OpenDataDSL Readies its New Data Management Platform Targeting Energy and Commodities
OpenDataDSL is a startup vendor in the energy and commodities data management space led by Colin Hartley. Its solution is SaaS in the Azure cloud and Colin describes it as an open platform in which the user can do a lot all by themselves – a sort of toolkit for data management that is both flexible and customizable, he told me. He also told me that it has created a new domain specific programming language (DSL) that it developed specifically for data management usage, and the solution uses the customer’s active directory for access and security. Colin gave me a short demonstration to illustrate what he told me about the solution.
Various types of data can be collected and created using the solution. Market data and any public data available online can be pulled into the solution along with any internal data created within the user’s business. It also holds smart data or data created from any of those sources within the solution and this smart data can be created by applying simple or complex expressions making it very simple and easy to create any sort of derived price curve, for example. The curves are always built on demand and are thus real-time. As a multi-tenanted solution, a customer’s data is of course private to that customer.
Entering the solution is via a configurable dashboard and the system is essentially self-onboarding for users, he told me. Metadata is collected about all data sources, and it is this that is used to search on available data in the solution. The solution is optimized to store data and present it rapidly using MongoDB. Metadata can include geodata so that the solution can be used to view data on various kinds on a map.
What Colin thinks is unique about the solution is the curve management section. Here, users can define and build curves, build validation rules, and use workflow to create, validate and distribute curves. This includes defining cut off points in time for data to become available for curve calculation and alternative calculation or approximation approaches should that data be unavailable on time via substitution rules. Additionally, workflow can be used to help decide whether to export the data or not for use in other systems like an ETRM for example. Essentially, the solution allows the creation and maintenance of derived curves as well as set up rules and workflow to ensure quality, validation, distribution, and substitution. “The user can control what should happen and when it should happen,” he said. Another area of the system provides insight reports allowing users to set up and define any type of graph to display and visualize data. Overall, the solution looks powerful, customizable, and flexible.
After a long POC, the vendor has its first customer onboard and is now targeting utilities, oil & gas companies and so on – any firm that uses an E/CTRM and needs to handle data and curve management effectively. Colin also is looking at the ags and softs side of things for users as well as it has a lot of data from that segment available too.
The solution should find a ready market as a newcomer on all new technology in an area that remains a key focus area for many energy and commodity firms – data management. It’s curve management ability and the overall configurability and flexibility probably owes a lot to having done this once before with Datagenic and, because of Colin’s connections in the industry as well, the solution should do well. The ability to ensure timely, valid and accurate time series data for things like pricing as well as capture a and utilize a wide range of data now available across the internet, is a key aspect of commodity trading and data management platforms that are cloud-native, nimble and cost effective ought to do well in this market.
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