It was a privilege to be the Chair for two intensive days of presentations, discussions and debate at the Liquid Learning Higher Education Business Intelligence Conference this week.
As the conference proceeded, a number of recurring themes began to emerge and while the focus was on the issues as they pertain to the Higher Education sector, I would suggest the areas that we highlighted are also just as relevant to other industry sectors.
Thought-provoking contributions came from University of Melbourne, University of Sydney, University of Wollongong, University of Newcastle, University of Western Sydney, Australian National University, RMIT and Macquarie University, with input from external consultants Pranay Lodhiya of Tadishi Group and Robert Eames of Fivenines Consulting, as well as my own keynote contribution to open the conference.
The range of topics and issues that we touched upon was diverse – institutional strategy, human and behavioural factors, organisation and structural challenges as well as technical, architectural and planning aspects (plus the impacts arising as a result of the Australian budget announcement which came overnight between conference sessions!).
There was clear enthusiasm and acceptance that there needs to be a move towards more predictive scenario models for such areas as student retention and churn, courses of study evaluation, staff management and financial impact planning.
However, this was tempered with the recognition that many universities are still getting to grips with the basic foundations of good quality standard reporting, trusted sources of information and establishment of KPIs and metrics.
2. The emerging role of the Business Intelligence Competency Centres (BICC)
BICCs are becoming more prevalent (even if that’s not what they’re sometimes being called!) aimed at focussing the organisation’s skills and expertise in Business Intelligence, Data Management and Analytics and creating a critical mass of capability. We recognised that the BICC needs to go beyond the technical delivery of BI solutions and must start to act as a change agent to develop and coach capability for business narratives, analytical thinking skills and decision-making into the wider organisation. The hierarchical position of the BICC can vary depending on the culture and organisational dynamics of the organisation, as long as it is positioned within an area that provides sufficient political sponsorship and support.
We noted that high-quality analytic thinkers and skilled BI specialists are in short supply and there is the ever-present threat that good people will be poached into other business sectors.
3. Business Strategy and Data Strategy in concert:
Business Intelligence solutions and services need to be expressed explicitly within the context of the business’s strategic objectives. The BI team needs to draw clear linkage and alignment between informational outputs and the strategic KPIs that they help to support.
Ideally, clear information management and data governance principles will be explicitly expressed within the business strategy.
4. Agile methods are great – when applied appropriately
IT departments are embracing “Big A” Agile methods and Kanban practices for continuous improvement with increasing zeal and these approaches are excellent for application development and process management initiatives.
Be careful, however. Agile methods are not suitable approaches for delivering Enterprise Data Models and Information Architectures, which require a more strategic, structured and holistic approach from the outset. IT colleagues need to be educated to understand the difference to ensure that the most appropriate methods are applied.
5. Higher Education needs canonical data models
A canonical data model (a.k.a. enterprise information model, reference data model etc.) provides an underlying “blueprint” for the information requirements of the enterprise. This drives consistency, repeatability, inter-operability and integrity of the organisations data and also supports consolidation of business rules and metadata.
Unfortunately, there are few industry-standard data models for the Higher Education sector and the data collection requirements for government reporting are not sufficiently detailed or comprehensive enough to support business strategic and operational data management demands at the institutional level.
Having recognised the need for such common core data model, some institutions are now working on developing their own canonical models to suit their own internal needs. There may be an opportunity to collaborate more widely on establishing a common standard information model, at least at the conceptual and logical data model levels, though it is unclear what the “compelling event” might be to make this happen.
8. Business Intelligence is a contact sport
You’ve got to engage on a continuing and ongoing basis. Get out and meet people, listen, understand what they do and then make relevant offers to help them do it more effectively. Build relationships – and then build them again. Don’t come empty-handed – have something interesting in your back pocket and ready to share as a point of stimulus.
7. Democratise the data
To be of value, data needs to be in the hands of business users – engage a pilot of group of interested users as your evangelist community. Re-purposing existing data for new applications drives innovation and creativity.
This will sometimes mean (appropriate) use of “skunk” works to get things done, at least in the short term. Don’t be afraid to bypass the corporate data warehouse – but do it mindfully!
If new one-off and ad-hoc solutions become of repeatable value, then go back and apply the engineering rigour, resilience and ongoing support as a second pass.
Effective change takes a long time in the Higher Education sector. Broad and in-depth consultation is expected for even the smallest change (everyone wants to have an opinion), high levels of unionisation inhibit responsive change, many people are task-oriented rather than outcomes-oriented and there is a general reluctance to make courageous or tough decisions.
(e.g. the issue of Academic Productivity is an “elephant in the room” but is one area where universities could make significant inroads into establishing long-term strategic budgetary health.)
That all said, change is possible. There are end-user champions who can engage with new initiatives to gain traction, and though it might take a bit longer, there is also a significant scope for a “just do it” approach, at least with initial prototype solutions (with after-the-fact consultation, socialisation and engagement). We need to be proactive change agents bringing innovative ideas to the university community, and it’s part of the BI to use data-driven evidence to act as facilitators, enablers and agents provocateurs. Keep challenging assumptions, keep asking the hard questions, keep pushing the boundaries.
And as Pranay put it: “If you’re not frustrated, then you’re not doing your job properly”!