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Managers are Spending Millions on Data. What are They Getting in Return and How Much Value is Left on the Table?
A leading law firm conducted a survey that indicated that 92% of investment managers use Alternative Data and 78% of them plan on increasing their data budget in the coming year. On average, managers pay about $1mm per annum for their Alternative Data subscriptions, although admittedly, the range is wide with the largest investment shops, especially multi-manager platforms spending many multiples of that.
But what about the value money managers are getting in return for that data spend? ROI on data can be tough to nail down given that Alternative Data is only part of the process, with many inputs coming from more traditional sources like sell-side research, management calls, and the network of other investors managers can access. Still, most managers admit that there are other challenges with ROI on Alternative Data. Our conversations with managers point to one common problem: many feel that although there is value in this data, a large part of it is being left on the table, not monetized by the money manager.
We described this and several other key challenges in our recent eBook: Top Challenges of Implementing Alternative Data in Investment Management.
One obvious contributor to the problem of not fully monetizing Alternative Data is the sheer volume of it and the resources needed to extract insights. With some datasets measured in terabytes, sifting through this data in search of signal requires sophisticated technological systems and data science skills. Not all managers are experts in building scalable data management technology or running large data science operations, hence a lot of signal is lost in the noise. They are money managers, not data managers, after all.
The standard Alternative Data use case with many smaller and mid-size managers is more “pull” than “push”. If an analyst is working on an investment thesis and realizes they need data, for example, consumer spending trends, they may consult their spending data provider, or initiate a search for such a vendor. Unfortunately, in the latter case, by the time they evaluate and onboard a new vendor the opportunity may have vaporized. And if they did happen to have the right data on hand, their value is usually limited to short-term ticker-specific trends. They often lack the expertise to stitch together years of historical data or look at the whole industry for context and peer comparisons that could be vital to the thesis.
Besides analyzing a single investment, the value being left on the table is all the other companies that go unnoticed. What if a competitor of your investment in the restaurant space is going through an inflection? Managers will miss that if they do not build a sophisticated alerts system. Or if one of the KPIs you are monitoring shows no significant changes, but another KPI you are not actively monitoring just dropped off the cliff? Without a robust reporting and visualization front end, you may miss that signal. Alternatively, what if a sub-industry you are following is experiencing a slowdown? To pick up on that signal early investors need to have a way of aggregating data or looking at multiple trends at once. Given there is no “push” delivery in most of the smaller Alternative Data operations, signals such as these are lost. This is the main reason why managers feel that much of the data in their (expensive) data sets remains untapped. And they are absolutely right.
To monetize a larger portion of the Alternative Data investment some managers choose to pay up. They invest in monitoring and alerting systems and hire top technology experts to build these systems for them in-house. They also build out large teams of data scientists to handle data mapping, tagging, normalization, staging, and analysis.
But even that may not always be enough. Portfolio Managers and data scientists often don’t speak the same language and some ideas are inadvertently lost in translation. Technology teams, no matter how large, still take lots of time to build new data processes and integrations can take months even with a large tech team. What’s worse is after a dataset is integrated, the manager may find that the Alpha has already been arbitraged away or is in some other way disenchanted with the data.
Some managers prefer to partner with a technology provider that has already built the needed “pipes” into a large number of available Alternative Data sources. Not only does this reduce the time to value for managers but it can help them bypass enormous investments in tech and data science while still getting to that “push” state with Alternative Data.
Managers that lean on battle-tested Alternative Data aggregation systems also realize that they can leverage Alternative Data in ways that were not possible to them before, using it for macro and thematic analysis, risk management, idea generation, and a deeper understanding of a company’s true drivers of revenue (KPIs), just to name a few.
Do you feel that some of the value in your Alternative Data is being left on the table? Give us a call and we can help you quantify it based on the work we have done with similar managers.
Learn more about ways managers overcome the ubiquitous challenges with Alternative Data: Top Challenges of Implementing Alternative Data in Investment Management.