We work with dozens of Alternative Data providers and speak with hundreds more about how their data can create value for investors. Many data vendors correctly see Hedge Fund managers as sophisticated, data-savvy investors with vast resources. But that does not necessarily mean they are ready to take raw, unprocessed data and quickly identify value. We wrote about a similar misconception when we discussed “Free Trials” here.

 

We can understand how some vendors might think their data is valuable “as is” and that the resources required for data cleansing are not necessarily a good investment. The most sophisticated funds, usually large multi-strats or platforms with internal data teams, often explicitly request raw feeds because it gives them more visibility into the biases and nuances of the underlying data. Additionally, if a fund believes that their “edge” comes from their ability to clean, aggregate and model unstructured data, it makes sense that they’d want to consume it at an earlier stage in its lifecycle. If done properly, this can allow for more unique insights that aren’t available to buyers of a more aggregated product.

 

In reality, even for the most sophisticated funds, finding unstructured data that allows for material and scalable “edge” is the exception and not the rule. In fact, it’s certain that a vendor is inherently prolonging their sales cycle when their data is not of a certain quality.

 

Let’s consider an e-mail receipt dataset for a well-known brand like Lululemon. If each row of your data corresponding to a Lululemon purchase has a different identifier, even if it’s just a spelling discrepancy (EX: Lulu / Lululemon Athletica / Lululemon) it will be more difficult, or at the very least, take more time to consume. Correcting semantic text is unlikely to be a fund’s differentiator. Even if a PM or Analyst is excited about the promise of a dataset, it may be an engineer who deprioritizes a project because the time investment is relatively high, and the yield is yet unknown.

 

At Maiden Century, we help vendors with data processing like mapping to an entity or ticker, but the data must meet a certain threshold for our team to expedite that journey…not unlike a data team at a Hedge Fund. Our advice to data vendors is to help managers see value by starting with the answer. Here is what we mean.

 

Regardless of the size or strategy of the manager, they all want your data for one thing – Alpha. The way they generate Alpha may differ from fund to fund but that is the underlying reason they would look at your data in the first place. You know your data better than anyone, and by working backwards from a result (EX: Generating ROI on a potential trade your data highlighted) you can show them how they could extract value from your data. This is true with both large and emerging managers.

 

A large fund typically has a team of data scientists and, on a relative basis, is not resource-constrained. But they are also bombarded with hundreds of trial requests and data evaluations. Starting with the answer helps them see why they would want to evaluate your data further. Having clean data helps them justify the time investment & get to the value prop faster. Conversely, a smaller manager who may not have dedicated data resources is not able to work with raw data. They need to be shown value in crisp and clear terms, upfront.

 

Regardless of the size & sophistication of a fund, we recommend using your data to tell a story, whether a thematic trend or data leading up to a particular event, such as earnings.

 

Starting with the answer is easier said than done. This requires you to do the cleaning ahead of time and to take the next steps of data & fundamental analysis. Our team at Maiden Century does a lot to bridge the gap between data and eventual monetization. We will map brands to the right ticker and KPI and build a predictive model that is most accurate and monetizable. We will also run backtests to help investors immediately see the value of your dataset in combination with other sources.

 

This process makes funds more willing to invest their time to trial your data. Once they take a deeper look, they will likely provide feedback on where it’s valuable and where your offering can improve. Or better yet, they may finally be willing to consume your raw, less structured data feed for bespoke analyses once they’ve seen the promise of leveraging the data for simpler use cases.

 

So, the next time you get the urge to talk to a manager about the attributes of your panel or the uniqueness of your source in hopes of starting a trial, remember: start with the ANSWER. And if you need a helping hand or even a sounding board, reach out to us.

 

Inquire on how to become a data partner today.

 

Jack Killea, VP of Partnerships