Much has been said in the data world about ideal states: “Data at your fingertips”, “Plug-and-play insights”, “Empower your whole organization with data”, and “Democratizing insights from data”. This list is long, and to be honest, does sound great. There is value in setting such big-picture goals in an organization, but often the route to go from here to there is a lot harder than initially considered. Data Warehouses such as Snowflake are often proposed as the solution that ushers in this step-change in organizational performance. If only your data was one well-defined, easy-to-understand, definitely maintained query away, everyone would be able to incorporate it into their day-to-day decisions. At Eagle Alpha, we think Data Warehouses can be useful but often find ourselves in a chicken-and-egg situation with them. As mentioned, Data Warehouses do their best when the data is organized, but who is supposed to do that organizing and how? How do you know what to set up so that data users in your organization can hit the ground running when all you have are 300,000 poorly named CSV files of unknown content scattered across 1500 sub-directories of sub-directories? The ideal starts to drift pretty far away when that is the reality – a reality that many organizations face even with the internal data that they nominally can control the production of. Add in the requirement to incorporate external third-party sources of data, and soon addressing the ambiguity and confusion that stems from this data circus becomes the all-consuming job of many data engineering teams.

As asset managers invest resources into building out alternative data initiatives, data sourcing is seen as an integral part of the process, with in-house and outsourced professionals finding and introducing datasets and data vendors. With datasets rapidly growing in price and impacting the growth in spending of firms buying alternative data, the data sourcing function is now more critical than ever before. These results are based on our first-of-its-kind survey of 24 buyside funds with varying levels of alternative data experience.

Exabel and Eagle Alpha’s partnership takes on this challenge end to end. By combining Eagle Alpha’s deep knowledge and expertise in the alternative data ecosystem with Exabel’s powerful analysis platform, investment teams can finally tackle these operational and technical challenges holistically, delivering efficient data evaluation, at scale.

The recent rally off the June 16 lows (S&P 500 +5.36%, Nasdaq +7.57%, Russell 3000 +5.42%) has made some crowded shorts more squeezable. We have seen the number of highly squeezable stocks in our Squeeze metric since June 15 more than double, and our average squeeze score has increased by over 250% (from 12.22 to 31.48). The Consumer Discretionary (primarily auto) and HealthCare (Biotech) sectors have the most constituents in the top 25 most squeezable stocks.