Alternative data has exploded onto the investing scene. Today’s professional investors live in a data rich world with a significant, and even overwhelming number of data providers. Some managers were early adopters of alternative data and have since built out internal teams dedicated to sourcing and integrating alternative datasets. That’s great for large funds who got in early, but what about everyone else? A recent survey by Greenwich Associates (which included 46 asset managers and 23 hedge funds) cites that ~80% of respondents want greater access to alternative data sources. Today, 80% of investors do not use alternative data, which begs the question, what are the barriers discouraging funds from taking advantage of alternative data? The same survey also asked respondents to identify obstacles to using alternative data, results below: Obsticles Survey Results The obstacles represent common feedback we hear from managers, and boil down to three key pain-points:
  1. Fit / Infrastructure: “I don’t have a data team or a quant strategy, so how am I supposed to get into alternative data?” or “I don’t have the time or the headcount to search for data providers or to work with raw data”
  2. Fatigue: “It is overwhelming to deal with all of these data providers”
  3. Value & Budget: “We can’t afford to spend that much on alternative data, we are already paying so much for research”
Let’s break these down.
#1 - Fit / Infrastructure
A big misconception with alternative data is that funds need to have a data science team and/or quant strategies to deploy insights and value from data. While a dedicated team may be helpful in order to work with large amounts of raw data, you don’t need one to tap into insights generated from alternative data sources, especially when you are first starting out. Some data providers have solutions which allow you to get the most out of alternative data with minimal investment in terms of additional headcount, workflow changes, or infrastructure. Further, consultants and aggregators exist to make the discovery and analytical process easier. Several options exist to plug into alternative data quickly and with minimal friction:

Plug-and-Play Providers

Data Brokers

  • Insights from alternative data delivered in easy to digest research format
  • Can do deeper dive analysis and deliver structured data
  • Work with you to understand knowledge gaps / questions then recommend datasets that could be a good fit
  • Collect datasets from multiple sources, overlay analytics, and present data in clean way
Sample Firms
Pain-Point Addressed
  • Fit / Infrastructure
  • Fit / Infrastructure
  • Fatigue
  • Engineers and analyst teams process and analyze the data for you
  • Research driven by owned data, scope limited to data coverage
  • Ownership of dataset increases usefulness of insights
  • Aim to streamline discovery and find the best dataset for your needs
  • Additional cost for the “discovery service”
  • May recommend raw data that could be hard to process
#2 - Fatigue
The concept of fatigue as an obstacle includes both actual experienced fatigue and potential fatigue (e.g. foreseeing the level of fatigue a potential task would cause). Seasoned users of alternative data experience Discovery Fatigue, which simply put, just means they are tired of dealing with provider after provider. The universe of alternative data providers is large and trying to evaluate everyone is tiring. Funds that have yet to incorporate alternative data have a different, but related problem - they are overwhelmed by the universe of potential providers that exist. It is very easy to imagine the Discovery Fatigue they will eventually experience by trying to evaluate so many providers. Whether you are fatigued by the process of evaluating data providers or overwhelmed by the future task of doing so and unsure of where to start, we would recommend the following strategies to help funds focus their search, find more relevant data, and reduce time spent: Focus on your questions and needs We see many funds that endeavor to evaluate as many data providers as possible without a clear focus or objective. This strategy will result in fatigue and dissatisfaction, and could lead to settling for datasets that are not a great fit or are not high quality. Rather than seeking to understand all the providers that are out there - first, ask yourself what are the key names you care about and what questions you need to answer on those names. Once you know where your knowledge gaps are, then seek to identify datasets that can deliver the information you seek. Scale, scale, scale Most companies don’t start with a fully formed product and go-to-market strategy. They develop a prototype, test, and iterate. The same is true for funds that have large data teams today. They started by integrating a few datasets, testing their results and proving use-cases, then scaling up from there. Funds that maximize efficiency only focus on a few names - either the largest positions or the names with the largest knowledge gap. Then, seek providers with datasets that shed the most light on those names and their key debates. It can be tempting to view alternative data as an aggregate database of incremental data points and to try to build teams and systems to process and understand everything; but that is a recipe for disaster. In alternative data, diversification is not necessarily your friend and can lead to analysts trying to extract insights from a compilation of datasets they don’t really understand - often times it will seem that different datasets are saying different things. Focus on the most important question you have to answer and find the best data to help you with that. Then move on to the next question. There is no “one size fits all” dataset. Our resources page contains an expansive database of alternative data providers which can be used to help define and narrow your search. Be methodical with your process  Every provider’s pitch is different and sometimes will spin their datasets in very different and often confusing ways. This isn’t because they are being dishonest, but datasets are nuanced and every provider is different. Seek to make the evaluation process about your needs, not their pitch. We suggest using a standardized evaluation process across multiple providers.
#3 - Value & Budget
Value: Will be self-evident if you employ a search process that seeks to identify the top data providers who can provide critical information you need on your top priority names and key questions/knowledge gaps. Once you’ve gone through a disciplined search process, found a dataset that aligns well with your needs, and are ready to purchase, the question of “is purchasing this dataset money well spent?” should be answered and should be an unequivocal yes. Budget: Marginal value is not the only factor in a buying decision. For some managers, total budget is a concern. Pressure on fees and third-party vendor spending (including research) is not a new concept. Now more than ever, managers are taking an increasingly discerning look at their research spend and trying to identify which providers add real value to them. While a dataset’s value may be clear, overall budget constraints may override. A couple of things to consider:
  1. Take a brutal look at your existing subscriptions and third-party providers. Which ones are nice-to-have vs. need to have? Which provides the highest and lowest ROI? We are hearing that managers, rather than growing their research budget, are shifting dollars away from traditional research toward alternative data providers
  2. Get creative. Utilize commission sharing agreements (“CSAs”) and other soft-dollar arrangements to help pay for valuable research. You could also find other analysts in the fund that are interested in getting this data for other names in order to help share the cost.
Are you facing any issues we haven’t covered? We would love your feedback, feel free to comment or email me at [Sassy_Social_Share]
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