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 

Horizon 3: Strategically Scale 

With these fundamentals in place, you can focus on delivering more advanced analytics models and  answer more complex university questions.