Where’s the good in data? How analytics is transforming government decision-making


Undoubtedly, the unifying ambition of everyone who serves in government is to do good for the local communities, regions and countries they serve. What I’m interested in is how departments and agencies arrive at those decisions, verify them and even predict their outcomes so that they know with some degree of certainty ‘good’ will come from them.

Where once the necessary decisions would have been made using mainly practical experience, even gut feel, today more and more agencies are employing analytics.

The UK’s public sector is brimming with examples of how analytics is making a rapid impact on solving some of society’s most urgent and complex issues. In fact, it is precisely the complexity of these challenges that makes analytics so right for government organisations. Particularly when an analytics platform can also do an efficient job of preparing and integrating all the new kinds of data that are being generated through internet connected devices, social sentiment, voice-to-text communications and many other forms of structured and unstructured data.


How data is being used for the common good  








Healthcare. The NHS is renowned for being under strain – an ageing population with multiple morbidities is one significant factor causing this pressure. NHS England began to look at cohorts of patients with particularly high utilisation of healthcare services and used decision-tree analysis to understand why these cohorts used NHS services so much. They worked with the city of Leeds health and social care system to understand the causes of high levels of use and to take targeted action to prevent or reduce use. Indeed, this work has resulted in some interesting findings about the types of condition or combination of conditions that tend to lead to high service use. This proof of concept has been so successful that NHS England are planning to provide this service to all 44 Sustainability and Transformation Partnerships to help them improve patient outcomes and reduce costs.

Smart cities. Analytics has been used to help local authorities convince residents and local businesses to reduce their energy consumption by 20 per cent. Importantly, data insights are being used end-to-end. From telling project owners what decisions are critical, to targeting the best ‘trigger message’ at citizens, to modelling changes in consumption patterns based on real-time IoT data, analytics is playing a pivotal role in delivering change for the better.

Child support. Combining data analytics with case knowledge and experience is helping social care teams to make better decisions about what intervention to take to encourage parents not to be delinquent in their child support payments.


As you can see, whatever the application or use case, analytics is playing a vital role in delivering better financial, social, environmental, health, resource and wellbeing outcomes for UK government departments and many others around the globe. In fact, SAS participates in the Data for Good programme by powering the work of the Gather IQ community, a group of dedicated data friends putting their spare time to good use to solve some of the world’s most challenging social issues.

In a business context, SAS can help government organisations to use data for good – and to do so quickly – by taking advantage of our Results-as-a-Service offering. Here, you bring your data and business problem to us and our experts solve it – with no hardware or SAS purchases required.

What I encourage you to do now is to let your imaginations run free, dream up use cases (your most difficult challenges) and talk to us about how we can solve them for you. In the meantime, the e-book ‘Doing good with government data provides more on what a powerful instigator of change analytics really is.




About Author

Roderick Crawford

Regional VP Northern Europe, SAS

Roderick has 25 years’ experience in international systems and software. He is responsible for driving analytics adoption across all segments of the UK and Ireland public sector. He is the leader of a senior and experienced team delivering value to major government clients through the full range of SAS solutions including advanced and predictive analytics, data management, data governance and visualisation.


  1. Mitchell Squires on

    Hi Roderick,

    Could you site sources in terms of UK councils around the social care applications.
    At Ealing we are doing some work based on a Hammersmith and Fulham statistical model of trying to predict early in the referral process what is the likelihood of children reaching Looked After risk levels so we can intervene. Early days!
    We would be interested in other practical applications such as encouraging parents to meet child support payments.

  2. Hi Mitchell,
    Thank you for your comment.
    I’m afraid I don’t have UK examples which are publicly available, but, being a global company, we have some examples from other countries that should be of interest.
    Firstly there is one from New Zealand you can find here - https://www.sas.com/en_gb/customers/msd.html While this was primarily focused on tackling long-term benefit claimants by offering additional help at key, younger ages (with some great results) it goes on to explain how analytics could also be used to help anticipate and curtail child abuse. This is obviously more in line with the scenario you mention and we could try and find out for you what progress they’ve managed to make in this area.
    I include another example here from the US - https://www.sas.com/en_gb/customers/la-county-dpss.html This focuses on tackling fraudulent claims but has application to any welfare system – if you can reduce fraud it frees up funding that can be directed to those that really need it!
    Regarding your other enquiry about child support payments, the New Zealand example demonstrates how a particular problem group were identified and then targeted, with significant results. I would imagine it’s possible to do something similar if there are common characteristics/circumstances that apply to those who appear least able to meet child support payments.
    I hope these examples are helpful and feel free to come back with any questions.

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