Critical Data Product Definition
Definition of critical data products for operations, market, analytics and AI, with named owners and SLAs.
Run energy data the way you run mission-critical applications.
Logiciel runs data reliability engineering for energy operators, utilities, renewables and oil and gas. SLAs, monitoring, lineage and on-call for grid, historian, market and operational data, with controls aligned to NERC CIP and ISO 27001. We work alongside data, operations and reliability teams to make energy data dependable enough for operational and analytical use.
Energy data combines operational urgency with regulatory weight, in a way that few industries match.
We give energy data and reliability teams a real reliability practice they can run.
We cover the reliability areas that recur across energy operators.
Definition of critical data products for operations, market, analytics and AI, with named owners and SLAs.
Monitoring, freshness, schema and quality checks across pipelines, warehouses and lakehouses.
Reliability engineering for OSIsoft PI, AVEVA, GE Predix, Honeywell and Siemens integrations.
Reliability engineering for ISO and RTO feeds, market data, trading systems and risk analytics.
Reliability engineering for streaming and CDC pipelines on Kafka, Kinesis, MSK and Flink.
End-to-end lineage from source to consumption, including AI workloads, with impact analysis for incidents and changes.
Incident response practice with named owners, runbooks, post-mortems and KPIs aligned with operational impact.
Dedicated Data Reliability Squad
A long-running team of data reliability engineers, data engineers and platform engineers embedded in your data function.
Data Reliability Advisory and Staff Augmentation
Senior data reliability engineers who reinforce your in-house team during specific phases.
Outcome-Based Data Reliability Engagements
Fixed-scope engagements, for example a SLA rollout, a critical data product reliability programme or a data incident response setup.
Definition of critical data products with named owners and SLAs.
SLA design per critical data product, tied to operational and analytical impact.
Monitoring, freshness, schema and quality checks across pipelines, warehouses and lakehouses.
Reliability engineering for OSIsoft PI, AVEVA, GE Predix, Honeywell and Siemens integrations.
Reliability engineering for ISO and RTO feeds, market data, trading systems and risk analytics.
Reliability engineering for streaming and CDC pipelines on Kafka, Kinesis, MSK and Flink.
End-to-end lineage from source to consumption, including AI workloads.
Incident response practice with named owners, runbooks, post-mortems and KPIs.
Patterns from our delivery teams that have run through real energy deployments.
Energy Data Reliability Operating Model
A reference operating model for SLAs, monitoring, lineage and incident response in energy environments.
NERC CIP Aligned Data Reliability Pattern
A reference pattern for data reliability engineering inside NERC CIP and ISO 27001 controls.
1. Discovery and Reliability Assessment
We map current critical data products, incidents, monitoring gaps and operating practice.
2. SLA and Operating Model Design
We design SLAs per critical data product and the operating model around them.
3. Tooling and Integration
We implement monitoring, lineage, observability and incident tooling, integrated with your data platform.
4. Roll Out and On-Call
We roll out across critical data products, establish on-call and run the first incident reviews.
5. Operate and Improve
We move into a steady-state operating model with reviews, dashboards and KPIs.
Ready to treat Data Reliability Engineering Services for Energy as production engineering instead of a side project? Partner with Logiciel to design, build and operate Data Reliability Engineering Services for Energy that engineering, security and business teams can all defend.
We cover strategy, architecture, build, deployment and operations for Data Reliability Engineering Services for Energy, aligned with your business priorities and operating constraints.
Most engagements reach a working pilot within 4-8 weeks, while larger rollouts run across phased waves over several months.
Yes. We integrate with cloud platforms, CRMs, ERPs, EHR, OT systems, analytics tools and other operational infrastructure depending on the use case.
Yes. We offer milestone-based pricing once scope, KPIs and delivery requirements are agreed.
You retain ownership of all workflows, integrations, prompts, infrastructure, systems and implementation assets.
We implement governance frameworks, observability, access controls, audit trails and compliance-aligned deployment practices.
We tune infrastructure, automate resource management, optimise deployment workflows and report operational cost back to teams and product lines.
Yes. We run managed operations with SRE, observability, on-call and continuous improvement.