Utilities data engineering and analytics, since 2008
Utilities is the industry where DI Squared has the deepest delivery muscle. We have built grid, AMI, customer operations, and regulatory analytics for investor-owned utilities, municipals, and cooperatives continuously since 2008. We understand MDM, CIS, OMS, GIS, and DERMS, and we have integrated them with one another and with the analytics platforms the operations and rates teams actually use. We are vendor-neutral, but fluent in the platforms that matter.
Snowflake, Databricks, dbt, Fivetran fluent
HQ Atlanta, serving US, Canada, Europe
- Utilities is DI Squared’s deepest industry practice.
- Grid analytics, AMI, customer ops, regulatory reporting, DER and program analytics.
- Common stack: Snowflake or Databricks; Qlik (Elite Solution Provider), Power BI, or Tableau.
- We respect the regulator. Reporting is built to support a rate case, not just a dashboard.
What utility data engineering actually requires
Utility data engineering is the work of pulling MDM, CIS, OMS, GIS, work management, DERMS, and the meter data management layer into a model that can support both day-to-day operations and the next regulatory filing. The data is high volume (interval meter reads, SCADA, IoT), heterogeneous (vendor systems that did not anticipate one another), and politically consequential (every reliability number is a number a regulator may eventually examine). Utility analytics done well treats reliability, customer programs, and regulatory reporting as adjacent problems that share a data foundation, not as separate dashboards that share a logo.
- Utilities delivery experience since 2008, DI Squared’s deepest practice
- Investor-owned, municipal, and cooperative experience
- AMI rollouts, regulatory reporting modernization, grid and customer analytics
- Three-time Qlik Solution Partner of the Year (2013, 2017, 2019)
Utility analytics use cases we deliver
Utilities engagements typically cluster around a few patterns. Most clients are working on more than one at the same time.
Grid and reliability analytics.
SAIDI, SAIFI, CAIDI, MAIFI, momentaries, and the underlying outage and switching data; circuit-level reliability targeting.
AMI and meter-to-cash analytics.
Interval read quality, exception management, billing exception reduction, voltage and load analytics from interval data.
Customer operations and care analytics.
Call center, billing dispute, payment arrangement, disconnect/reconnect, and program enrollment analytics.
Regulatory and rate case support.
Reliability filings, cost-of-service inputs, program performance reporting, and the audit trail that a state PUC will accept.
Distributed energy and program analytics.
Net metering, energy efficiency and demand response programs, EV programs, and DER interconnection analytics.
Workforce and asset analytics.
Crew productivity, work management, vegetation management, and asset health.
A utility-specific data foundation
A utility data platform integrates CIS (Oracle CC&B, SAP IS-U, Itineris, Cayenta, NISC, and others), MDM, OMS (Oracle, GE, Survalent, and others), GIS (Esri ArcGIS Utility Network in most modern environments), work management, DERMS, and increasingly distribution and AMI data analytics platforms from the meter vendor side (Itron, Landis+Gyr, Sensus). We integrate these into a cloud data platform (typically Snowflake or Databricks) and model the data into a coherent warehouse layer your operations, customer, regulatory, and rates teams can each draw from. Where reliability and rate case reporting is involved, the lineage and audit trail are built into the design, not bolted on later.
Discover, Map, Navigate, Adjust in utilities
Discover.
We assess the CIS, MDM, OMS, GIS, work management, and any existing data warehouse footprint. We talk with operations, customer care, rates, regulatory, and IT. We look at what the regulator has asked for over the last three filings and what the team had to build by hand to answer.
Map.
We document a utility-specific target state: data domains, ingestion patterns, warehouse model, BI footprint, governance and lineage, and the sequence of work. The document is one a VP of operations, a regulatory director, a CIO, and a CFO can all sign off on.
Navigate.
We partner through delivery: pipelines, warehouse, semantic model, BI implementation, and rollout. Regulatory reporting and reliability work tend to anchor early phases because the value is immediate and the audit trail benefits compound.
Adjust.
Utilities operate on multi-year regulatory cycles. As rate cases, integrated resource plans, and program portfolios evolve, we help the data platform keep pace.
Utility technology stacks we work with
We are vendor-neutral, but fluent in the utility platforms that power modern operations. Our clients commonly rely on CIS, OMS, MDM, GIS, asset management, and customer service platforms from vendors such as SAP, Oracle Utilities, Esri ArcGIS, and Microsoft. We integrate these data sources to create a trusted foundation for analytics, reporting, and AI.
A typical engagement may use Snowflake or Databricks as the data platform, dbt for transformation, and Fivetran or vendor-specific connectors for utility system integration. For governance, we work with platforms such as Collibra, while Qlik, Power BI, and Tableau provide the analytics layer.
We are pragmatic about existing technology investments. If your organization has standardized on SAP and Qlik, Oracle Utilities and Power BI, or Esri and Tableau, we build on that foundation rather than recommend starting over. Our focus is maximizing value from the systems you already use while creating a scalable data platform for future growth. We are equally comfortable supporting cloud, hybrid, and on-premises environments when security or regulatory requirements demand it.
Utilities data engineering: common questions
Q: Do you work with municipal and cooperative utilities, or only investor-owned?
A: We work with all three. The data models and operational patterns are similar; the regulatory environment, financing model, and scale differ. We have delivered for investor-owned, municipal, and cooperative utilities and adapt the engagement model to fit.
Q: How do you handle AMI data volume?
A: AMI data is high volume but well-structured. We design ingestion patterns that land interval reads in a cloud data platform with appropriate partitioning and clustering (Snowflake) or Delta Lake patterns (Databricks). Analytics that need full interval detail run against the warehouse layer. Customer-facing or executive analytics run against aggregated layers that are governed and lineage-tracked.
Q: Can you support a rate case directly?
A: We do not provide regulatory testimony, but we routinely build the data and analytics foundation that supports a rate filing: reliability inputs, cost-of-service data, program performance, and the lineage and audit trail that a state PUC review will require. We work alongside your regulatory team and external counsel.
Q: Why is utilities DI Squared's deepest practice?
A: We started the firm in 2008 with utility clients and have delivered continuously in the sector ever since. The combination of regulatory complexity, operational scale, and the multi-year cadence of rate cases rewards a consultant that already understands the data and the operating environment. We have been doing that work in utilities longer than in any other sector.
Build a utility data platform that serves operations and the regulator
Talk with a DI Squared utilities strategist about your CIS, OMS, AMI, and reporting footprint. We have been doing this work since 2008 and will tell you honestly where to start.