The Raven's Blog


Visual.ONS: How to compete with the big data aggregators

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Christopher Gallagher, territory manager at SAS, explains how big data can be used by the public sector to find innovative solutions to common problems. 

I have been really impressed by the work of Visual.ONS – the team at the Office for National Statistics, who are responsible for exploring imaginative new ways of "making data, statistics and analysis more accessible, engaging and easier to understand."

It delivers data in a really engaging way to answer some of the biggest questions in our society – something that’s especially important in the era of ‘fake news’. One such output is the Well-being Wheel (that measures national health, happiness and economic satisfaction etc). Yet one question strikes me: how can the underlying statistical analysis be nudged on a stage further in order to become more meaningful to individual citizens and businesses, especially start-ups and small firms? 

I believe it’s particularly important for the ONS to always be seeking new sources of value it can deliver to traditional customers and to a generation of new ones. Because today, it is somewhat competing against powerful data aggregators such as Google and Zoopla. These organisations are in the business of collecting contextual data that can really help citizens understand more about the fabric of their daily lives, in ways that help them make important decisions.

If you were making one of your most important financial decisions – buying or renting a house, for example – knowing how well the local schools are performing, or how likely you are to be afflicted by an environmentally triggered health condition, would be incredibly helpful. Similarly, if you were planning a coastal retirement, you’d probably want to know what the flood risk is or the quality of care you could expect to receive by local health and social care providers.

Imagine taking that first intrepid step to setting up a small business. You’d really want to understand the nitty-gritty of locating the best possible market opportunities, or knowing which local authorities offer the best incentives, what other start-ups are saying about the support they’ve had from local banks, business groups, councils etc. In short, you’d want to know what potential the local population holds for you – facts that population density, predicted growth rates and disposable incomes can help with.

Making your statistics more personal means diving down to a more local level. It requires Visual.ONS to be able to perform analyses on a continued basis throughout the year in order to deliver more frequent outputs. Most importantly, it requires the ONS to be able to integrate new sources of data – both structured and unstructured. We live in an era where citizens keenly use social media channels to deliver their ‘thoughts’ on a wide range of topics. Harnessing that enthusiasm could be one way to add new richness, depth, context and meaning to the statistics you provide.

Could you one day produce a Local.ONS? This could be a personalised account that recommends data based on your preferences, using the kind of AI-driven intelligence as ‘Amazon recommends’.

The challenge this approach poses is one of data management and rapid analysis. Therefore, what would be required is a system that is capable of collecting massive sets of disparate yet associated data, preparing them rapidly and accurately for analysis in line with data privacy legislation.  

Furthermore, once collated, the best way to gain the maximum possible value from that data would be to make it, and analytical investigation tools, readily available to different departments within the ONS. Using a single analytical platform which also provides users with the ability to work in the coding language of their choice is key to driving the value of data and collaboration. This capability exists in your current analytics investment. 

By developing analyses and insights that are more highly tailored to the needs of local communities, or to businesses at different points in their lifecycle, the ONS would be well placed to reposition itself as an enabler of citizen decision-making. That’s a powerful place to be, with nation and life changing events, such as Brexit, on the horizon.

Imagine if you were able to predict the impact of the removal of funding from the EU on the economy of certain locales, such as the London Borough of Brent, Somerset and Inverness. Such intelligence would give vital insights and risk-assessments to would-be start-ups. Or perhaps you could model crime risk based on historical factors, forthcoming changes to police budgets and strategy, and local sentiment analysis.

The exciting thing is that you can deliver this kind of value-generating work right now and become the ‘go-to’ organisation for citizens and businesses who need credible answers to some of their biggest decisions. By putting your current analytical platform and tools to work, alongside your existing SAS products, you have a powerful analytical engine – with machine learning and AI capabilities embedded – to support automation of your statistical outputs and a more collaborative approach to working with fellow ONS departments.

Your ability to compete and to surpass your competitors is at your fingertips. We have compiled a helpful eBook to show how we believe the ONS can further extend its position as the pre-eminent provider of UK statistical data – and deliver more insights and value to a broader range of customers. Please download a copy here and share with your colleagues. 


Ndeea   20/11/2017 at 11:00

With an emphasis on predictive analytics, it is important to provide customers with the ability to move beyond simple reactive operations and into proactive activities that help plan for the future and identify new areas of business. Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. Modeling provides results in the form of predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables.

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