about data analytics in a more casual setting. Finally, the institution uses “good practice” videos to reinforce the role of data and analytics.


Many business schools use accreditation processes as a driver for the development of their data architecture. This is a sensible approach, since accreditation ambitions are a means of aligning faculty and staff with a sharply crafted institutional objective. Initially, data availability should be compared to an accreditation system’s minimum data requirements.

As a second step, voluntary over-reporting should be considered to further strengthen the narrative presented to the accreditor. It will be most effective when combined with analytics that convert data into credible quality signals. Finally, a plan should be put in place to fill existing gaps to further develop the school’s data pool.

The scoping of available data must include quality checks that extend to a review of current data management practices. Is the data flow as intended, and are key actors aware of what they need to provide when and in what form? Is data cleansed, validated, and embedded in an audit trail?

Needless to say, not all data needs to be generated internally. For example, Jönköping International Business School (JIBS) in Sweden makes heavy use of externally hosted research data. The information is fed into the school’s internal repositories, then harmonized in ACADEM to produce reports that can be used by accrediting organizations, researchers, and faculty.

To develop and manage data pools, schools must create a governance structure that accomplishes several goals. It must ensure compliance with regulations such as GDPR; protect data privacy by spelling out who can access data for what purpose and under what conditions; and possibly assign a time stamp that defines when data should be erased from the system. Data governance further must specify the handling of data. Who is responsible for what part of the data? Who is managing the data? What controls or cross checks need to be put in place to establish and protect data quality? For instance, systemic deficiencies can emerge if staff members perceive data pool maintenance as a residual add-on. Intense work cycles might then invite spells of sloppiness.

But accumulating too much data will actually kill data. Not everything that can be collected is worth collecting. If providers and users of data perceive data management as a catalyst for productivity, especially their own, they will happily provide data that can be converted into valuable insights for themselves or colleagues. In contrast, if they feel they are feeding a data engine that has the touch and feel of a black hole, they will consider the work a waste of time.


In many business schools, the richness of data pools is met by not-so-smart data analytics. At level 0, schools mainly track aggregated indicators they use in external reporting—for instance, to determine the schoolwide ratio between FTE students and faculty. But if schools only collect data they need for an external accreditor, they are leaving a lot of unutilized information in the data pool.

At level 1, business schools take a more disaggregated look at their operations—e.g., considering faculty sufficiency by field, program, or semester—and produce forecasts aligned with their strategic planning cycles. They also may take a wider look at important indicators by capturing accreditation systems and rankings that are currently relevant, plus the ones that are part of their strategic ambitions.

Level 2 adds benchmarking and gap analysis to the picture. How does the development trajectory look relative to key competitors? Where does the school fall short in regard to its own ambitions and external standards?

Level 3 involves the utilization of more data, including big data sitting in the cloud, as well as the application of more sophisticated research methodologies. State-of-the-art text analysis tools can be used to measure whether outputs comply with the criteria of responsible research and cover societally relevant topics.

Finally, in level 4, business schools are ready to assume a pioneering role and measure performance in topic areas that so far lack established indicators. These might include quantifying impact outside of the sphere of academic research or applying metrics to capture the international dimension of student learning. Such high-level innovation may be tied to a school’s investment programs—for instance, helping schools determine when to invest in smart campus structures.

When we speak of smart data analytics, we refer to levels 2 through 4. Level 2 is the entry gate, and level 3 is the sweet spot for most business schools.

Let’s return to the case of Nottingham Business School. Innovation in data analytics allowed the school to implement a large-scale personalization initiative by helping administrators understand every single student’s engagement patterns. This enabled the school to meet the needs of each individual student and provide targeted support for study, self-development, experiential learning, and career development.


Smart data analytics will become much smarter in the future. We anticipate a shift away from self-reporting of collected data for purposes of external quality validation, be it accreditation or rankings, toward data insourcing using public data repositories and the wisdom of the crowd. Artificial intelligence will enhance the reliability and information value of data, which will be important as institutional structures that provide management education become more transitory and fluid.

Business schools will be forced to walk on more uncharted ground in the future. Smart data analytics will not remove all uncertainties, complexities, or ambiguities, but will make the forward vision less blurred. And that is all that is needed to convert data into competitive advantage. While “Management by Data” is often a curse because it creates behavioral rigidities, “Leadership by Data” can be a strategic game changer.

Isabelle Fagnot is associate dean of quality and accreditation and professor of management of information systems at KEDGE Business School in France.

Ulrich Hommel is professor of higher education finance at EBS Business School in Wiesbaden, Germany; founding partner of XOLAS; and former director of quality services for EFMD.

Benjamin Stévenin is the CEO of RimaOne and co-creator of ACADEM, a data management solution to assist business schools with the management of their faculties, research, and accreditation processes. He is also a founding partner of XOLAS.