The Predictive Analytics Journey

December 11th, 2018

Times are tough – adversity is hitting our public sector and healthcare agencies from every angle. Budgets and resources are stretched beyond breaking point with many agencies facing multi-million real-term cuts. Combine this with an increase in complex demand, heightened public scrutiny and a continued expectation of improvement; something must change.

The Predictive Analytics Journey

So what is the foundation for innovation and transformation?

Our leaders need to hold the mirror up and ask "is the information I am getting helping me make decisions and take effective action at the earliest point?" Then ask "can I get more from my data to drive better outcomes?".

The basic underpinning of all analytics is: Data - Insight - Action.

The astute and visionary leaders have realized that as organizations have become more complex, centralized, globalized and digitalized – they have also become data rich but insight poor and siloed. Some have lost sight of the basics in service delivery. There are so many data sources being captured and stored in multiple locations – a blizzard of data swirling around with untapped value. Data; one of the most valuable assets in any organization is being lost. It is not uncommon for healthcare trusts to have hundreds of systems operating that carry huge latent value.

Governance over efficiency, effectiveness and legitimacy is sub-optimal. In many cases mediocre at best. If you use a data driven approach and ‘turn some stones’ – opportunities will fall at your feet quite readily.

Moving up the curve and embarking on the predictive analytics journey

Once a preserve of bearded boffins in darkened rooms, predictive analytics technology is now readily accessible and affordable to the public sector. Technology has been moving so fast in this field; the limiting factor is the confidence and creativity to exploit what it offers. You can deploy predictive analytics through integrating Python/R or visual modeling software such as Big Squid/Data Robot into your analytics platform. You really can get started quickly if you have the vision, ambition and knowledge.

So how does it work in simple terms?

Probability is the underpinning logic behind predictive analytics; algorithms working the 'math' to generate the likelihood of an event occurring.

Once you have the prepped the 'lifeblood' data flowing in (the most important bit), you can build and train your models on key outcomes (the target) you want to shift, for example; Vulnerability risk (any agency), length of stay (healthcare), offender risk (law enforcement), CSE risk (Local Authority), high risk of road flooding (Highways) and demand forecasts (any agency). The model will identify the key predictors that influence the target outcome from a true data driven approach. When run against your new data, the model generates a risk score or likelihood of the outcome occurring.


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