The challenges of managing data for new trading strategies and future-proofing your enterprise data management system and processes.

Many asset or fund managers are looking to diversify their strategies when looking for trading opportunities within financial markets. Part of that might be due to reduced margins in older methodologies or increased competition. New strategies often encompass a wider range of data and/or analytics to allow creation of more advanced or bespoke trading ideas. Those institutions who are ahead of the curve in that respect can look at improved time-to-market of those ideas and competitive advantage.

Historically, enterprise data management systems (EDM) have had a “traditional” data model built around reference data and market data – such as prices and rates – on which trading strategies can be built. EDM systems vendors have long-established products which encompass traditional data models. The challenge presented is the extension of those data models to include additional data pertaining to the new strategies.

This new data typically might be company information, assets, economic measures or other fundamentals. It might also be very specific information based upon companies in a particular industry sector, such as energy or technology. This data falls outside the ‘traditional’ data model of nearly all EDM systems as they were designed for a fairly general purpose and not for niche or specialized markets.

Those enterprise data management systems that are based upon older, stringent relational database management technology (RDBMS) tend to require significant development effort to include this new data as their existing data models are generally fixed towards the perceived ‘standard’ model for asset types such as an equity or a bond. Coupled with that is the requirement to be able to store data of any type, over any frequency and from multiple sources. In addition, the various EDM system tools and APIs need to access that data and make it available for users to consume and analyze.

The ideal scenario is an enterprise data management system which is totally independent of the data that is stored within it, in the sense that the data model can quickly and easily be extended to include pretty much anything. More importantly, the same EDM software must be able to work on this new data without change, and users of the system should be able to configure any new trading strategies without the EDM software vendor needing to be involved – thus minimizing costs and promoting business user ownership. This approach also reduces the perennial headaches of backward compatibility and upgrades, which for many EDM system users present huge problems and unnecessary monetary and opportunity costs if the data model has been customized or extended by users and is now ‘out of step’ with the original supplied by the vendor.

In essence, we need the enterprise data management system to ultimately be ‘owned’ by the users, with the knowledge that they can configure their new trading strategies as and when they need them, on their own and without fear that when a newer version of the software becomes available everything they have done will either be broken or require significant re-work at a huge cost. The EDM system should grow with the firm and be future-proof to all the data management requirements and business initiatives that may be needed in new areas of the market.