The client, a large electric utility, was investing in an enterprise data management program to turn a sprawling data estate into a trusted, well-understood asset. Data lived across many systems, and analysts routinely lost time to two questions before they could do any real work: where did this data come from, and can I trust it?
Adoption of the platform was growing, but training was often built on guesswork rather than real demand, and teams did not always know which datasets to use or how to access them. Analysts also flagged structural barriers, including data that was hard to find, inconsistent manual entry, and unclear ownership.
At the same time, some of the utility’s most valuable operational data, including LiDAR, aerial and drone imagery, and engineering documents, lived entirely outside the platform. Without a governed home for that information, the business could not easily reuse it or build the next generation of geospatial and digital-twin capabilities on top of it.
IBM Consulting partnered with the client, Snowflake, and Informatica to advance the program on three fronts that reinforce one another: making data trustworthy, making it usable, and extending the platform to new kinds of data. On governance and quality, the team introduced a data catalog and governance capability that connects business lineage, quality rules, and classifications in one place. A data-quality dashboard flags records, attributes, and values that need attention so the business can fix errors at the source, and a broad review of data-element classifications corrected wrongly tagged fields and resolved an integration issue between the governance and data-quality tools.
On talent and adoption, the team asked analysts how they actually work and what they most want to learn, then shaped the training plan around those answers rather than assumptions. Practitioners walked teams through the tables behind their dashboards, ran guided tours of key mapping and records data, and helped groups confirm access and pinpoint the exact datasets they needed. A quick-reference guide and targeted training rounded out the enablement effort.
On emerging technology, the team centralized unstructured data such as LiDAR, aerial imagery, and engineering documents into governed storage spanning development, test, and production, and connected upstream data providers so new imagery can flow into a shared repository for reuse. In parallel, a digital-twin discovery effort worked with subject-matter experts to validate real operational use cases first, so that future investment is directed at proven needs rather than speculative build.
