Higher Education Business Intelligence Conference 2014 - Top Takeaways

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!).

1. Moving from basic reporting to forward forecasting and predictive modelling

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.

8. Making progress is slow and can be frustrating. Take heart!

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”!

Five Whiskies in a Hotel

Key questions to get you started with Data Governance.

A "foreign" colleague of mine once told me a trick his English language teacher taught him to help him remember the "questioning words" in English. (To the British, anyone who is a non-native speaker of English is "foreign." I should also add that as a Scotsman, English is effectively my second language...). 

"Five Whiskies in a Hotel" is the clue - i.e. five questioning words begin with "W" (Who, What, When, Why, Where), with one beginning with "H" (How).

These simple question words give us a great entry point when we are trying to capture the initial set of issues and concerns around data governance - what questions are important/need to be asked.

• What data/information do you want? (What inputs? What outputs? What tests/measures/criteria will be applied to confirm whether the data is fit for purpose or not?)
• Why do you want it? (What outcomes do you hope to achieve? Does the data being requested actually support those questions & outcome? Consider Efficiency/Effectiveness/Risk Mitigation drivers for benefit.)
• When is the information required? (When is it first required? How frequently? Particular events?)
• Who is involved? (Who is the information for? Who has rights to see the data? Who is it being provided by? Who is ultimately accountable for the data - both contents and definitions? Consider multiple stakeholder groups in both recipients and providers)
• Where is the data to reside? (Where is it originating form? Where is it going to?)
• How will it be shared? (How will the mechanisms/methods work to collect/collate/integrate/store/disseminate/access/archive the data? How should it be structured & formatted? Consider Systems, Processes and Human methods.)

Clearly, each question can generate multiple answers!

Aside: in the Doric dialect of North-East of Scotland where I originally hail from, all the "question" words begin with "F":

• Fit...? (What?) e.g. "Fit dis yon feel loon wint?" (What does that silly chap want?)
• Fit wye...? (Why?) e.g. "Fit wye div ye wint a'thin'?" (Why do you want everything?)
• Fan...? (When?) e.g. "Fan div ye wint it?" (When you you want it?)
• Fa...? (Who?) e.g. "Fa div I gie 'is tae?" (Who do I give this to?)
• Far...? (Where?) e.g. "Far aboots dis yon thingumyjig ging?" (Where exactly does that item go?)
• Foo...? (How?) e.g. "Foo div ye expect me tae dae it by 'e morn?" (How do you expect me to do it by tomorrow?)

Whatever your native language, these key questions should get the conversation started...

Remember too, the homily by Rudyard Kipling:

Information Management Quote of the Week 03/05/14

“There are three kinds of intelligence: one kind understands things for itself, the other appreciates what others can understand, the third understands neither for itself nor through others. This first kind is excellent, the second good, and the third kind useless.” 

(Niccolo Machiavelli)

See also: https://www.youtube.com/watch?v=gH-NxQmf87k 

Sell! Sell! Sell!

Why we’re all in “Sales”

Who is responsible for sales in your organisation?

“It’s the Sales Team.” “No, the Customer Service Group”. “Isn’t it Marketing?” “I know! I know this one! It’s the staff in the Retail outlets, right?!”

I’d offer (somewhat glibly perhaps) that a suitable answer is "we're all in sales, all the time". Certainly, that expresses a certain mind-set for developing positive customer relationships, engaging with new ideas and having a willingness to open up to new opportunities. I would encourage everyone to aspire to build an attitude of “we’re all in sales” into their work practices, regardless of whether or not their formal role or job description includes “sales”.

However, maybe it isn't too helpful at a practical level, especially for folks who might not have much sales experience, or who aren’t naturally blessed with an outgoing persona. So here are a few simple thoughts on what “we’re all in sales” really means to me.

Selling is typically a team activity. Across the team, you need to identify who will play which roles; the Introducer, the Commercial Lead, the Subject Matter Expert etc. (If you're a solo operator, you've got to get good at playing all the roles!) I tend to never put a job title on my business cards - this allows me the freedom to be a chameleon and present myself to the client in whatever capacity is most helpful.  

When the customer asks “who are you?”, then your mental response aught to be “who do you want me to be?” (Though you might not actually say it out loud to the customer!) Don't be untruthful, though - you'll get found out very quickly. Honesty and integrity will get you a lot, lot further than snake-oil.

It is also my view that selling products (and indeed information) is actually very similar to selling services. You'll be most successful if you show that you can really understand the client's needs and offer a genuine solution. Selling on the basis of product features (or how good your team's skills are, if you're in services) is the best way to lose in the long run.

And please - don't sell on features! The worst thing you can do is try to tell the customer why your "thing" (or service, or resources, or whatever) is better than everyone else's.  

You might also find my Blog article fromMay 2012 of interest, which discusses the tests that I apply to make sure that your messaging is hitting the mark.

Oh! One more thing - learn to tell stories! (But maybe that's another story - and a likely topic for another blog post sometime…)

Happy selling!

Information Management Quote of the week 27/04/14

“The alchemists in their search for gold discovered many other things of greater value.”

(Arthur Schopenhauer)

See also:

https://www.youtube.com/watch?v=4kBMDYUb0RY 

Data Quality is Boring!

Is this the kind of response you get when you mention to people that you work in Data Quality?!

Let’s be honest here. Data Quality is good and worthy, but it can be a pretty dull affair at times. Information Management is something that “just happens”, and folks would rather not know the ins-and-outs of how the monthly Management Pack gets created.

Yet I’ll bet that they’ll be right on your case when the numbers are “wrong”.

Right?!

So here’s an idea. The next time you want to engage someone in a discussion about data quality, don’t start by discussing data quality. Don’t mention the processes of profiling, validating or cleansing data. Don’t talk about integration, storage or reporting. And don’t even think about metadata, lineage or auditability. Yaaaaaaaaawn!!!!

Instead of concentrating on telling people about the practitioner processes (which of course are vital, and fascinating no doubt if you happen to be a practitioner), think about engaging in a manner that is relevant to the business community, using language and examples that are business-oriented. Make it fun!

Once you’ve got the discussion flowing in terms of the impacts, challenges and inhibitors that get in the way of successful business operations, then you can start to drill into the underlying data issues and their root causes. More often than not, a data quality issue is symptomatic of a business process failure rather than being an end in itself. By fixing the process problem, the business user gains a benefit, and the data in enhanced as a by-product. Everyone wins (and you didn’t even have to mention the dreaded DQ phrase!)

Data Quality is a human thing – that’s why its hard. As practitioners, we need to be communicators. Lead the thinking, identify the impact and deliver the value.

Now, that’s interesting!

Rory Sutherland - lessons from an Ad Man.

On a number of occasions recently, I've had cause to refer colleagues and friends to the highly entertaining and thought-provoking TED Talks of Rory Sutherland, Vice-Chairman at Ogilvy and Mather marketing agency.

I don't want to steal Rory' thunder, except to say that he has some fascinating ideas about the benefits of lateral thinking and development of psychological solutions to real-world problems. His ideas are particularly applicable in areas where there is limited budget, significant architectural or technical constraints or general resistance to change.

In the world of Information Management and Data Governance, I think these translate into questions such as:

* How do we influence people's behaviour more to achieve better information-enabled outcomes?

* Can we shift focus to business outcomes, not technical outputs?

* Do we really need to build another solution, or can we just make better use of current capabilities?

There are three videos, all well worth watching in full:

Do you agree with Rory? Are there areas where this type of thinking could be of value within your organisation? Are we spending too much time "doing" and not enough time "thinking"?

I'd love to hear some of your stories.