Your Energy Data Lakehouse for greater transparency, efficiency, and revenue.
Unify wind, PV, BESS, trading, market, and weather data on a future-ready platform. Build a robust foundation for reporting, governance, data products, analytics, and AI in the energy industry.
One platformfor SCADA, OEM portals, market, trading, and weather data
End-to-end traceabilityfrom source data through transformations to business KPIs and curated data products
AI-readyfor curtailment, BESS, availability, forecasting, and data products
Reference architecture for an Energy Data Lakehouseanimated data flow from source to application
⚡
Asset, market, and operational dataSCADA, OEM portals, day-ahead, intraday, weather, and trading
Data Sources
OT network
🌬️Wind
☀️PV
🔋BESS
external sources
📈Trading
💹Market
🌦️Weather
IT network
⭳
Landing ZoneIngestion, validation, and arrival of raw data
Ingestion
Batch
Streaming
ETL
Quality checks
🏛️
Energy Data LakehouseRaw data storage, historization, harmonization, and governance
Core Platform
Raw Data & Time Travel
Historization & Audit Trail
Data Quality & Lineage
Curated energy data products
📊
Consumption LayerDashboards, APIs, exports, and domain-specific data views
Power BI / Reporting
APIs / Data Services
Finance & Management
Trading & Operations
🤖
AI & ApplicationsAnalytics Chatbot, EnerCode, BESS Maximizer, and optimization apps
Natural Language Analytics
App generation
Optimization Engines
Decision Intelligence
The Renewable Energy Market Is Changing
From capacity growth to performance growth.
The next competitive advantage will not come from owning more megawatts alone-but from extracting more value from every existing asset, every grid connection, and every flexibility option.
Yesterday
Build more capacity
Growth was primarily measured in installed megawatts and gigawatts.
Acquire and develop new projects
Add wind and PV capacity
Expand portfolios rapidly
Optimize assets mostly in isolation
Today
Growth meets constraints
New capacity remains essential, while grid access, permitting, available sites, and operational complexity increasingly limit expansion.
Grid constraints and curtailment
Negative electricity prices
More vendors and interfaces
Rising regulatory and evidence requirements
Next
Optimize every MWh
Leading producers combine technical, market, weather, grid, maintenance, and BESS data to improve every operational and commercial decision.
Increase availability and yield
Quantify every energy loss
Coordinate wind, PV, and BESS
Automate high-value decisions
Portfolio growth in MW and GW
→
Grid, land, market, and complexity constraints
→
Data-driven performance growth
Renewable energy companies are entering a new phase. Installed capacity remains important-but the decisive advantage increasingly lies in operating every asset, every megawatt, and every flexibility option more intelligently. The Energy Data Lakehouse provides the trusted decision foundation required to unlock the final percentage points of performance.
Current challenges in the energy sector
Your assets produce energy. Your data does not yet produce decisions.
Wind, PV, and BESS generate valuable data every second. In many energy companies, however, this data remains distributed across SCADA systems, OEM portals, BMS platforms, weather services, trading platforms, Excel files, and maintenance systems. Each department sees only part of the reality.
Fragmented viewOperations, trading, management, and data science work with different fragments of the data landscape.
Data gaps become revenue gapsTechnical, grid-related, and market-driven causes are not evaluated together from an economic perspective.
Regulation becomes reconstructionWithout historization, lineage, and controlled data products, audits and evidence processes remain manual.
🧩
Many systems. No shared truth.
SCADA, BMS, OEM portals, market systems, and maintenance systems deliver contradictory fragments. KPIs are calculated multiple times and according to different rules.
⏱️
Decisions arrive too late.
Management reports arrive after economically relevant events have already passed. Trading and operations continue to reconcile data manually.
📉
Losses remain without a clear cause.
Curtailment, outages, grid limits, sensor errors, and market decisions are viewed in isolation. Lost MWh cannot be explained unambiguously.
🔋
Flexibility remains invisible.
Trading sees market opportunities, but not always the BESS's truly available flexibility corridor, including SoC, limits, reservations, and degradation.
🛡️
Compliance becomes a manual risk.
REMIT, unbundling, NIS2, and audits require evidence of what information was available when, who was allowed to access it, and how decisions were made.
🤖
Otherwise, AI simply automates data problems.
Without asset IDs, quality flags, historical context, and approved data products, answers may sound convincing but remain unreliable.
An Energy Data Lakehouse ends the blind flight.
It connects wind, PV, BESS, grid, weather, maintenance, and market data in a historized and traceable data foundation. Raw data becomes quality-assured data products, conflicting KPIs become a shared truth, and reactive decisions become proactive control.
What a modern Energy Data Lakehouse must deliver
An energy platform must do more than store data. It must consolidate sources, assure quality, make lineage traceable, and turn data into curated products, analytics, and productive applications.
🧭
Data transparency
From raw data storage through every transformation step to the KPI, it remains clear where a value came from and how it was calculated.
🧩
Cross-domain integration
Connect asset data, trading information, market prices, weather, maintenance, and finance in one shared data logic.
🚀
A faster path to applications
Use curated data products as the starting point for dashboards, chatbots, one-pagers, BESS optimization, and custom business applications.
Connect, Don’t Replace
Your existing specialist systems remain valuable.
The Energy Data Lakehouse does not replace SCADA, CMMS, ETRM, OEM portals, or BI tools. It connects them through a shared, historized, governed, and reusable data foundation.
SCADA remains the operational source for asset telemetry and control-related information.
CMMS remains the system for maintenance processes, work orders, and service history.
ETRM and trading platforms remain the systems for market execution and commercial workflows.
Power BI, IBM Cognos, and specialist applications remain consumption and decision tools.
The Data Lakehouse creates the common context, quality controls, history, and traceability across all of them.
Energy Data LakehouseShared, governed data foundation
SCADA
CMMS
ETRM
OEM Portals
Power BI / Cognos
Cloud Analytics
Consumption Layer
Governed access to trusted data-for every user, application, and external service.
The Consumption Layer controls who may consume which data and through which channel. It turns governed Data Lakehouse content into secure, high-performance services for internal teams, BI platforms, cloud analytics, partners, and operational applications.
01
Permission-aware data access
Role-based and policy-based permissions define which users, departments, applications, or external parties may access specific data products, domains, and fields.
02
Secure external consumption
Approved cloud analytics services can consume only the data they require-without receiving unrestricted access to the underlying Data Lakehouse.
03
BI and enterprise integration
Power BI, IBM Cognos, and other analytics applications consume curated and quality-assured data products instead of fragmented raw data extracts.
04
High-performance REST APIs
An API Gateway exposes governed data services through performant REST APIs with authentication, authorization, throttling, monitoring, and complete auditability.
Trusted sourceEnergy Data LakehouseCurated data products · lineage · quality status
↗Partner ApplicationsOperational and domain-specific services
One governed access layer-many secure consumption channels.Data remains centrally controlled while every consumer receives only the information required for its role and business purpose.
Use Cases
The right information. For the right entity. At the right time.
Curated data products make it possible to provide data from the grid, generation, operations, and trading in a targeted, secure, and compliant way. Instead of unstructured raw data, business teams receive approved, quality-assured, role-based views tailored precisely to their use case.
🛡️
Grid ↔ Generation
Approved information on congestion, feed-in limits, and grid restrictions
The generation company receives only the grid-relevant information that has been approved operationally and from a regulatory perspective. This creates an actionable view of restrictions and feed-in limits without unnecessary raw-data complexity.
The result is a reliable data product for faster coordination, better planning, and clearly documented collaboration between grid and generation.
GridCongestion · Feed-in limits · Restrictions
🧊
Curated Data Productapproved · quality-assured · role-based
🛡️
Regulatory confidence Only approved information is distributed in a controlled way, aligned with governance, compliance, and clear responsibilities.
🗄️
Management-ready data view Instead of data chaos, business teams receive a reliable product with clear business meaning and high usability.
👁️
Traceable and reliable Definitions, origin, and approval logic remain transparent-ideal for operational decisions and internal alignment.
⚡
Operations ↔ Trading
Controlled provision of curtailment, availability, and BESS-relevant data
Trading needs economically relevant information, not arbitrary raw data. Curated data products provide curtailment, availability, and BESS-related information specifically for commercialization decisions.
This creates a controlled, compliant data flow between operations and trading that strengthens both operational relevance and economic decision-making.
OperationsCurtailment · Availability · BESS data
Tradingprovided in a controlled way for commercialization
➡️
Controlled Information Flow Economically relevant data is provided securely and deliberately between entities-not distributed without control.
⚡
Faster decision-making Trading and operations work from aligned, reliable data views and can assess economic measures more quickly.
🧭
Audit-ready documentation Data origin, business definitions, and approval logic remain technically traceable and documented in a management-ready format.
How Data Becomes Business Value
From trusted data to measurable financial impact.
A Data Lakehouse is not the business outcome. It is the decision infrastructure that connects data quality, insight, action, and measurable operational value.
Faster InsightCurated products and governed analytics
Better DecisionTechnical and economic context together
Automated ActionWorkflows, applications, and AI agents
Business ImpactYield, margin, risk, and productivity
Higher availability and yieldIdentify performance losses earlier and prioritize corrective action by economic relevance.
Lower curtailment lossesEvaluate whether curtailed energy could be shifted, stored, or marketed differently.
Reduced imbalance costsCombine forecast, availability, weather, and incident data to understand deviations.
Improved BESS marginOptimize charging, discharging, reservations, and degradation as one decision problem.
Smarter maintenancePrioritize interventions by expected energy and revenue impact, not alarm volume alone.
Faster reporting and auditsReplace manual reconciliation with traceable KPIs, evidence chains, and automated reports.
Illustrative value calculator
Adjust the assumptions to explore the scale of value that better availability, curtailment recovery, and process automation could unlock.
Illustrative scenario only. The result is not a financial forecast and excludes implementation cost, taxes, market volatility, BESS degradation, availability of capacity, and other project-specific constraints.
Illustrative annual value potential€0Combined potential from performance uplift, recoverable curtailed energy, and process savings.
Performance uplift€0
Curtailment recovery€0
Process savings€0
One Platform. Different Decisions.
A shared data foundation creates role-specific value.
Every stakeholder receives a governed view tailored to their responsibilities-without creating separate and contradictory versions of reality.
◈
CEO & Management
Portfolio-wide transparency, revenue at risk, investment priorities, and performance benchmarking.
Decision focus: value and risk
⚙
Head of Operations
Loss causes, availability, recurring incidents, and interventions prioritized by expected impact.
Decision focus: operational performance
↗
Trading
Approved availability, forecast corridors, curtailment context, and genuinely available BESS flexibility.
Decision focus: commercial optimization
€
Controlling & Finance
Automated reconciliation, traceable KPIs, settlements, evidence, and faster month-end processes.
Decision focus: financial truth
🛡
CISO & Compliance
Controlled access, audit trails, data sovereignty, policy enforcement, and documented information flows.
Decision focus: security and compliance
Your use cases in the Energy Data Lakehouse
From a shared data foundation to measurable operational and financial impact.
Explore 34 concrete use cases for super-hybrid parks, operations, trading, BESS, finance, compliance, asset performance, and AI-powered automation.
Scroll horizontally or use the arrows. “Learn more” opens the full description.
📉
Use Case 01
Energy Loss Ledger
Lost or non-injected energy is assigned across systems to a clear cause and financial impact.
Challenge
In super-hybrid parks, it is often unclear why energy was not generated or injected into the grid. Technical outages, grid restrictions, negative market prices, curtailment, and data errors are documented in different systems.
The Data Lakehouse solution
The Energy Data Lakehouse time-aligns operational, grid, weather, market, and BESS data and assigns every lost MWh to a clear cause.
Business value
A single source of truth for all energy losses
Clear separation of technical, grid-related, and market-driven causes
Financial valuation of lost or shifted energy
A reliable foundation for management and optimization
🧾
Use Case 02
Curtailment Truth Record
Every curtailment event is fully documented with trigger, setpoint, actual output, market price, restriction, and BESS status.
Challenge
After a curtailment event, it is often unclear who triggered it, which setpoint applied, and whether the BESS could have absorbed the excess energy.
The Data Lakehouse solution
All curtailment events are connected with setpoint, actual output, trigger, market price, weather, grid restriction, and BESS status to form one complete record.
Business value
Complete traceability for every curtailment event
Differentiate avoidable from unavoidable losses
Automatically calculate economic impact
Identify optimization opportunities for BESS and trading
📡
Use Case 03
Trading-safe Availability
Trading receives an approved, quality-assured view of technically available output and forecast corridors.
Challenge
Trading needs current information on available output. Unverified technical raw data can be misinterpreted or may be regulatory-sensitive.
The Data Lakehouse solution
The Data Lakehouse creates an approved data product with technically available output, forecast corridor, quality status, and validity period.
Business value
Controlled Information Flow zum Trading
Reliable capacity forecasts instead of raw data
Faster commercialization decisions
Clear separation between operational and trading views
🛡️
Use Case 04
REMIT Event Workflow
Market-relevant events are identified, assessed, documented, and only released to trading after approval.
Challenge
Technical outages or output restrictions may constitute market-sensitive information. Detection, assessment, publication, and approval are often handled manually.
The Data Lakehouse solution
An automated workflow detects relevant events, calculates the affected capacity, and creates a complete information and decision chain.
Business value
Identify relevant events early
Automatically prepare notification drafts
Document publication and updates
Release information only after approval
🔐
Use Case 05
Unbundling-compliant grid data product
Only required and approved grid restrictions are shared with generation and trading on a role-based basis.
Challenge
Generation and trading need information on feed-in limits and grid restrictions. At the same time, sensitive or unnecessary grid data must not be shared without control.
The Data Lakehouse solution
The Data Lakehouse provides a curated grid-constraint data product with approved feed-in limits, time windows, ramps, and restriction status.
Business value
Share only required grid information
Role-based, traceable access
Better generation planning
Clear separation of organizational responsibilities
🔋
Use Case 06
BESS Maximizer
Market prices, generation, grid restrictions, SoC, SoH, and degradation are combined into one optimization foundation.
Challenge
A battery storage system can support arbitrage, curtailment absorption, balancing energy, and grid services at the same time. Most systems, however, assess these objectives separately.
The Data Lakehouse solution
The Data Lakehouse combines market prices, weather forecasts, generation, SoC, SoH, grid restrictions, and degradation costs in one optimization foundation.
Business value
Optimal charging and discharging windows
Reduce curtailment losses
Capture market opportunities economically
Include degradation in decision-making
⚡
Use Case 07
BESS Flexibility Product
Trading receives the truly available flexibility corridor instead of only the current state of charge.
Challenge
Trading often sees only the current state of charge. That alone does not show which power and energy volumes are actually available.
The Data Lakehouse solution
A curated BESS data product calculates the real flexibility corridor from SoC, SoH, temperature, BMS limits, grid restrictions, and reservations.
Business value
Actually available power instead of theoretical capacity
Avoid technically infeasible schedules
Use storage flexibility more effectively
A consistent view for operations and trading
🧮
Use Case 08
BESS Degradation Cost Engine
Each schedule is supplemented with the actual aging and warranty costs of the storage system.
Challenge
A trading transaction may look profitable in the short term while causing substantial aging and warranty costs over time.
The Data Lakehouse solution
The Data Lakehouse evaluates every charge and discharge cycle using C-rate, temperature, SoC window, cycles, and expected lifetime.
Business value
Calculate actual net revenue
Include degradation costs in each schedule
Protect lifetime and warranty
Choose the economically optimal operating strategy
🔌
Use Case 09
Hybrid Park Grid Connection Optimizer
Wind, PV, and BESS are jointly optimized against connection limits, market prices, and storage potential.
Challenge
Wind, PV, and BESS share the same grid connection point. High simultaneous generation can create connection bottlenecks and avoidable curtailment.
The Data Lakehouse solution
The Data Lakehouse combines available generation, connection limits, BESS capacity, market prices, and forecasts for joint park optimization.
Business value
Optimize grid-connection utilization
Store surplus energy deliberately
Reduce curtailment
Control wind, PV, and BESS as one system
🕒
Use Case 10
Canonical Energy Timeline
All data sources are harmonized on a common timeline with quality and lineage information.
Challenge
SCADA, BMS, weather, and market data use different time resolutions, time zones, and aggregation logic.
The Data Lakehouse solution
All data is harmonized on a common timeline and enriched with quality and lineage information.
Business value
Comparable time series across all systems
Handle daylight-saving and standard time correctly
Detect shifted or missing intervals
A reliable foundation for analytics and AI
🕸️
Use Case 11
Energy Asset Graph
A central asset model connects all physical and commercial entities across systems.
Challenge
Assets and measurements are named and structured differently in SCADA, OEM portals, CMMS, and trading systems.
The Data Lakehouse solution
A central asset model connects park, turbine, inverter, string, battery rack, grid connection point, and commercialization unit.
Business value
Consistent asset IDs
Clear assignment of all measurements
Cross-system analytics
Faster integration of new assets
✅
Use Case 12
Data Quality Command Center
Data quality is continuously measured, visualized, and improved through traceable correction processes.
Challenge
Data gaps, frozen sensors, time shifts, and incorrect units are often discovered only after reports or models produce incorrect results.
The Data Lakehouse solution
The Data Lakehouse continuously monitors completeness, plausibility, freshness, and consistency across all data sources.
Business value
Automatically detect errors
Make data quality transparent
Document corrections traceably
A reliable basis for reporting and AI
📶
Use Case 13
Data gap or genuine outage?
Redundant signals and communication status distinguish telemetry failures from technical unavailability.
Challenge
Missing telemetry is often incorrectly classified as technical unavailability.
The Data Lakehouse solution
The Data Lakehouse compares redundant signals, communication status, output, and asset state to classify the true cause.
Business value
Differentiate communication failures from outages
Calculate availability KPIs correctly
Avoid incorrect claims
Resolve data issues faster
🏭
Use Case 14
KPI Factory with Data Lineage
KPI definitions are centrally versioned, standardized, and traceable down to the raw record.
Challenge
KPIs such as technical availability, energy availability, CUF, or performance ratio are calculated differently across departments.
The Data Lakehouse solution
KPI definitions, formulas, filters, and data sources are centrally versioned and traceable down to the raw record.
Business value
Standardized KPI definitions
Full traceability for every KPI
Fewer disputes about numbers
Faster audits and approvals
🌦️
Use Case 15
Unified Forecast Hub
Every forecast version is historized and can be compared with the actual outcome that followed.
Challenge
Weather, generation, price, and curtailment forecasts are stored in different systems. Often only the latest version is retained.
The Data Lakehouse solution
The Data Lakehouse historizes every forecast with creation time, model version, validity period, and the actual value observed later.
Business value
Know which forecast was available at the time of the decision
Compare models objectively
Trace incorrect decisions
Forecast Accuracy verbessern
🏆
Use Case 16
Forecast Champion/Challenger
Multiple models compete automatically; the best model is selected by park, weather situation, and horizon.
Challenge
A forecasting model does not perform equally well for every park, season, and forecast horizon.
The Data Lakehouse solution
Multiple models are automatically compared against historical actuals. The best model is selected depending on park, weather situation, and horizon.
Business value
Higher forecast quality
Lower imbalance costs
Automated model evaluation
Transparent model selection
⚖️
Use Case 17
Imbalance Root Cause Analytics
Schedule deviations are analyzed together with weather, outages, curtailment, BESS behavior, and grid restrictions.
Challenge
Deviations between schedule and actual feed-in create costs. The cause often remains unclear.
The Data Lakehouse solution
Schedule, generation, weather, outages, curtailment, BESS behavior, and grid restrictions are analyzed together.
Business value
Identify causes of imbalance deviations
Reduce imbalance energy costs
Distinguish forecast errors from process errors
Make responsibilities transparent
💶
Use Case 18
Revenue-at-Risk Cockpit
Technical risks and market prices are translated into a forward-looking revenue assessment.
Challenge
Management and operations often only learn afterward which revenues were at risk due to outages, curtailment, or BESS limitations.
The Data Lakehouse solution
The Data Lakehouse combines technical risks, market prices, forecasts, and available output in a forward-looking revenue assessment.
Business value
Identify revenue at risk early
Prioritize actions economically
Translate technical risks into euros
Accelerate management decisions
🛠️
Use Case 19
Predictive Maintenance by Economic Risk
Maintenance actions are prioritized not only technically, but also by expected financial risk.
Challenge
Traditional predictive-maintenance systems prioritize technical anomalies but often ignore their economic significance.
The Data Lakehouse solution
Failure probability, expected damage, market price, lost generation, and maintenance windows are evaluated together.
Business value
Prioritize maintenance by financial relevance
Avoid unnecessary service visits
Prevent outages with high revenue risk
Allocate resources more effectively
🔎
Use Case 20
Cross-Asset Root Cause Analysis
AI identifies shared root causes across wind, PV, BESS, and grid infrastructure.
Challenge
A shared fault at the grid connection point or power plant controller can trigger numerous alarms across wind, PV, and BESS at the same time.
The Data Lakehouse solution
AI analyzes temporal relationships across all assets and identifies shared primary causes instead of isolated follow-on alarms.
Business value
Locate root causes faster
Avoid parallel misdiagnoses
Reduce downtime
Identify shared infrastructure issues
🚨
Use Case 21
Alarm Flood Reduction
Thousands of individual alarms are grouped, prioritized, and condensed into actionable incidents.
Challenge
During disturbances, operators may face hundreds or thousands of individual alarms.
The Data Lakehouse solution
Alarms are grouped by time and business context, follow-on alarms are filtered out, and a central incident is created.
Business value
Significantly reduce alarm floods
Highlight the primary cause
Show economic impact
Deliver recommendations faster
📋
Use Case 22
Automated Work Order Creation
An AI workflow prepares a complete maintenance work order from a detected anomaly.
Challenge
Between detecting an anomaly and creating a maintenance order, there are often manual analyses and media breaks.
The Data Lakehouse solution
An AI workflow validates data quality, identifies the asset and component, assesses the risk, and creates a prepared work order.
Business value
Respond faster to disruptions
Reduce data-entry effort
Standardized fault description
Recommend spare parts and required skills
☀️
Use Case 23
PV soiling and degradation detection
Irradiance, temperature, string data, weather, and thermography distinguish PV loss causes.
Challenge
Performance losses caused by soiling, shading, degradation, or string faults often look similar in total output.
The Data Lakehouse solution
Irradiance, module temperature, string data, precipitation, cleaning, and thermography are analyzed together.
Business value
Clearly distinguish loss causes
Plan cleaning based on actual need
Detect string and module faults earlier
Sustainably increase PV yield
🌬️
Use Case 24
Wind Power Curve Analytics
Power curves are compared with weather, operating state, and peer turbines to detect underperformance early.
Challenge
Deviations from the expected power curve may be caused by yaw errors, pitch issues, icing, wake effects, or faulty sensors.
The Data Lakehouse solution
Historical power, wind, temperature, and operational data are analyzed against reference curves and comparable assets.
Business value
Detect performance losses early
Narrow down fault causes
Compare turbines
Unlock additional revenue
📑
Use Case 25
Warranty and SLA validation
Contract clauses are linked with actual asset events, response times, and availability.
Challenge
Contract terms are stored in PDFs, while operational, alarm, and maintenance data is held in separate systems.
The Data Lakehouse solution
Contract clauses are connected with actual asset events, availability, response times, and service activities.
Business value
Automatically identify warranty claims
Calculate contractual availability
Compile complete evidence packages
Monitor deadlines and exclusion criteria
🧾
Use Case 26
Finance-grade Energy Reconciliation
SCADA, meter, trading, balancing-group, and billing data are reconciled automatically.
Challenge
SCADA, meter, trading, balancing-group, and billing data often do not fully match.
The Data Lakehouse solution
All energy volumes and prices are reconciled automatically. Differences receive a cause, amount, and processing status.
Business value
Detect billing errors faster
Make financial discrepancies transparent
Accelerate month-end closing
Provide reliable finance-grade data
🤝
Use Case 27
Automated PPA Settlement
Contract logic, measurement data, market prices, and schedules are converted into an automated settlement process.
Challenge
PPA settlements contain complex rules for delivered volumes, market prices, shortfalls, curtailment, and price thresholds.
The Data Lakehouse solution
The Data Lakehouse connects contract logic, measurement data, market prices, and schedules in an automated settlement process.
Business value
Automate billing
Apply contract rules consistently
Reduce disputes
Document deviations traceably
🔒
Use Case 28
Cyber Incident Evidence Chain
Security, communication, and operational data form an immutable incident timeline.
Challenge
In OT or IT security incidents, technical impact, sequence of events, and affected systems must be reconstructed quickly.
The Data Lakehouse solution
Security, communication, and operational data are historized immutably and combined into a complete incident timeline.
Business value
Investigate incidents faster
Secure a traceable evidence chain
Quantify operational impact
Support audits and regulatory notifications
💬
Use Case 29
Energy Operations Copilot
An Analytics Chatbot answers operational questions using curated, quality-assured data products.
Challenge
Business users currently have to gather information manually from SCADA, weather, market, CMMS, and BESS systems.
The Data Lakehouse solution
An Analytics Chatbot answers natural-language questions based on curated, quality-assured data products.
Business value
Accelerated root-cause analysis using pre-integrated, governed data
Direct access to KPIs
Answers with data lineage and context
Less manual analysis effort
🌅
Use Case 30
Morning Operations Briefing
An AI agent automatically creates a daily operational picture of outages, risks, curtailments, and revenue at risk.
Challenge
At the start of a shift, operators must compile disruptions, market conditions, BESS status, and open actions from multiple systems.
The Data Lakehouse solution
An AI agent automatically creates a daily briefing with outages, risks, data issues, curtailments, and revenue at risk.
Business value
Unified operational picture at the start of the day
Identify critical issues immediately
Set clear priorities
Accelerate shift preparation
🔄
Use Case 31
Automated shift handover
Alarms, tickets, schedule changes, and open actions are handed over to the next shift in a structured format.
Challenge
Important information is lost during shift changes or passed on in unstructured emails and logbooks.
The Data Lakehouse solution
An agent summarizes alarms, tickets, schedule changes, active limits, and open actions in a structured handover.
Business value
Never lose relevant information
A standardized handover format
Highlight open risks
Less coordination effort
🧠
Use Case 32
Autonomous Data Quality Agent
An AI agent detects data issues, evaluates substitute sources, and recommends traceable corrections.
Challenge
Data issues often have to be searched for, assessed, and corrected manually.
The Data Lakehouse solution
An AI agent detects data gaps, compares substitute sources, recommends corrections, and documents the method and confidence score.
Business value
Automatically process data issues
Keep corrections traceable
Preserve raw data unchanged
Continuously improve data quality
🧪
Use Case 33
Automated Incident Investigation
After an outage, a timeline is generated automatically from alarms, measurements, grid events, and market data.
Challenge
After a park shutdown, reconstructing the cause, energy loss, and financial impact often takes several days.
The Data Lakehouse solution
The Data Lakehouse automatically creates an incident timeline from alarms, measurements, grid events, market prices, and maintenance information.
Business value
Accelerate root-cause analysis
Calculate lost energy and revenue
Assess regulatory relevance
Derive actions and claims
🧑💻
Use Case 34
AI App Builder
A coding assistant quickly creates new dashboards and business applications from approved data products.
Challenge
New dashboards and business applications often require lengthy development and coordination cycles.
The Data Lakehouse solution
A coding assistant quickly creates new interfaces, dashboards, and operational apps based on approved data products.
Business value
Shorter time to value
Business applications from text prompts
Less custom BI development
Pilot new use cases quickly
Data Lineage
Traceability down to the individual data record.
With Data Lineage, you see not only the final KPI but every step on the path: from the curated KPI through harmonization and raw data back to the source.
Every KPI can be traced back to its underlying raw data.
Transformations, filters, and calculation rules remain transparently documented.
Business teams gain confidence in the numbers because cause and origin are visible.
Audits and discussions with trading, grid, or operations become faster and more robust.
Beispiel: CUF – Capability Utilization Factorvertical trace-back journey of the data product
📘
Curated KPI – CUFcurrent value: 87,4 %
provided for reporting, steering, and analysis
🛠️
HarmonizationUnits, time synchronization, and quality rules
Gap correction, curtailment-code assignment, and quality flagging
🗄️
Raw Zone1-second and 5-minute telemetry historized
unchanged time series with timestamp and asset ID
🌬️
SourceHohenwerfen Wind Farm · SCADA / OEM
Signals: Actual Power, Availability, Curtailment Status
Trace-back view
CUF = actual energy output / theoretically available energy Data-record path: Curated KPI "CUF" → availability logic → curtailment flagging → time synchronization → raw telemetry → Hohenwerfen wind farm / SCADA
Analytics
Ask questions as you would ask an analyst-and receive answers from curated data products.
The Analytics Chatbot connects natural-language business questions with the curated data products in your Energy Data Lakehouse. Business teams can immediately discuss causes, impacts, and options, including visualizations.
Understands operational questions about curtailment, availability, yield, portfolio, and markets.
Accesses curated, quality-assured data products and calculations.
Can explain, contextualize, and present quantitative statements directly.
Creates charts, one-pagers, and management-ready visualizations on request.
AnalyticsNatural-language analytics on your Energy Data Lakehouse
U
The chatbot is calculating
AI
U
The chatbot is creating the chart
AI
Active power
Wind speed
Curtailment 11:45–16:25
LLM required. In Cloud or onPrem with dedicated GPU Hardware
From Data Silo to Decision
From data silo to a decision in seconds.
When trusted data products are pre-integrated, questions that previously required hours of manual consolidation can be answered in seconds.
Without a Data Lakehouse
A simple management question becomes a manual project.
“Show me all curtailments from the last 30 days-including weather data, electricity prices, technical availability, BESS potential, and foregone revenue.”
SCADA→WIS→Trading System→Weather Data→BESS→Excel→CSV Exports→manual consolidation→multiple business departments→Power BI
OperationsExports SCADA and WIS data and validates timestamps.
TradingAdds market prices, dispatch context, and commercial assumptions.
Data & BIReconciles CSV files, formulas, gaps, and different data versions.
ManagementWaits for a consolidated Power BI result before deciding.
⏱ Result after several hours
Data must be consolidated from multiple systems.
Different data versions create uncertainty.
High manual effort and numerous handoffs.
Decisions are often made too late.
With an Energy Data Lakehouse
The same question is answered automatically.
“Show me all curtailments from the last 30 days-including weather data, electricity prices, technical availability, BESS potential, and foregone revenue.”
Sparkharmonizes data calculates KPIs creates curated data products
→
Icebergorganizes data versioning metadata
→
Prestoparallel SQL queries connects multiple data sources results in seconds
Curtailment timeline
Weather
Market Price
Technical availability
Foregone revenue
BESS recommendation
⚡ Decision-ready result in seconds with pre-integrated data products
One central data source for all business teams.
Fully automated data preparation.
Curated data products instead of manual Excel analyses.
Decisions based on current, quality-assured data.
An Energy Data Lakehouse turns billions of measurements from wind, PV, and BESS assets into decision-ready data products-automatically, traceably, and in seconds instead of hours.
Technical note: Apache Spark prepares data and creates curated data products. Apache Iceberg organizes and versions it efficiently. Apache Presto enables high-performance queries across billions of records and even multiple data sources at the same time.
EnerCode – Coding Assistant
EnerCode-your coding assistant for rapidly building interactive applications.
EnerCode creates user-friendly interfaces for querying curated data products in a statistical context and presenting them visually. This turns data into usable business applications quickly.
Creates interfaces for maps, dashboards, tables, and interactions from text prompts.
Dramatically shortens the path from idea to usable business application.
Enables business users to work with curated data products without complex BI development.
Ideal for the BESS Maximizer, Curtailment Explorer, park maps, and management cockpits.
EnerCode can be operated in the cloud or on-premises with certain hardware requirements (GPU).
EnerCode – App BuilderGenerates UI components and clickable business applications
U
EnerCode is generating the application
EC
Self-assessment
Calculate your Relevance Index.
Answer seven questions. The index indicates how relevant a centralized Energy Data Lakehouse is for your energy company.
Implementation Roadmap
Start small, prove value early, then scale across the organization.
An ascending delivery path that creates a secure foundation, demonstrates measurable value early, and evolves into an enterprise-wide data and AI platform.
01
↳
Step 1 · Foundation
Connect & Ingest Data
Establish the Data Lakehouse foundation by connecting core data sources, integrating systems, and implementing secure data pipelines.
Priority data sources connected
Canonical asset model established
Secure landing zone and pipelines
Baseline data-quality monitoring
02
◆
Step 2 · MVP
Deliver First Business Value
Build the first analytical use cases and dashboards to generate measurable business value quickly and demonstrate the platform's potential.
One productive data product
One management dashboard
One automated report
Measurable business baseline
03
AI
Step 3 · Intelligence
Enable AI Capabilities
Extend the platform with AI workloads-leveraging GPU infrastructure for on-premises deployments or Agentic AI services in cloud environments.
One governed AI or optimization use case
Copilot or agentic workflow
Confidence, quality, and approval controls
GPU or cloud-AI execution pattern
04
↗
Step 4 · Scale
Expand Use Cases & Adoption
Onboard additional departments, subsidiaries, and data domains while introducing new analytics and AI-driven business use cases.
Repeatable source-onboarding pattern
Data-product catalogue
Additional teams and subsidiaries
New analytics and automation use cases
05
∞
Step 5 · Enterprise Growth
Continuous Innovation
Evolve the Data Lakehouse into an enterprise-wide data and AI platform through ongoing optimization, governance, automation, and long-term scalability.
Enterprise governance model
Continuous platform optimization
Automation and lifecycle management
Long-term scalability and adoption
Start small.Prove value early.Scale with confidence.
Operating the Data Lakehouse
Three operating models-depending on strategy, sovereignty, and scalability requirements.
An Energy Data Lakehouse can be operated in different ways: as a cloud service with watsonx.data, in your own cloud environment, or on-premises on Fusion HCI. Each model has clear strengths-the key is choosing the operating model that fits your IT, data, and transformation strategy.
☁️
watsonx.data in the cloudFast start with a managed service
Ideal for organizations that want to quickly establish a first productive use case, curated data products, or analytics capabilities.
🏢
Private cloudGreater control with high flexibility
Suitable for organizations that want cloud benefits while retaining more control over security, architecture, and governance.
🖥️
Fusion HCI on-premisesEnterprise-scale platform for data, applications, and AI
For maximum data sovereignty, a flexible platform strategy, OpenShift applications, and GPU-accelerated AI training on scalable infrastructure.
Comparison criterionwhich operating model fits your strategy?
watsonx.data Cloudmanaged service · rapid production readiness
Private clouddedicated · flexible · greater control
Cloud-based Data Lakehouse with low initial infrastructure effort and rapid onboarding.
Operation in your own cloud environment with greater influence over architecture and security.
On-premises on Fusion HCI-a flexibly scalable, GPU-capable enterprise platform for the Data Lakehouse, OpenShift applications, and AI.
Time to valueHow quickly first use cases can go live
very fastlow initial complexity
Particularly suitable for pilots, initial data products, and rapid implementation.
mediumdepends on the cloud setup
A strong compromise between speed and individual design freedom.
mediumlonger planning phase
Requires more preparation, but creates a strong long-term platform foundation.
Data sovereigntyControl over data storage and the physical environment
lower
Less physical control, but high operational convenience.
high
Greater influence over infrastructure, security, and operating boundaries.
maximum
Full control over data, platform, network, and integration rules.
ScalabilityExtensibility for data volumes and workloads
Highly scalable without procuring your own hardware.
Flexibly scalable, depending strongly on the selected cloud operating model.
Flexibly scalable within the Fusion HCI strategy-not only for the Lakehouse, but also for additional platform workloads.
Application hostingUse beyond the Data Lakehouse
Primarily focused on Data-Lakehouse-related services and data processing.
Broader use is possible, depending on cloud architecture and operational approvals.
OpenShift-enabled
OpenShift can also run applications, services, and platform components-so this is not a Data-Lakehouse-only environment.
AI and GPU capabilityTraining and running compute-intensive models
Well suited for AI-related use; GPU availability depends on the cloud setup.
Also feasible depending on the architecture, but more dependent on the individual environment.
GPU-readylocal AI training
GPU hardware can be used directly to train and run AI models on the platform.
Typical advantagesWhat management and business teams notice immediately
Fast start, low entry barrier, short time to value.
Greater design freedom while retaining high cloud flexibility.
Maximum sovereignty, a platform strategy for additional applications, and strong future readiness for AI and energy use cases.
Typical disadvantagesWhat needs to be considered
Less physical data sovereignty and dependency on the cloud framework.
Greater operational and architectural responsibility than with a fully managed service.
Higher investment and planning effort, plus the need for sound platform governance and capacity management.
Recommendation: If data sovereignty, OpenShift-based application operation, and GPU-enabled AI workloads are strategically relevant, Fusion HCI provides the strongest long-term platform foundation.
Data Governance Operating Model
Technology creates the platform. Governance creates trust.
Clear ownership is required for KPI definitions, data quality, access decisions, and the lifecycle of every data product.
ACCOUNTABILITY
Data Owner
Owns the data domain, approves access principles, and determines how information may be shared and used.
PRODUCT
Data Product Owner
Owns definition, quality, freshness, users, documentation, and the roadmap of a curated data product.
PLATFORM
Platform Team
Operates architecture, pipelines, security controls, observability, reliability, and the technical product lifecycle.
CONSUMPTION
Data Consumer
Uses approved data for reporting, trading, analytics, AI, applications, or external services within defined policies.
⇄
Example: Operations owns the technical-availability data product. Trading consumes an approved commercial view. The platform records which version was accessed, by whom, and when.
Reference Delivery Pattern
From fragmented renewable-energy data to a governed platform.
An anonymized implementation blueprint shows how Beyond Tech structures the journey from first connection to scalable business value-without requiring a disruptive replacement of the existing system landscape.
Representative energy-sector implementation
Wind, PV, and BESS data are connected with operational, weather, market, maintenance, and asset-master information. The first MVP establishes one trusted data product and a management-ready analytical view.
DiscoverPrioritize business value and data domains
ConnectIntegrate operational and commercial sources
ProveDeliver one productive data product
ScaleExpand users, domains, and AI workflows
What the first delivery should prove
One traceable KPIFrom management figure back to transformation and source record.
One automated reportReplacing repetitive consolidation and manual reconciliation.
One curated data productOwned, documented, quality-assured, and reusable across applications.
One measurable baselineA quantified starting point for yield, loss, process effort, or revenue at risk.
This is an illustrative delivery pattern, not a claim about a named customer or guaranteed project result. Scope and outcomes depend on the selected data sources and business use case.
Why Beyond Tech?
Your trusted implementation partner for the Energy Data Lakehouse.
The Team
Beyond Tech.Energy Industry. Delivery.
A Data Lakehouse delivers its full value only when technology, energy-industry expertise, and business processes work together seamlessly.
◆ IBM Silver Partner🛡 ISO 27001✓ ISO 9001
Beyond Tech supports energy companies from the first data strategy through productive implementation-with many years of genuine experience in the renewable-energy sector and a deep understanding of wind, PV, BESS, and hybrid-park operations.
Our interdisciplinary team of data architects, software engineers, energy experts, and project managers develops a tailored Energy Data Lakehouse aligned with your existing systems, data sources, and business processes.
Instead of a standard product, you receive a platform that addresses today's challenges and establishes the foundation for future AI, analytics, and automation applications.
As an IBM Silver Partner, we combine IBM watsonx.data, OpenShift, and IBM Fusion HCI with proven delivery experience in the energy industry.
Matthias Gerstl
Project Implementation Manager
Watson and Maximo Application Suite certified, with a focus on structured delivery and enterprise implementation.
Sophia Weis
Project Implementation Manager
Maximo Renewables and Maximo Application Suite certified, connecting project governance with energy-sector execution.
Herbert Steinmetz
Renewables Solution Architect
IREB CPRE Requirements Engineering and ITIL certified, translating business needs into scalable solution architectures.
Leon Vizi
Renewables Support Specialist
IREB CPRE Requirements Engineering and ISTQB certified, supporting reliable applications and high-quality operations.
Energy Industry ExpertiseWind, PV, BESS, hybrid parks, operations, and trading from one shared perspective.
End-to-End DeliveryFrom data strategy, architecture, and governance to the productive platform and applications.
Enterprise Technologywatsonx.data, OpenShift, and Fusion HCI as a scalable foundation for data, applications, and AI.
Long-term PartnershipSecure, quality-focused, and designed for sustainable business value.
TwoPager Access
Receive the TwoPager by booking a demo
Register for a personalized demo of your Energy Data Lakehouse and receive exclusive access to our TwoPager and a potential target-architecture roadmap afterward.
📅
Live demo with experts Experience typical target architectures, Data Lineage, and applications in practice.
📄
Exclusive TwoPager Receive structured insights, implementation models, and concrete examples.
🛡️
No obligation The demo is free, personal, and tailored to your current situation.
TwoPager PreviewFront page and Data User Journey
TwoPager Preview
Secure your demo
Secure your demo and receive the TwoPager
Complete the form to schedule your personalized demo. After confirmation, you will receive the TwoPager and appointment details.
Frequently Asked Questions
Answers to the questions that matter before an enterprise rollout.
Do we need to replace SCADA, CMMS, ETRM, or existing BI tools?
No. The Energy Data Lakehouse connects and governs the existing landscape. Specialist systems remain in place while the Lakehouse creates shared history, quality, context, and reusable data products.
Can the platform run fully on-premises?
Yes. It can be deployed on-premises, in a private cloud, or as a cloud service. The operating model is selected according to sovereignty, latency, security, scalability, and internal operating capabilities.
Who owns the data and the developed data products?
Ownership is defined contractually and organizationally. A governance model should identify data owners, data-product owners, platform responsibilities, and approved consumers before productive rollout.
How are OT and IT environments separated?
Data ingestion can be designed through segmented networks, controlled gateways, read-only connections, secure landing zones, and policy-based interfaces. The exact architecture must be aligned with the customer's security and OT requirements.
How does the platform support REMIT, unbundling, NIS2, and audits?
The platform supports controlled access, historization, lineage, versioning, evidence chains, and governed data products. Legal and regulatory compliance still requires customer-specific processes, responsibilities, and review.
Can we start without GPU hardware?
Yes. Core ingestion, Data Lakehouse, BI, SQL analytics, governance, and many automation use cases do not require dedicated GPUs. GPU capacity becomes relevant for selected model training and advanced on-premises AI workloads.
How quickly can the first MVP become productive?
The timeline depends on source accessibility, data quality, security approvals, and scope. A strong MVP is deliberately narrow: connect priority sources, establish one data product, and prove one measurable business outcome.
Which internal resources are required?
Successful delivery typically involves a business owner, data and system experts, IT and security stakeholders, and representatives from the consuming function such as operations, trading, or controlling.
How do we reduce vendor lock-in?
Use open table formats, documented interfaces, portable data products, clear ownership, and standardized APIs. Architecture decisions should separate the data foundation from individual consumption tools wherever practical.
How are KPI definitions and data quality governed?
Definitions, formulas, filters, data sources, quality rules, owners, and versions are documented centrally. Data lineage links each KPI back to its transformations and source records.
Can external partners access only selected data?
Yes. The Consumption Layer and API Gateway can enforce authentication, authorization, role-based access, field-level policies, rate limits, monitoring, and audit trails for selected data products.
What is the difference from a traditional Data Warehouse?
A Data Lakehouse combines flexible storage of raw and semi-structured data with governed tables, versioning, high-performance analytics, and curated business products-without forcing all use cases into one rigid warehouse model.
Ready to define the first business-value MVP?
Start with one high-value decision, the required data, and a measurable baseline.
Energy Data Lakehouse
