Your WIL system may contain valuable research and planning data, but can you see it clearly?

The data accumulating each day in your university's WIL system may be the richest single source of industry data the university has. Granted, it's mostly low-level data which is hard to access, or to find useful patterns in. 

What kinds of questions could your WIL data help you answer, if you were able to effectively use it?

Learn more about the International relationships you already have

Many universities have made substantial investment in opening up International relationships. Revenue from International students fees is high. Relationships established in creating those markets presents opportunities for the University to partner with organisations in the countries of origin.
Could you find out which countries are already hosting the greatest number of your WIL students outside of Australia? 
It's likely to be those counties where most of of your international enrolments come from, but could you find out for sure? 
What about the companies in those countries that are hosting your WIL students. Who are they? Are they in industries your are targeting for growth or research partnerships? Are there opportunities to work directly with those companies to develop more WIL opportunities or other kinds of partnerships with the University?
If these questions matter then its worth considering that the answers could well be sitting in your WIL data today - but that doesn't mean you can necessarily find it.

Discover the depth of relationships you may already have (but can't see)

What if you decide that you want to target a particular organisation, to take on more of your WIL students.  Could you find out who else at the university already has an established relationship with that organisation?
This information will almost certainly be captured somewhere in the university's WIL system (or systems). If you were able to identify which departments, schools or colleges were already connected with an organisation that you want to work with, then you could approach your colleges to discuss your plans. You may be able to enlist their support in making an approach. 
It's true that it can be difficult to get your peers to collaborate on what they might perceive as you moving in on their contacts. Its also true that doing things this way is way better for the organisation you've targeted, who may otherwise be confused by the disparate groups contacting them from the same university. It is also better for your colleges who have established the current relationship (although they may take a little convincing of this) as a coordinated approach makes the university a more attractive partner for industry - relative to universities who don't internally cooperate.

Measure cross-program benefits from your investment in Alumni or Mentoring programs

Alumni and Mentoring programs provide the opportunity to deepen your industry partnerships. Deeper relationships can be leveraged to foster re-investment in the University by alumni - including increased opportunities for WIL. You might already be trying to achieve this. Investment in these type of programs needs to be tracked and returns that can be measured help to keep the investment flowing. 
It would be really helpful if you could use WIL data to measure return on the investments in building Alumni and Mentoring relationships.

Fish where the fish are

A lot of the time spent prospecting for more companies to host WIL placements is wasted. Staff cold calling organisations, selected more or less at random yields a very low return on the time spent. This approach is pretty common but could actually be framing the university as a 'telemarketer' of students.
Deploying WIL data you already have, alongside other strategic university data-sets such as that from Mentoring and Alumni Programs, is a step towards a much more effective approach. If you can bring in external data sets  to round out the picture, you could target organisations more likely to be responsive to participation in WIL programs. 
Some early case studies are starting to appear in the universities who are beginning to explore ways to fully exploit their WIL data. 

What is your story?