A priori or data-driven reasoning in public service provision?

Big data in several areas of public service is now an emerging reality.  Many government organisations including the National Health Service in the UK have announced investment plans to bring big data to the service of users.

In the case of the NHS, patients data is to be used to identify trends and mainly to establish correlations between data variables.

These correlations could shed light on why some health problems appeared in some geographical areas but not in others; how people behaved prior to or after these problems occurred.  And if treatments (including visits to general practitioners and medicines prescribed) worked.

Sundeep Sahay calls this type of reasoning data-driven.  It can provide an interpretation of events which is different from the subjective views of stakeholders involved.  It can also be used to predict potential problems and therefore it can suggest ways in which services can be tailored to specific populations.

Sundeep also is concerned that this type of reasoning is going to replace the apriori one, in other words the one that is exercised by professionals planning or delivering public services.

It is important to distinguish first that correlation is not causation (this is not my idea, I have seen it before).  From a systemic perspective (this is more my idea), it is important to evaluate the assumptions that are built into the design of systems that support data-driven reasoning.  Many of them have to do with who an ‘average’ as well as ‘non-average’ user that the system is going to deal with. And there are others.  Assumptions about what constitutes the ‘environment’ of this user of services. Assumptions about what expected outcomes will systems generate and what purposes are these systems to meet.

If data-driven reasoning is to stay in public service delivery, so should learning to learn.  This last part is about adapting organisations and their management to changing conditions.  So far this type of reasoning does not speak much about this. Not only data but the people using it to make decisions with it should change.  Unless of course they expect or fear that they can be replaced by intelligent systems or tools.

The last consideration on this dichotomy of reasoning has to do with the ethics of reasoning.  Data-driven reasoning seems to have overcome potential conflicts of ethics by embedding an ethic of ‘happiness of the majority’ by focusing on inferring insights from large amounts of data (individual cases).  How can this ethic be overruled by those reasoning a priori or using their own judgement?  Service systems need not only to provide over ruling features but should also generate insights to help decision makers challenge services themselves.

Data is important, but so is human judgement.  One without the other cannot co-exist in this modern era.  Public services and their supporting systems should be designed with both of these elements in mind.

José-Rodrigo Córdoba-Pachón

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