The evolution of energy analytics: Moving beyond the "build vs. buy" trap

First published on: 18/5/2026

For decades, energy companies faced a familiar dilemma when it comes to analytics and risk modeling: building bespoke internal tools or relying on the analytical capabilities embedded within an Energy Trading and Risk Management (ETRM) system.

While ETRMs can handle trade capture, confirmations, compliance, settlement, and profit and loss tracking, their analytical layers were never designed to support today’s modeling intensity, uncertainty, or speed of decision‑making.

As markets grow more complex, firms are discovering that the real constraint is no longer whether to build or buy, but where advanced analytics should live within their technology stack.

The result? Many organizations find themselves trapped in a model‑limited environment, where analysts spend more time maintaining tools than uncovering insight.

1. The death of the "build vs. buy" dilemma

Historically, a "build" strategy started with a few spreadsheets and eventually ballooned into a fragile web of independent tools. Conversely, "buying" meant multi-million-dollar implementations that rarely delivered more than 80% of required functionality.

The modern alternative is to assemble. By leveraging a cloud-native Software-as-a-Service (SaaS) platform, companies can access sophisticated, pre-vetted energy models on demand. This composable approach allows firms to chain together specific analytic components — such as price simulation, asset optimization, and contract valuation — into a proprietary workflow without the overhead of building the engine from scratch.

2. From spreadsheet vulnerability to SaaS scalability

Spreadsheets remain a staple of the industry, but as portfolios grow, its strength — flexibility and low cost — become liabilities. In energy markets, where a single miscalculation can lead to millions in losses, the human error inherent in complex spreadsheets is a systemic risk.

Beyond security, the primary limitation of spreadsheets is concurrency. Most spreadsheet models are single-threaded and deterministic. They provide a "best guess" but struggle to run the thousands of simulations required to understand volatility. Transitioning to SaaS analytics isn't just about moving to the cloud; it’s about moving to high-performance computing (HPC) environments that can process complex Monte Carlo simulations in minutes rather than hours.

3. Methodology: Why fundamental models aren't enough

A common misconception in energy risk is that a robust fundamental model (which represents market supply, demand, and grid topology) is sufficient for risk management.

While fundamental models are excellent for long-term resource planning and single-path price forecast creation, they are inherently limited for risk analysis. Because they provide only one view of the future, they cannot quantify downside risk or the effectiveness of a hedging strategy.

Simulation-based modelling is the necessary partner to fundamental analysis. By running thousands of price paths around a fundamental forecast, simulation models produce a distribution of outcomes. This allows portfolio managers to see not just the expected margin, but also the Gross-Margin-at-Risk (GMaR). In an era of intermittent renewables and battery storage, where volatility is the only constant, simulation is the only way to capture the full distribution of portfolio risk.

4. The human element: Empowering the better quant

In energy analytics, there is often an assumption that a company's unique internal model provides a competitive advantage. In reality, such approaches often create or increase model risk,  especially when critical decisions rely on unvetted and insufficiently governed code.

The competitive advantage doesn't lie in the code itself, but in how a team uses the results. By adopting a platform-based analytics approach, internal quants are freed from  maintenance and data plumbing. Instead of spending most of their time in low value activities (think fixing broken links in spreadsheets), they can focus on high-value activities:

• Optimizing battery dispatch strategies.

• Evaluating complex Power Purchase Agreements (PPAs).

• Executing more deals by having rapid, vetted valuations at their fingertips.

Conclusion: Future-proofing portfolios

The evolution of the global energy system has made the old ways of managing risk obsolete. As market dynamics shift toward cleaner energy and drive more complex and volatile price behaviour, the companies that succeed will be those that move away from model-limited choices.

By adopting a cloud-native analytic stack, moving beyond the limitations of spreadsheet-based workflows, and applying tested simulation-based methods, energy firms can finally stop fighting their tools and start navigating the market efficiently. The resulting Decisioning Advantage isn't the model, but the speed and confidence with which insights drive action.

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