“Today’s alternative data can be tomorrow’s mainstream data”
The current boom in alternative data sources and tooling in financial services has the makings of a massive (and permanent) push of data civilization into what was hitherto in some sense terra nullius.
So, what is happening, asks Martijn Groot, VP of Marketing and Strategy, Asset Control ? What is driving the rapid growth in alternative data spend?
Simply put, we have seen a rapid expansion in ways to produce data (e.g. using geospatial information, imagery, sensors), some regulatory drive towards transparency (pre-and post-trade disclosures) and means of acquiring and aggregating information (for instance, through new data marketplaces and new alternative data feeds) and also – up to a point – to shape it for actionable use.
Up to the present day, the practice of good data management has been a little like maintaining a walled garden: there has been a strong emphasis on careful pruning and controlling a predefined data set for operations and compliance, in any case well-defined use cases.
What has changed has been the arrival of the weeds and the wildflowers of the alternative data jungle – as well as the opportunity that comes with that. We are all gardeners.
At the moment though, there is a danger that the garden will become overgrown. Banks and financial institutions hold vast amounts of alternative and unstructured data which is largely under-analysed and rarely put to use. Previously, these data sets either went to waste or were running free and used at best in isolated situations but today there is much greater understanding of the nature and value of alternative data. Concurrently, we are seeing the rapid emergence of new techniques and tools that can more effectively harness it.
“This can start with tagging images with geospatial coordinates and date/time, marking up newsfeeds and earning call transcripts …”
Today’s alternative data can be tomorrow’s mainstream data and one way to define alternative data is to label it as ‘all data not commonly used by the financial services industry’. Due to new data marketplaces, new data sources and the incorporation of new data sets into larger enterprise data products the alternative data frontier has been pushed back. Developments in curation tools that can make alternative data actionable has sped up that process.
The importance of these curation tools speaks to one of the paradoxes in alternative data: to properly access, search, interrogate and integrate data into business processes you need to impose some structure. We could also refer to the whole category as ‘alternatively structured data’ rather than unstructured data. After all, something without any structure is quite literally random noise. This can start with tagging images with geospatial coordinates and date/time, marking up newsfeeds and earning call transcripts and range to correlate vast amounts of data with entities, specific instruments and risk factors.
Where we are seeing the most significant developments is in rapidly closing the gap in the tools to integrate these data sources into day-to-day business workflow processes. To put it crudely, if you can’t put a data source to use, interest in it will quickly diminish.
Adoption areas can range from compliance (early use cases were studying behavioral patterns in large transaction data sets) to gaining an investment edge. In the latter case, there is an analogy that can be drawn between using alternative data sets and building an increasingly accurate and more complete model, map or representation of the (financial) world. It is rather like moving from a simple compass-based approach to the latest most technologically advanced satnav. Alternative data sets provide additional detail or even additional angles for users to explore.
Furthermore, the tooling needed to navigate and map this new data world is increasingly growing in order to prevent users for getting lost and effectively not seeing the wood for the trees. And putting this kind of tooling in place is important. Imposing structure, inferring connections between datasets and detecting patterns is the task of data management. It drives insight and will make the new datasets actionable.
A further important area of use cases is risk assessments. The data intensity of risk and reporting processes is likely to continue to evolve. However, additional data sets are also fast becoming prevalent in trading and investment decisions. Their use in those processes will be another factor pushing back the alternative data frontier.