How event-driven automations in OpenDataDSL eliminate manual intervention, reduce latency, and let your data pipelines run themselves.
An automation in OpenDataDSL is a rule that says: when this event happens, do that action
Many organisations assume that managing market data feeds in-house is straightforward: connect to a provider, ingest the data, move on. In practice, it is one of the more demanding operational commitments a data or technology team can take on. This post sets out the risks that are frequently underestimated.
If your organisation is running a legacy data management system, you already know the signs. Slow curve builds. Software bugs that have been there for months. Data quality issues that require manual fixes under time pressure.
The question isn't whether to move - it's whether moving is worth the pain.
We think it doesn't have to be painful.
AI-readiness in energy and commodity markets isn't a data science problem. It's a platform decision. And most organisations make it too late — because they bought a product when they needed a platform.
For teams still managing forward curves manually — pulling data from multiple sources, re-running models, rebuilding reports — March 9 was a stress test they didn't ask for.
OpenDataDSL gives you the platform to build forward curves that are as dynamic as the markets you trade.
Every analyst knows the drill. Pull power prices from one source, gas prices from another, dig out the heat rate from a spreadsheet buried in a shared drive, wire it all together in Excel. By the time you’re done, the prices have moved. Rinse and repeat daily.
That’s the hidden cost of dumb data — values with no understanding of what they mean or how they relate. In energy and commodity markets, that gap between raw data and actionable insight is where time, accuracy, and competitive edge quietly bleed out.
Smart Data fixes that.
The ETL vs ELT debate has its merits, but for those of us in energy and commodity markets, it misses something fundamental. Both patterns treat transformation as a monolithic step, when in reality there are two very different kinds of work: enrichment and transformation. They are not the same thing, and conflating them creates real problems.
That's why we think the energy industry needs its own pattern: EELT — Extract, Enrich, Load, Transform.
There's a moment every energy data manager remembers. A regulation changes with a 90-day deadline. A critical data source goes dark. A new trading partner needs integration and your vendor says "we can scope that, I'll have someone reach out about timeline." That's when you realize: you don't control your own infrastructure. Your vendor does.
And they're busy with someone else's emergency right now.
A platform that transforms operations by delivering intelligent data management, automated processes, and actionable insights that drive both operational efficiency and regulatory compliance.
A platform that has AI-powered data ingestion that adapts to changing formats and automated actions triggered by real-time performance thresholds.
This blog lays out the problems with legacy, locked-down data management technology and services.
The solution is to use a true data management platform that empowers companies to create, enrich and extend their data experience with an extensible data management platform like OpenDataDSL.