Business Intelligence & Analytics
We deliver the reporting and self-service analytics that give traders, risk, and ops teams one trusted view of the numbers — governed, fast, and consistent across the desk, so meetings start from agreement instead of reconciliation.
Book a Consultation →Typical engagement deliverables
- Governed semantic models
- Executive and desk-level dashboards
- Self-service analytics enablement
- Report rationalization (retiring redundant legacy tools)
Technology & partner ecosystem
Interactive demos built with business intelligence & analytics.
Live Power BI dashboards on synthetic data — click any to load it in place.
Billing Accuracy & Attribution
Trace billing accuracy and its attribution across meter, billing, financial, and revenue systems — synthetic utility data.
Where business intelligence & analytics shows up in energy.
A public-power utility took analytics off its billing system and cut a 3-hour report to 40 seconds.
A large municipal utility's billing system couldn't support analytics, and ad-hoc extracts were degrading it in production. We moved reporting onto an elastic cloud analytics layer with one governed source of truth.
A power utility cut its load-forecast error from 18–25% down to 2.75%.
An 18–25% swing between load forecast and actuals meant costly real-time market exposure. The strategic target was under 4%. We delivered forecasting that brought average variance to 2.75%.
A battery operator can see the nodal spread before it's obvious — and dispatch against it.
We built NodalAlpha — a battery-storage arbitrage engine that forecasts nodal basis spreads, ranks every ERCOT and CAISO node by forward opportunity, and schedules charge and discharge against a sized unit. Backtested on a full year of real ERCOT settlement prices, energy arbitrage only.
A power trading business got a historic, attributable view of position and P&L — one version of the truth.
Market and credit risk, hedge effectiveness, and P&L attribution were scattered and hard to trust. We built an enterprise risk data warehouse — with VaR by Monte Carlo and full position and P&L attribution — as one consistent source across the organization.
A trading desk stopped reconciling every dashboard by hand and started trusting one governed source.
The desk had dashboards, but nobody trusted them — every number got re-checked against a private spreadsheet before anyone acted on it. We rebuilt the reporting on governed definitions and visible lineage that reconcile to the books, so the dashboard became the number people work from, not a second opinion.
A commodity trading firm made compliance proactive — catching trading aberrations before they became violations.
Trading commodities across global markets meant living under many overlapping regulations. We built transaction surveillance that flags untypical trading activity for investigation before it becomes an enforcement problem.
A retail energy provider learned which customer and product mixes were actually profitable.
In deregulated markets, a complex mix of products and contracts made it hard to tell which customers and deals paid off over time. We built the analytics to evaluate profitability by product, customer, and deal — and the churn behind it.
A power marketer automated the gross-margin and variance analysis it used to assemble by hand.
Forecast-variance and realized/unrealized P&L analysis was manual and slow. We built the data foundation and reporting to automate gross-margin analysis, cash-flow forecasting, and budget-to-actual variance analysis.
An oil & gas major built one risk repository for market, credit, and Dodd-Frank compliance.
Market risk, credit risk, and Dodd-Frank compliance lived in different places and rarely agreed. We led the design and delivery of a single risk data warehouse — one repository, one set of numbers the desk and the regulators both rely on.
A multi-business-unit retailer got one BI master plan instead of four disconnected ones.
Four business units were each adopting BI their own way. We built an enterprise BI master plan — one assessment, one architecture, one adoption path across wholesale/risk, C&I, mass markets, and home services.
How a business intelligence & analytics engagement runs.
- 01
Assess
We start by understanding the current state — the data, the systems, and the real problem underneath the stated one.
- 02
Roadmap
We prioritize the work that will actually move the business, sequence it, and build the case for funding it.
- 03
Build
We deliver the pipelines, models, and reporting — working alongside your team, not in a silo.
- 04
Run
We hand over a solution your team can operate and evolve, with the enablement to keep it running after we leave.
Perspective on business intelligence & analytics.
Waiting for month-end to find out you lost money
A daily, non-accounting-grade P&L won't satisfy your auditors — and that's the point. It tells the desk what moved and what to do about it while there's still time to act.
Jul 28, 2026 · Read →The margin you lose to a wrong bill
A wrong bill looks like a service problem; it's a margin problem in disguise. The win is quantifying the chain from error to erosion to bad debt so accuracy competes for investment on its real return.
Jul 26, 2026 · Read →Seeing risk at every level of the desk: market and credit together
A single VaR number for the whole book hides more than it reveals. The useful view is risk decomposed — standalone, incremental, component — down to the trader, desk, and commodity group, with credit exposure sitting right next to it.
Jul 11, 2026 · Read →