Alternative data has become a burgeoning part of the financial information industry. As part of a series of educational articles (which so far have looked at market data and reference data, data management, data quality, and securities mastering, this piece looks specifically at alternative sources of financial information, why they have become sought after, and the specific challenges of managing such data.

What is alternative data?

Alternative data can be somewhat ill-defined. It typically refers to data that supports trading and investment decisions (Wikipedia’s definition is “data used to obtain insight into the investment process”). However, the label “alternative” means that these data sets fall outside the traditional definitions of market data and reference data. As we saw in our earlier article, market data typically covers pricing and risk information originating from markets (used to carry out technical analysis), but a broader definition also includes information relating to the fundamental performance of an asset – news, company financials, research, ratings, etc. Either way, traditional market data tends to originate from sources whose primary focus is on the market (exchanges, market operators, trading members, research houses, data vendors, financial newswires, credit rating agencies etc.).

Alternative data tends to originate from sources that are not necessarily market focused. However, the information they collect can offer a strong read on market performance. For example, weather data is not typically collected for the purpose of trading financial instruments. But the weather can have a significant impact on demand for energy (particularly cold weather meaning more energy to heat homes, while unusually hot conditions triggering demand for air conditioning), so it is therefore highly relevant and an important decision-making factor for energy traders. Equally, clinical trials are primarily designed to test the safety and efficacy of a drug/therapy, and to obtain regulatory approval to bring those drugs/therapies to market. But the details of those trials can be equally important for investors. Satellite imagery can also potentially be a rich source of alternative data to gauge economic activity. For example, by keeping a count of cars in carparks to assess retail activity, or gauging levels of traffic to estimate revenues for toll road operators. There are already countless examples of these types of datasets, but as more data is generated across the economy (through Internet-of-Things initiatives like smart cities, smart farming, industry 4.0, etc.) investors could potentially be offered ever more detailed insights into the businesses or assets that interest them.

Why are investors seeking new and diverse sources of data?

Investors have always sought to be the most well informed about their market or particular asset class. A couple of centuries ago, the use of carrier pigeons was all the technology you needed for an edge in data dissemination. But modern market infrastructures operate so close to the speed of light that getting a significant speed advantage has become increasingly difficult. Rather than be the first to get market data, investment firms are seeking out more diverse sources of information for potentially richer and deeper insights into a company or asset.

What are the challenges of managing alternative datasets?

Most market data tends to be relatively simple to store in relational databases made up of tables (columns and rows). But alternative data comes from a variety of sources and structures – such as geospatial data, textual content and documents, video, audio and other bespoke formats. These are, by their very nature, harder to store, manage and interrogate for the business insight they may provide. Disparate data types often require disparate systems or technologies to enable them to be useful for investment purposes.

Can you easily manage alternative data alongside market data and reference data?

Most traditional systems for managing market data and are not well-equipped to handle this diversity because they tend to be built on more rigid relational data models. Alternative data storage technologies (e.g. data lakes) may be able to manage the diversity of data types, but provide little or no business structure to enable meaningful insight to be gained from it.

The Xenomorph platform is built on a hybrid object/vector database that can easily incorporate a wide variety of data types, structures and relationships. As an example, Bio-Tech investment experts RA Capital recently selected Xenomorph’s platform as a comprehensive enterprise data management solution – not only to manage securities and price data, but also information on drugs, catalysts, events, and clinical trials. Other clients use the system to manage weather and other energy-related trading data. If you have a requirement to manage market data, reference data, and alternative data in one system, book a demo to see how we can help.