MEGA International: How effective data sharing and interoperability drives positive change
One of the key topics for Digital Place Week 2022 is to explore how technology and data are enabling the virtualisation of politics and governance. But this got me thinking about the potential enormity of this challenge, and some suggestions for where to start.
Trying to base government services and decision making on data relies on that data being complete – and most importantly – reliable. To achieve this, public sector organisations need to first figure out how to collaboratively share data within and between different departments.
Breaking down traditional silos to create reliable data repositories
It’s not just the public sector – organisations of all kinds struggle even to share data between different teams, never mind across departments, partners, contractors, and other stakeholders.
For the public sector, this means trying to make sense and create commonality between constituent data, health information, tax details and so on – all completely different datasets organised completely differently. On top of this, there are increasingly complex data regulations to be complied with, as well as scrutiny of how data is used and shared.
Without the right approach in place, sharing this information would seem impossible – and most likely is.
Changing culture is the first step
Although it may be true that some data has always been shared between teams or departments to make governmental organisations run, this has almost always been in piecemeal format.
But to achieve real interoperability and provide a real source of innovation, there must be an understanding across the organisation that sharing information outside of the usual parameters can bring benefits – and the potential for positive change.
Practical examples for the public sector
A good example of this is outlined by my colleague Frederic Fourquet in his blog “data governance; the 7 challenges of an innovation machine”, in which he cites the use of artificial intelligence (AI). AI can draw on pooled data to produce outcomes for broader governance initiatives across the organisation. It can also provide a resource from which previously siloed business functions units can benefit from the deeper intelligence AI provides. For organisations of local councils, this could mean early insights into all kinds of issues.
One example of this is the DVSA’s AI programme to improve the quality of MOT testing. This initiative combined data from 66,000 testers across 23,000 garages nationwide – and 40 million MOT tests.
What’s interesting about this case is that the agency adopted an approach of clustering data – this makes it possible to analyse these huge amounts of data, and to put it into a language that can develop a continually evolving risk score both for garages and individual testers.
This data can then be used to provide DVSA enforcement officers to focus on garages or individuals who may be cutting corners on tests or committing flagrant fraud. The result? The agency’s examiners’ preparation time for enforcement visits has fallen by 50% - and the overall safety levels of cars in the UK has increased.
Where to start
Reconciling data with business needs or opportunities is the first step for public sector organisations looking to drive similar efficiencies or positive outcomes. To do this, a concrete use case must be established to guide the project. From there, the different business units, organisations, stakeholders and data sources can define concepts and data dimensions.
From there you can establish the concepts and related elements of a business glossary, build a data catalogue, and monitor your data sources according to their quality, validity, completeness and accuracy of data.
When it comes to the task of sharing data across people and ‘places’, the advice and practical examples outlined above should offer reassurance to all across government, industry and beyond.
