Analytics Maturity Assessment

How mature is your energy analytics?

24 questions across six areas — trading & risk, revenue, customer, grid operations, finance, and IT & data platform. Pick only what applies. Your answers stay in your browser; you choose whether to share your result.

Choose your focus ↓

Step 1 · Your focus

Which areas apply to you?

Pick any that fit your role — we'll only ask about those. Not sure? Assess all six.

Commodity value chain Trading & Risk 4 questions Revenue value chain Revenue Assurance & Billing 4 questions Customer value chain Customer Intelligence 4 questions Services value chain Asset & Grid Operations 4 questions Financial value chain Financial & Regulatory 4 questions Data platform foundation IT & Data Platform 4 questions

Scale 1 Reactive · 2 Descriptive · 3 Diagnostic · 4 Predictive · 5 Prescriptive

01 · Commodity value chain Trading & Risk

Can you see standalone, incremental and component risk by trader, desk and commodity in one place?

1 Risk is a month-end spreadsheet 2 You can report total risk, but breaking it down by desk or trader is a manual exercise. 3 You can decompose risk by desk, trader and commodity on demand from integrated data. 4 You project how exposures will move and stress them under scenarios. 5 Live risk decomposition across the enterprise

Can you monitor counterparty and credit exposure and correlate it to gross margins?

1 Exposure checked manually, in isolation 2 You can report current exposure, but not link it to margins. 3 You can see exposure alongside the margins it supports, from integrated data. 4 You forecast exposure and model its margin impact before committing. 5 Exposure-to-margin modeled continuously

How fast can you explain what drove a P&L or MTM swing?

1 Days of back-office reconciliation 2 You can report the P&L number, but explaining the swing takes digging. 3 You can attribute a swing to its drivers across desks and books. 4 You anticipate likely swings from forecast positions and market moves. 5 Automated driver attribution, same day

Can you run what-if scenarios to test hedges and optimize margin?

1 No scenario capability 2 You can report hedge positions, but not test alternatives. 3 You can run manual what-if scenarios on key hedges. 4 You routinely model scenarios to shape hedges ahead of the market. 5 On-demand scenario & optimization at portfolio level

02 · Revenue value chain Revenue Assurance & Billing

Can you detect and attribute revenue leakage across meter-to-cash — unbilled usage, theft, soft shutoffs, tampering?

1 Leakage found by accident 2 You can report totals, but can't pinpoint where leakage occurs. 3 You can trace leakage to its stage and cause across meter-to-cash. 4 You flag likely leakage before it becomes lost revenue. 5 Leakage attributed automatically by stage & cause

Can you quantify the revenue and margin erosion caused by billing errors?

1 Error impact unknown 2 You can count billing errors, but not their revenue impact. 3 You can tie errors to the revenue and margin they erode. 4 You predict and prevent error-driven erosion before bills go out. 5 Error-to-margin erosion tracked and prevented

Do you segment customers to match collection techniques and predict recoveries vs. write-offs?

1 One-size-fits-all dunning 2 You can report collections outcomes, but treat all accounts the same. 3 You segment accounts and match collection techniques to each. 4 You predict recovery vs. write-off and target effort accordingly. 5 Technique scored per segment, write-offs predicted

Is your aging predictive — near-real-time and probabilistic — or backward-looking?

1 Static aging report 2 You can produce an aging report, but it's periodic and backward-looking. 3 You can see current aging refreshed from integrated data. 4 You forecast aging and probable write-offs ahead of time. 5 Probabilistic aging driving proactive action

03 · Customer value chain Customer Intelligence

Do you have a unified customer view combining structured and unstructured data?

1 Data scattered across systems 2 You can pull customer reports per system, but not a single view. 3 You have an integrated 360 view across core systems. 4 Your 360 view feeds predictive models on usage and value. 5 Single 360 view, all sources

How rich is your segmentation — revenue, usage, behavior, economics — vs. basic tiers?

1 A few static tiers 2 You can report a handful of fixed segments. 3 You segment across revenue, usage and behavior from integrated data. 4 Your segments update dynamically and predict behavior. 5 Multi-dimensional, behavior-driven segments

Can you predict lifetime value and churn, and tie interventions to recurring revenue?

1 Churn noticed after it happens 2 You can report churn after the fact. 3 You can explain what drives churn and value across segments. 4 You predict churn and lifetime value and act before customers leave. 5 Churn/LTV predicted, interventions ROI-tracked

Can you predict which customers respond to which channel and offer — demand response, EV, time-of-use rates?

1 Mass campaigns 2 You can report campaign results, but target broadly. 3 You target by segment based on past response. 4 You predict the best offer and channel per customer. 5 Next-best-action per customer & channel

04 · Services value chain Asset & Grid Operations

Do you have a health index and failure/underperformance prediction by asset?

1 Run-to-failure 2 You can report asset condition after inspections. 3 You track a health index from integrated asset data. 4 You predict which assets will fail or underperform. 5 Predictive health index with liability modeling

Are OMS, AMI and SCADA integrated to detect and predict outages and affected customers in real time?

1 Outages reported by customers 2 You can report outages after they're called in. 3 You integrate OMS/AMI/SCADA to see outages and impact as they happen. 4 You predict outage impact and customers affected in advance. 5 Premise-level detection + predictive impact

How granular and automated is load forecasting — interval-level, DER/EV incorporated, variance attributed?

1 Coarse, manual forecast 2 You produce periodic forecasts, but they're coarse and manual. 3 You forecast at finer intervals and can explain forecast variance. 4 You forecast interval-level load, DER included, and self-correct. 5 Interval-level, self-correcting, DER-aware

Can you correlate renewable output, weather, forecast-vs-actual, and hedge effectiveness?

1 Weather is a surprise 2 You can report renewable output after the fact. 3 You correlate output with weather and explain the variance. 4 You forecast weather-driven output and hedge effectiveness ahead. 5 Weather-adjusted output + hedge effectiveness tracked

05 · Financial value chain Financial & Regulatory

How automated is the data integration behind financial reporting and variance attribution?

1 Manual consolidation 2 You can produce financial reports, but consolidation is manual. 3 Integrated data lets you explain variances with some automation. 4 You forecast results and attribute variance automatically. 5 Automated integration + predictive variance attribution

How automated is reconciliation between financial and operational systems?

1 Manual tie-outs 2 You reconcile periodically, by hand. 3 You reconcile from integrated data with partial automation. 4 You catch reconciliation breaks as they arise, near-real-time. 5 Continuous automated reconciliation

Do you have a daily (non-accounting-grade) P&L with driver attribution?

1 P&L only at month-end 2 You can produce P&L periodically, without daily granularity. 3 You can produce a daily P&L and explain its drivers. 4 You anticipate daily P&L swings from forecast positions. 5 Daily P&L with swing attribution & corrective actions

How fast can you answer an audit or regulatory request from integrated data?

1 Weeks of scrambling 2 You can respond, but it takes manual assembly across systems. 3 You answer from an integrated data set with some automation. 4 You anticipate common requests and keep evidence ready. 5 Near-instant from a governed data layer

06 · Data platform foundation IT & Data Platform

Is your core data integrated across systems, or moved between silos by manual exports and spreadsheets?

1 Manual exports between silos 2 You centralize some data, but integration is hand-built and brittle. 3 Core data is integrated through defined, governed pipelines. 4 Integration is automated and near-real-time across systems. 5 Governed, automated integration across systems

Does your analytics run on an elastic cloud platform, or is it constrained by on-prem capacity?

1 On-prem, capacity-bound 2 You've moved some workloads off on-prem, ad hoc. 3 Core analytics runs on a managed cloud platform. 4 The platform scales elastically to demand. 5 Elastic cloud platform, scales on demand

Do you have shared definitions, automated data-quality checks and lineage — or is trust in the data ad hoc?

1 Trust is ad hoc, definitions vary 2 You document some definitions, but checks are manual. 3 You have shared definitions and governed quality checks. 4 Quality and lineage are automated and monitored. 5 Governed definitions, automated quality & lineage

Can business users get governed, near-real-time data themselves, or does every request wait on IT and analysts?

1 Every request waits on IT 2 Users get standard reports, but anything new waits on IT. 3 Users self-serve governed data within defined models. 4 Users get near-real-time self-service across the business. 5 Governed self-service, near-real-time

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