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Dirty Data: The Behind the Scenes Threat to Environmental Projects

duplan, neno, locus technologiesData quality for EHS compliance or sustainability management isn’t a glamorous topic — especially when it comes to analytical data management. Data quality initiatives have long languished in the shadow of sexier projects. But due to compliance violations, regulatory fines, ill-advised decision making, failed EHS software implementations, and endless efforts to build yet another spreadsheet, that is starting to change.

Environmental and EHS data is no longer viewed as a secondary component of business. Today, information contained within a database is viewed by senior management and many departments as a critical factor in decision making, compliance, brand management, and cost reduction. Verify it and do it right the first time and every time is becoming the new standard.

Tools that address data quality fall into a variety of categories, including:

  • Data profiling, validation, and QA/QC software
    •  sift data fields for duplication, valid values, missing information and other errors
  • Data cleansing and matching tools
    •  parse data into discrete elements, clean it, standardize it in formats, and match and merge records
  • Data enhancement tools
    • enrich data by incorporating, for instance, third-party elements
  • Data monitoring tools
    • ensure that data maintains a preset level of quality

Data Quality and Decision Making

When Locus was hired by a major Fortune 10 company to centralize the management and data flow of their environmental analytical data for contaminated sites, we were also entrusted to examine the data quality at legacy systems we were supposed to replace with EIM: our web-based analytical data management system.  We thought that our main challenge was going to be moving the massive amount of data associated with thousands of sites and over twenty years of investigative and monitoring programs. But after a thorough review, we were surprised to find that almost every site we touched had significant data quality problems. Every silo application we looked at, it seemed, was loaded with redundant and inaccurate data; a very serious issue when you consider that these data were the cornerstone of multimillion dollar cleanup decisions in the past.

Is information management that important? Yes, it is, and it will become increasing so as most environmental and EHS programs are really never completed. In addition, every decision that EHS managers make, particularly those that are associated with large capital projects, hinges on having high-quality, error-free validated data at their disposal. Monitoring is here to stay for a long, long time, and more of it is coming. Even contaminated sites, after being cleaned up, enter what has come to be called the long-term stewardship (LTS) phase. At larger, more complex sites, it is not uncommon to drill several thousand boreholes and wells, collect tens of thousands of samples, and then analyze each of these for tens of hundreds of contaminants.  This information on site conditions must be entered and stored properly, then made readily available to managers, engineers, scientists, and regulatory agency personnel for reporting, analysis, and decision making.  Long-term monitoring of conditions at such sites, even after the initial cleanup is complete, can last for decades and cost from the thousands to several million dollars per year per site.

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One thought on “Dirty Data: The Behind the Scenes Threat to Environmental Projects

  1. Data cleaning is a critical issue when companies consolidate and upgrade their environmental management information systems. When moving data from legacy systems to a new system, what is your advice–from a data transparency perspective–where the company must add data to “new” database fields that did not exist in the legacy systems, but present in the new system?

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