EHS Data Validation: A Blend of Human and Software Excellence
Keeping track of air quality and EHS requirements in an active refinery requires constant analysis of millions of pieces of data, to which complex rules must be applied. The results must be made available for interpretation across a range of parameters – a task that expands by orders of magnitude when a company has numerous refineries operating in different countries.
Performing data analysis and validation on this scale is highly labor intensive. Until recently, specialists had to spend large amounts of their time manually collecting and entering the data, relying on tools like spreadsheets and email. Only then could they intervene to make engineering judgment calls. It was mainly a manual process.
The Power of Automation
In the age of big data and wireless connectivity, manual approaches present two significant challenges. First, more data is needed than ever before, and for more reasons. For any industry operating today, data is king. Data moves around the world like a planetary information flow, and the speed and accessibility of internet technologies create a hunger for data that never ends.
Limitless computing power means data can be observed, repositioned and compared in infinite ways, and from such new approaches come innovation and new business opportunities.
The second challenge is that human activity should not be wasted on low-level tasks and data analysis, regardless of their sophistication. Software can do that faster, and can do it full-time, while removing the risk of human error. It can validate the thousands of data points used to calculate air emissions for compliance reporting, apply complex logic rules that automatically review the data, and use those rules to flag the outliers that require human attention while ensuring legal compliance.
Engineers and other specialists take great pride in a concept called engineering judgment, which boils down to intuition and wisdom based on education and experience. The problem emerges when that judgment is misplaced or misused if only because of tools and circumstances.
Typically, in any large organization like a refinery, data analysis is laborious to configure and difficult to transfer to others. But one of the critical advantages of big data is how well it can be shared. The collaborative nature of a connected workspace magnifies knowledge and extends its reach. By contrast, performing assessments on proprietary spreadsheets leads to isolated work, inadequate documentation, and inconsistencies between validators at different locations.
Blending Judgement with Automation
Since quality EHS data is needed for successful EHS management programs, the ideal solution comes when engineering judgment is captured and incorporated into the validation rules of the software. This takes the best of human wisdom and places it inside a software module that can run thousands of records every minute, and can do so 24 hours a day, or during ideal computing hours.
Software can use predefined triggers to substitute data or signal for a user decision. Statistics show that by doing this, an organization can drop data validation time by 85 percent.
This opens the door for sound engineering judgment at the right times by the right specialists wherever they may be. It is a perfect match of computing power and human intellect.
What to Watch for in a Best-of-Breed EHS Data Validation System
Potential buyers of EHS data validation software for industrial purposes will need to know how the data gets pulled into the software, and what the timeline is for results.
Given the sensitivity and variability of data in different facilities – not just refineries but in any industry – air quality data and other EHS data validations require careful calibration. Buyers should seek suppliers that can work on a case-by-case basis.
Clients and suppliers should work together to set up flexible data integrations and use cases to figure out where the data currently resides, and then develop a data solution that fits their specific air quality or EHS output requirements. A top-quality system will have a significant level of depth to its framework of validation rules to match that of the human specialists who rely on it.
Summary of Key Attributes to Look for in an EHS Data Validation System
In conclusion, make sure the following attributes are present:
- A smart user interface for data entry. Rather than relying on data manually entered into a spreadsheet, which is a primary source of error, use a smarter system that guides users into entering the right type of data, and prevents the insertion of data that does not correspond to pre-defined types and formats.
- Mobile data capture that allows entry of incident information in the field in real-time, using tablets, smartphones and smartwatches, even in areas where there is no internet or cellular access.
- Validation rules and consistency checks. This is the true advantage of a powerful software solution, since it can handle far more data without error than a human possibly could.
Download NAEM’s 2017 EHS and Sustainability Software Buyer’s Guide and learn more about the business objectives for EHS and Sustainability software buyers, desired software capabilities, and their selection criteria.