Navigating the Alt-Data Ecosystem: How to choose the right partners to improve ROI.
11 Jan 2021
The use of alternative data within the investment process is on the rise. Previously the domain of sophisticated quantitative investors, 2020 saw “alt data” go mainstream, with fundamental investors expanding data science capabilities and carving out budgets to procure alternative data, alongside (or in some instances in lieu of) traditional research. Financial Services firms spent $1.2bn on alt data in 2020 and this number is expected to rise to over $17bn by 2027. For context, the global investment research market currently stands $12bn annually.
As the popularity of alt data rises, so to do the number of platforms, marketplaces and tools to integrate and extract value. In a recent survey, 70% of investment managers stated they were using alt data, but less than 25% claimed to be effective at integrating it into the investment process.
As a result of this contrast, there is an attraction for both data providers and buyside firms to leverage tools and marketplaces to support their alt data strategies. However, experiences have been mixed at best – with many platforms failing to deliver on their promise.
Below, we detail the various services offered by such platforms and some of the challenges and questions that should be considered when looking at third party solutions to support your strategy.
Over the past few years, a number of marketplaces have emerged that provide various services to address information and operational issues that impede the alt data market. These can be grouped up into the following:
Discovery: The most common solution offered by marketplaces is content discovery. This can take the form of a directory (such as https://alternativedata.org/), personalised scouting services, “alt dating”, sales outsourcing or events.
Some key questions to consider when evaluating discovery services are:
- What is the business model and who pays for the service (the consumer or the provider)?
- What is the track record of the vendor in successfully placing and developing long term relationships?
- What checks are performed to ensure the data has been collated and anonymised in line with legal requirements?
- How are data providers assessed for quality and investor fit?
- What is the contractual relationship and is there potential to use datasets in a “Netflix” style subscription model?
- Is discovery digitally delivered or via sales and marketing professionals – what is the skillset and value of search people?
- How broad is the network of buyside firms and do they have potential the expand addressable market (beyond the traditional quant shops that have invested heavily in alt data already)?
Content delivery: Recognising the bulk of the challenges of operationalising alt data lie structuring, organising and delivering data in a way that is usable, some vendors have moved beyond discovery to help reduce the effort of creating actionable datasets. These services can include dataset construction and normalisation, independent verification, filtering and smoothing out gaps, matching to investable assets (i.e. tickerisation).
Some key questions to consider when evaluating delivery services are:
- What processes (e.g. NLP, clustering) are used to convert information into time series datasets?
- What format is the data delivered (e.g. flat file, spreadsheet, API)?
- What independent quality checks are performed?
- What assumptions and techniques are used to clean up and smooth out data and does this add any risk/noise?
- What level of flexibility is there in matching datasets to investable asset?
- What is the frequency of updates?
- Are there clear feedback loops between data provider and investor and usage and value?
Integration: The last mile problem in alt data, is converting information into signal. To do this, a broad range of statistical, machine learning and financial modelling techniques are employed. These services are delivered by a myriad of firms within the ecosystem and often take the form of consulting services, online back-testing tools, portfolio construction tools and python programming libraries.
Some key questions to consider when evaluating integration services are:
- What is the deliverable of the solution – a set of actionable investment strategies, or “enablement” tools?
- How transparent and flexible are the statistical and financial modelling techniques used (e.g. black box or ready to cook)?
- What level of programming and modelling skill is required to use the service effectively?
- Can multiple datasets be used in conjunction to created blended indicators?
- What level of flexibility is there in defining investment universe and portfolio construction rules?
- What visualisation and analytical tools are available to integrate and assess alt data signals alongside traditional research and investment analysis outputs?
- How are ongoing changes, maintenance and new signals handled and delivered?
The use of alt data within the investment process is now a mainstream concern for investment managers. However, the gains and benefits of use are sporadic at best. As adoption continues to rise, it will become imperative for investment managers to not just acquire data, but effectively integrate into the investment process.
At present, the market for solutions to support discovery, delivery and integration are nascent, but evolving rapidly. Managers that are able to quickly identify the right partners will have a competitive advantage in demonstrating process to asset owners and driving performance.
If you would like access to Invisage Alpha’s Alt-data marketplace comparison sheet, or to learn more about how Invisage helps data providers and investment managers maximise the ROI from alt-data, please get in touch on email@example.com to set-up an introductory call.