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Case Study

Data remediation

Offshore oil field at sunset

We helped our client to establish a consistent class library for their data and imported all of their source data into a single engineering data warehouse.

The challenge

Our client wanted to create a plant environment where their plant operations and maintenance teams could access any piece of engineering data, wherever they were and regardless of the source data. They managed to achieve this, however, they soon realized that their data wasn’t reliable.

Their data was riddled with missing fields and there were discrepancies between the different data sources. These issues meant that incorrect parts were being procured for maintenance, and there was no certainty about what was in the field.

We worked with our client to establish a consistent class library for their data, importing all their source data into a single engineering data warehouse. From there, we were able to generate reports to highlight the gaps and discrepancies.

Advisian digital solution

We delivered a fast, accurate and governed strategy and approach to improve our client’s data integrity using proven and automated technology to:

  • Create a highly-automated approach to data remediation, allowing multiple data sets to be examined across multiple assets

  • Identify and remediate missing information using in-built libraries and automated processing – where manual engineering assessments were required, it was provided thought our global deliver centres

  • Assess extremely large and varied data sets against structured data framework - where there were discrepancies or missing data, be it vendor or engineering data, the remediation process ensured a complete and accurate data set.

The end result was a centralized data warehouse with complete and accurate data sets, creating a single source of truth for our client.


  • Visibility of consistent and accurate master data in all key data sources, increasing confidence in operating information

  • Rapid implementation of a platform that consolidates data from disparate sources to identify data discrepancies


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