Data 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:
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.
Similar big data challenges are present at almost all environmental and sustainability monitoring activities from greenhouse gases and air emissions management to surface water and drinking water quality management to monitoring of wide spread, low level contamination associated with the application of pesticides and herbicides in the agricultural industry.
Taking Control of Your Data
The most common recommendation I make to companies to improve their EHS programs and lower operating cost is to assume greater control of their own data. By this I do not mean that they have to collect their own samples or generate their own reports. However, I tell them they should become more familiar with the information gathering and management practices of their field sampling crews, consultants, laboratories, and other suppliers to determine if appropriate and up-to-date tools and technologies are being used. I urge them to take the long-term view and when appropriate, embrace change. Too many years of monitoring and reporting lie ahead to continue using outdated technologies, especially when more cost-effective alternatives exist like web-enabled mobile devices, wireless sensors and in-situ web-enabled measurement instrumentation. These are all connected to cloud-based databases that offer real-time data validation.
Lastly, I encourage companies to move their data from individual databases and spreadsheets to a central, web-based repository where it can be accessed by both themselves and their consultants, laboratories, and other stakeholders. This imposes standardized processes and valid values across all sites and emission sources and eliminates the data transfer costs that plague so many projects and more importantly, a key stranglehold that often prevents a company from switching to different third-party providers.
Cleanup vs. Monitoring
Most companies have no problem spending about $200 to obtain a single concentration reading of a chemical in a water sample, but will not approve the additional investment of approximately 5 cents to store and manage that same piece of information to their advantage. Why? Because immediately after obtaining that concentration reading they focus on the only thing they know: the technology to clean it up or the way to contain and monitor it. After all, most employees responsible for environmental decisions in large companies are engineers or geologists, and that is what they know how to do.
Unfortunately, in some cases employers may request that instead of cleaning it up, the issue be placed in an ongoing monitoring process in order to avoid a large, upfront expense. Often companies think monitoring is less expensive then cleaning up. However, our experience shows that many times long term monitoring costs dwarf the cleanup cost, particularly if inefficient monitoring, data management, and reporting systems are applied. They sign up for a long-term annuity! Yet, employers continue to supply the right tools to figure out treatment technologies, and will not allocate budget toward the right monitoring tools. This vicious circle prevents companies from realizing a potentially huge amount of savings that is available to them if they could only depart from these old practices and embrace new thinking.
A Quality Foundation
Meanwhile, constantly expanding and mounting regulatory compliance requirements dictate increased data vigilance. A company can have all the controls in place, but if the data is not accurate, they’ll be signing off on, and making important decisions based off of inaccurate information. Environmental data quality inaccuracy leaves organizations and their management at significant risk.
With the increasing volume of information collected through a variety of channels, there is more room for human error. This combined with the prevalence of segmented, departmental approaches to data accuracy is preventing stakeholders from analyzing, improving and controlling data problems.
Organizations that are not able to control the quality of their environmental data are unable to effectively communicate with regulators. Data quality is the foundation for any data-driven environmental effort and in order to succeed in the years ahead, organizations will need to look at prioritizing environmental data accuracy and accessibility. It may not be sexy, but it sure is important.
Neno Duplan is the president & CEO of Locus Technologies, an environmental data management software company that’s been providing businesses with the power to be green-on-demand since its founding in 1997. Locus’ cloud software enables companies to organize and validate all key environmental information in a single system, which includes analytical data for water, air, soil, greenhouse gases, sustainability, compliance, and environmental content. For more information on Locus Technologies, visit: www.locustec.com.
