Making data science usable, accessible and valuable to investors, at a price point that makes it affordable for ALL investors, is not just hype, it is a reality with EDS.
But what if you have quantitative personnel in-house or are thinking of building it yourself?
Then the question becomes Build vs. Buy. Of course, the rational for "Build" is straightforward - if we have the resources, and we view it as a competitive advantage, shouldn't we just build it internally?
But the answer is less so and buying may be a better option.
When Could Buying be a Better Solution?
A quantitative platform already exists and has many of the functions the organization needs. Remember, building a quantitative framework is a multi-disciplinary exercise requiring several unique resources, such as software development, user-interface design, fundamental and quantitative domain knowledge, and IT resources to support, train and evolve.
The platform is flexible and can be customized to the needs of the organization.
The platform is largely debugged, is easy to deploy, and customers are not locked in for an extended period of time.
The vendor can provide training, manuals, and ongoing support.
Allows in-house quant teams to focus on creating winning investment strategies, not building, training and supporting a software platform.
The provider is iterating and advancing the product on a regular basis (at EDS we are improving the platform at a rapid pace).
Buying also allows you to deploy at a much quicker pace, accruing benefits now, versus the added cost and length of time it takes to build internally.
Mark Lutchen, former Global CIO at PwC has this to say on the topic:
"When evaluating whether to buy or build, it's critical to thoroughly understand total costs during the software lifecycle which is typically seven or eight years. This step is important, Lutchen says, because 70 percent of software costs occur after implementation. A rigorous lifecycle analysis that realistically estimates ongoing maintenance by in-house developers often tips the balance in favor of buying."
Is Building an Internal Platform Really a Competitive Advantage?
Another important consideration, perhaps the most important, is whether a quantitative platform is a "core competitive advantage" or a "means to an end" for your business. The end being more informed decision making, increased collaboration and an increase in actionable insights. At EDS, we believe that the majority of quantitative platforms are custom built, thus are very expensive and time consuming to build, evolve and support - the message from this small community is that these platforms are a key determinant of out-performance and a unique internal competitive advantage. However, at EDS we believe its not the platform itself, but the decisions it enables that are important. Let's repeat that - Its not the platform itself, but the decisions it enables. That's why at EDS we are leveling the playing field by making data science accessible and usable to all investors - actionable insights in a lightening fast, intuitive and off-the-shelf solution, so that investors have more time for the critical decisions that enhance performance and increase assets - in this scenario, the platform becomes a conduit, as it is for most software, and a firm should choose the conduit that has the least friction, both today and for the future.