Building A Data Innovation Strategy For A Global University Enterprise

The Covid-19 pandemic has disrupted the global university sector in ways very few people would have predicted. We have seen universities shift at an unprecedented scale and pace to virtual education and remote working, enabled by digital technologies, in order to maintain business continuity. Many universities are now looking at reshaping their strategies and operational models for long-term institutional viability, realizing that, post-Covid-19, the world will not be the same. There is a paradigm shift emerging that transcends the entire university enterprise that embeds digitalization at the core of the corporate strategy.

Disruptive digital technologies such as the cloud, big data platforms, the internet of things and artificial intelligence are all fuelled by the data explosion created across hyper-connected networks and cyber-physical-human systems associated with complex enterprises. This explosion of data allows digital ecosystems to continually adapt and evolve and helps keep organizations and their services relevant, innovative and competitive.

Contemporary global universities will also evolve as digital ecosystems involving intelligent networks that transcend the entire enterprise and connect all stakeholders on and off campus. As we face the challenges of the Covid-19 pandemic and consider our future state within the global university sector, we should look to data-driven technological innovation as an enabler of new models of enterprise and leadership, new hybrid forms of education and new directions in industry-engaged research that are better suited to the changing operating environment and digital economy.

The Role Of Data

The power of data analytics is endless; it is at the heart of every decision universities make at the enterprise level on a daily basis. Used in the right way by the data-literate staff, it helps to become a lot more flexible and agile and helps universities respond to the changing needs of their students, industry partners, academic community and staff. But how do we get there?

Data Analytics Strategy: Enabling Enterprise Innovation

More and more, universities are realizing that they need to establish a data analytics office and appoint a chief data officer to develop and drive the implementation of the enterprise data strategy. This strategy needs to have a clear vision and support the university’s goals and directives in an integrated manner across all areas of business and operations. It must address core university objectives and performance outcomes in education and research, but also equally address the needs of other supporting functions, including HR, property, marketing, finance, engagement and others. A data analytics strategy must help the university answer key business questions, both now and in the future, which is not an easy task.

How can data analytics help?

If data is managed ethically, it has the power to help universities gain a competitive advantage in the following ways:

• You can use data to understand and shape who you target in your marketing campaigns to increase demand and the number of enrolments.

• You can analyze historical data to understand where you need to invest your efforts to be more successful with research bids and grants.

• You can create new insights to guide your interactions with industry partners, alumni and beneficiaries.

• You can partner with private enterprises to create bespoke offerings for your students.

• You can analyze students’ progression and activate earlier interventions to retain students and help struggling students improve.

• You can significantly decrease the costs of energy and water consumption by analyzing IoT data from meters and building devices.

• You can optimize teaching spaces based on a good understanding of space management and student demand.

• You can improve staff engagement by analyzing warning signs and improve staff retention.

Possibilities and opportunities are great, but this requires strategic organizational effort over time. You do not know what you do not know, but you may know enough now to set up the foundation for your success.

What should a good data strategy deliver?

To address this question, ask yourself the following:

• How do we create the culture of data-driven insights, where data-literate staff have the skills and knowledge to interpret data, ask the right questions and tell a story?

• How do we enable a culture of innovation and experimentation and grow our analytics maturity both through increased skills and knowledge and a “fail fast” approach?

• What information does your university need to meet its strategic objectives? • Can anyone within the university find data that they need?

• Do we have clear accountability for data quality?

• How do we ensure we speak a common language across everyday activities, metrics and reports?

• How do we manage data ethically and increase its value?

• How do we maximize insights to optimize educational and business processes?

• How do we ensure wider and easier access to data while complying with security and privacy standards?

• How do we structure data and analytics organizations and services to build a strong foundation first and then enable the democratization of data services managed by data literate staff?

• What technologies do we need to deploy to be able to work with data in any format, velocity or volume?

A data strategy should cover all of these elements and prioritize areas based on what the university deems important. Look at this scope in terms of horizons.

Horizon 1: Set Up The Foundation

Develop a data strategy and set up data governance structures. Next, get the university to start talking about data through data definitions workshops, key data standards, technology roadmaps for data analytics. Align accountabilities for data and analytics between core university functions and teams. Finally, build a business case for key foundational projects.

Horizon 2: Prepare For Growth

Implement key foundational projects, such as data literacy for university staffers, technology enablement, data management transformation and data quality uplift. Identify quick wins and deliver value to different parts of the university through analytics use cases