The race for alternative data

'Data is the new oil.’ It’s a dramatic statement – and certainly a contentious one. Just as one publication makes the case for it, another rubbishes the concept. The first argument points out that data is becoming the world’s most valuable resource; the second, that the analogy is lazy and rapidly collapses, whether because data is infinite or because it can be transported around the world almost instantaneously and at minimal cost. 

Both sides of the argument are, of course, correct. The metaphor has its limitations but also contains useful angles – and one is to think of the race to alternative data. Rather like the oil booms of the 20th century, we are seeing organisations and entire industries scramble to take advantage of this powerful new resource, rushing to extract and harness it more effectively than their competitors and ultimately to transform it into enormous wealth. 

Alternative data is, very simply, data retrieved from non-traditional sources. Indeed, the Internet of Things (IoT) is fuelling massive growth in alternative data, as billions of connected sensors and devices are deployed in myriad different contexts.  Whilst all data should be anonymised in order to protect personal information and remain in line with data privacy regulations, in other contexts anonymity can render a dataset less useful or reduce the number of insights that can be garnered from it. With alternative data, this is not the case.

Alternative data is anonymised and therefore contains no personally identifiable information, meaning it be harnessed without concerns over data privacy and regulation. 

More pertinently still, alternative data can be collected and analysed incredibly frequently – weekly, daily or even minute-by-minute, depending on the data type. This makes it far more dynamic and potentially richer than traditional sources of data. Combined with other information sources, it can ultimately highly intelligent and multifaceted insights, and ultimately a more dense and precise analysis on anything from customer behaviours to market trends. 

The US has been the world leader in making use of alternative data, particularly in the financial services sector. Alternativedata.org, an industry trade group, identified 447 providers of alternative data provider to institutional investors in September of this year, up from 375 a year ago and fewer than 250 in 2013. The same group has predicted that total spending on alternative data by mutual funds, hedge funds, pension funds, private-equity firms and the like will jump from $232 million in 2016 to a projected $1.1 billion this year and $1.7 billion next year. At the end of 2018, NASDAQ acquired Quandl, a leading alternative data provider based in Toronto, a clear indication of the value the stock exchange is placing on alternative data.

Now, the race is on not just in the UK and Europe but also in other sectors to catch up with the flourishing – and highly profitable – alternative data boom in the US financial services industry. However, making effective use of alternative data requires organisations to have the right infrastructure in place to collect and harness it. 

What does this infrastructure look like? Organisations need a means of collecting the alternative data in the first place, which might mean anything from deploying an IoT ecosystem of connected sensors, to installing tools to automatically extract and consolidate point-of-sale information. Then they need a means of analysing that data, which is where innovations in big data analytics, machine learning and artificial intelligence come into play. All are playing a vital role in enabling massive datasets to be analysed in real-time, integrated and aggregated with other datasets and ultimately translated into tangible intelligence. Indeed, tangibility is a crucial part of the alternative data race – such data is only useful if it is transformed into formats that organisations can make use of, whether visual formats which can be interpreted by non-data specialists, or actionable insights and suggestions for strategy.

Ultimately, the key differentiator in the race is the value of the data itself – and good, meaningful data is hard to find. Organisations seeking to monetise their data and compete effectively should be looking for information which, ideally, is unique to them, tells a story, and is timely and specific. The more granular and detailed the data, the more valuable it is.