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Defining the Value of Data

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Defining the Value of Data

By Richard Walker, Data and Insights Partner, Agilisys

With the Prime Minister declaring for the last year or more that Government is “following the science” and whole swathes of the population staring at charts and monitoring trends, the link between the data that government collects and the outcomes it drives has suddenly assumed a place at the forefront of public consciousness.

While the concept that “data saves lives” has perhaps never felt more prescient, it is not only in times of unprecedented uncertainty that the value of data is highlighted.

For example, in the public sector, data can be the invaluable key to answering many ongoing challenges. How do we deliver better care for an ageing population with changing needs? How do we predict and prevent threats to public safety? How do we move around the country more efficiently and arrest the damage we are doing to our environment? These are questions we were grappling with before COVID-19 that will continue to challenge us as we emerge from the pandemic.

Unfortunately, it is all too often that organisations fail to prioritise the case for investment in collecting, managing, and exploiting data so that it’s ready when it’s needed. This is largely due to organisations failing to define and measure the value in their data.

However, when one thinks of data as an organisational asset, it seems to me there are very few other assets that influence as many organisational decisions, processes and outcomes. Businesses and public sector bodies have data across their companies – in HR, IT, finance, customer engagement but it is only recently that we are starting to see the emergence of dedicated data capabilities led by Chief Data Officers.

Why is data important?

We all know that budgets are not limitless, and the difficulty in explaining where the return on data investment will be realised is a serious issue. It is probably the number one reason to get serious about defining and measuring the value in your data, but it is not the only one.

  1. Data fuels action
    Richard Walker

    Richard Walker

When it comes to choosing which elements of a data strategy to invest in, we must make choices. These should be informed by the value each initiative will individually or collectively unlock.

The answer for which actions to prioritise should lie in a clear understanding of the route to value. Not all data is equal when it comes to delivering your strategic objectives, you will need to prioritise and be able to defend those decisions to those who would have preferred you start with their area instead.

  1. Accurately measure effectiveness

Just like everyone else, us data folk should be held to account for the investments we make in improving data itself (collection, curation, quality etc.) or the methods used to extract its value (analytics, digital applications etc.).

Without a baseline of the value in our data, how can we persuasively present a case for the improvements we have made over time? The answer is we cannot – providing a clear need to value your data assets and revisit those valuations over time.

  1. Data accessibility is a form of monetisation 

Not everyone will feel comfortable with the idea that public sector data assets could be commercialised in a straightforward transactional sense, but it is unlikely to go away. The world’s wealthiest companies are data companies. Make no mistake the value in public sector data assets to those companies is monumental. It is data they can only approximate from all the other sources they mine.

In a hypothetical scenario whereby a local or regional authority were to look to monetise its data, you would want a fair price, to be assured that value is fully understood, and the best deal negotiated. This will not happen without measuring value.

What are the different values of data? How can they be calculated?

  1. Cost Value of Data

The cost to the organisation of collecting, managing, protecting and storing data, including people, process and technology costs. When you factor in the people and technology elements, it is indeed a costly endeavour.

The asset that those elements fundamentally store, manage, protect, move, and analyse is data. If you are looking for a compelling statistic to use to get your investments in data as an asset taken seriously, working out how much your organisation currently spends looking after it is a good way to get some attention.

Presenting your plan as an incremental investment (often a small percentage), to get better bang for the significant buck already committed, is a technique frequently used by those responsible for other strategic assets. The sell can take many forms, it might be that you will drive process efficiencies or performance improvements elsewhere in the organisation, or that you can identify savings and efficiencies within the current data spend portfolio but need to land an investment to save proposition.

  1. Economic Value of Data

That is, the benefit generated, less the cost of the intervention itself.

Using a logic chain to address the bridge example you would have five buckets of considerations. The inputs, activities, outputs, outcomes, impacts. The first two columns are the things that cost money and the latter three, are the things that either save or generate money (benefit). In the bridge example, you tot up the inputs e.g. materials and the activities e.g. construction, administration, maintenance etc. and these are your costs of the intervention. You then look at the outputs e.g. the bridge itself, regeneration of surrounding land, followed by the outcomes e.g. faster journey times, less congestion, new jobs, new business start-ups and new revenue streams and the impacts (usually directional) e.g. net additional GVA, reduced CO2 emissions, increased local employment, net additional returns to the exchequer etc.

In this context the Economic Value is the difference between those costs and benefits.

Applying this type of framework to a data related initiative is often more difficult. In commercial settings, it can be achieved through testing e.g. make data available to sales team ‘a’ through a new BI platform but not to sales team ‘b’ — measure the difference in performance and you have a measure of value returned for your investment.

  1. Opportunity Cost Value of Data

The cost of unrealised value due to the “state” of the data asset. This can be measured using the opportunity cost of investments already made or outcomes not achieved.

Imagine your organisation has made a £20m investment in a new technology platform. You were persuaded to go for the Rolls Royce version with all the bells and whistles, reassured that concerns over the data in legacy platforms were nothing the vendor hadn’t seen before and that their solution would fix all of that anyway. Two years in and only 60 per cent of the functionality has been enabled. Your vendor is blaming the state of your data. The state of your data is therefore costing you £8m.

  1. Market Value

There are many means via which organisations can understand the market value of its data. Think about the partnerships being struck around innovative new technologies between public and private sectors. Often the public sector provides the data whilst the private sector provides the means to turn it into insight and action. I believe we will see more and more of this type of “joint venture” going forwards and we will very quickly come to think of public sector data as a magnet to pull in private sector investment.

Data is not finite and so the opportunity to repeatedly sweat the same asset through such deals is much greater. Not forgetting legitimate concerns around the ethics of using data in this way, but there are ways to navigate those and end up at this reality sooner than we think.

It is important to remember that these approaches are designed to be used in tandem. When competing with other investment priorities, you will probably need as much ammunition as you can muster. Therefore, it is vital that in prepping yourself to cross the final hurdle to secure funding, you don’t overlook the power that data can have on the human element of the business too. Combine the two, and you should have a winning proposal.

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