The pros and cons of predictive analytics for service design
- 26 November 2018
- Posted by: Helen Nicol
- Category: PSTA
Richard Selwyn, Head of Transformation at the MoH Troubled Families Team, takes a look at the possibilities of using predictive analysis in the design of services for vulnerable people. But what is predictive analytics, exactly? Selywn explains:
- The term ‘predictive analytics’ is used for assessing large quantities of information to see if there are trends. Those indicators can then be used to identify more families who might be at risk.
- Big data means the huge amount of data that can now be searched by computers – with the potential to add new data sources such as from surveys.
- We talk about risk and protective factors which are identified by predictive analytics. For example risk factors for a child in care becoming homeless include having a relative in prison. Protective factors would include good educational outcomes.
- And you’ll have heard the term ‘machine learning algorithms’ or ‘artificial intelligence’. This means the apps used to trawl through the data don’t need to be told exactly what to look for, they can learn about connections between multiple indicators.
Deploying data in this way so as to inform service design is still a fairly new idea, but Selwyn provides examples in order to argue that there are distinct advantages to predictive analytics that other approaches cannot match: the possibility of redesigning genuinely novel services in response to the data, for instance; easily identifying the most vulnerable or at-risk; building up a knowledge base that improves over time as more intelligence is gathered; and faster analyses, which results in more rapid responses.
That said, there are still some risks involved, particularly related to accuracy, to the potential for amplifying existing inequalities through the incorporation of existing biases into algorithms, and to the ethical hazards of aggregating data from across multiple government databases. Furthermore, there are practical challenges rooted in finding the right datasets, training practitioners in their use, designing the algorithms and evaluating impacts, among others. Nonetheless, Selwyn sees much potential in predictive analytics for service design, not least in contributing toward the goal of “sustaining the impact of Troubled Families post-2020 and continuing to improve families’ lives”.