or LinkedIn here https://uk.linkedin.com/in/tonysmiththathousingitguy
Predictive analytics look to me to becoming Social Housing's next 'Self-serve' or 'staff mobile working'. The latter pretty much now being rolled out by pretty much all forward thinking landlords. I have recently written a whitepaper on predictive analytics for arrears case management. Definitely one area which I would take a proven solution off the shelf.
But how should you go about building a model yourself, for other purposes? What might be the gotcha's that are worth thinking about?
Well the first one for me would be focus carefully on your end-game. As I have heard it described quite well, by one data scientist friend, don't build yourself a rather elaborate hammer, then go looking for your nail. Predictive Analytics is a current buzzword, much like 'Digital Strategy' was until recently. At least most organisations understand what the latter practically means now. Before setting out, know what most of your outcomes are going to be.
Next, when you have some outcomes in mind, check your available data supports it. Easier when you start with a well integrated set of systems. Do get in touch with me directly if you still haven't cracked that one. Never try to structure a project around data you just don't have. Ask that crucial question before you start. Where specific data is missing, there may be the opportunity to generate it or collect it specially. You might want to target a particular sub-set of residents/customers, to have reliable data to utilise.
Data quality is the next gotcha. Assume a good deal of your data will be of poor quality. That need not hold you back. Many projects have been delayed until data is thought to be 100% correct. Believe me, you won't have 3 years or more to get to that point. I would urge you to cautiously work with what you have and enforce a data culture to constantly improve it, in parallel to any predictive analytics project.
Assume too, that you will find even more poor data, as you build models & work with it. I have lost count of the number of tenancies & works order requests, with end dates before start dates etc. If your data cannot be corrected, filter it right out, lest it skew models.
One example I heard about, management were too bullish about the apparent good quality of data. After being advised that data cleansing was required in the project, this was dismissed to shorted timescales, against the advice of consultants. Later when poor data quality was revealed, and more time needed to be added to extend the project, to achieve outcomes, it became a credibility issue for the director involved. Don't be caught out by too much good news, hiding the bad.
Finally, when your lovely shiny predictive analytics model is built, ensure you have planned a proper deployment phase. It is not enough that you have 'built it and so they will come'. Work out how you are going to tell the wider organisation what your model can do and seed their imagination as to how they can apply it to their own department issues and decision making. Find out what their particular nail will be, to which they can apply your new shiny hammer.
I promise, soon I will post on what typical calculations for SocialHousing, might work for us, and how this might affect process.
Related Post: Sick of your housing management system? Why not write your own?
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Todd Rundgren – For The Want Of A Nail.
(c) Tony Smith, Acutance Consulting www.acutanceconsulting.co.uk 07854-655009
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