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MetricsRobert Kimball
Director of Marketing
November 22nd, 2022 by Robert Kimball
Part 1 of this article focuses on improving data literacy. Read it here.
“If we have data, let's look at data. If all we have are opinions, let's go with mine.”
- James Love Barksdale, former president and CEO of Netscape Communications
Most organizations realized long ago that they can’t rely on opinions when it comes to EHS performance. They know they need structured data that informs decision making, but they don’t always know how to get it. Since you’re reading this, you’ve already taken an important first step toward becoming data-driven. The next step is an honest assessment of your organization’s data maturity and where your EHS department is on the data journey.
Stage 1 - Data Denial
In this stage, chaos rules. Here you will find paper-based processes and disparate spreadsheets scattered across network drives. With information and data locked away on desktops and forgotten folders, it’s nearly impossible for EHS leaders to identify or resolve issues. Fortunately, most organizations realize that EHS impacts are far too important and have moved beyond this stage.
Stage 2 - Data Indifference
In this stage, data is acknowledged but still unstructured. Here the dreaded “data silos'' lead to limited visibility and accountability. With no centralized data repository, collbusiness units are left to make their own independent decisions. Stage 2 is dominated by ‘gut feelings’ and ‘hunches’. “I think that…” or “I feel that…” are common drivers of decisions. Organizations with significant EHS risks dare not linger in this stage for long.
Stage 3 - Data Aware
In this stage, data may or may not be centralized, but it is still unstructured or only semi-structured. Some locations and business units may be working together, but data curation is still manual and time consuming. Combining data of multiple sources requires specialized roles or reports and is performed manually. “I think that…” or “I feel that…” are STILL common drivers of decisions but, like the quote above from Netscape’s former CEO, are now HIPPOs (the Highest Paid Person’s Opinion). EHS departments in the mid range of data maturity may still reside here.
Stage 4 - Data Informed
In this stage, data has been combined into a central repository, but may still take substantial effort to maintain or add new data sets to. Data curation is mostly automated and centralized dashboards have become popular with EHS and site leaders. Business decisions can now be made based on data analysis, but significant effort is required to get the data correlated. Most mature EHS departments have made it to, or are close to reaching, this stage.
Stage 5 - Data Driven
In this stage, data is king. The ability to add new data sets to a centralized repository has been streamlined, data curation is automated and are normalized at data entry. Less specialized roles are required to generate reports and analysis (generally an analytics platform is in place and has been optimized for use by non-developers). Business questions can be answered immediately, or in a short time-frame, and corporate EHS has full visibility across the enterprise. In a data-driven environment, “Trends indicate that…” and “What if we looked at…” become common phrases for business decisions.
Changing processes, creating new technical avenues, and adapting your culture to base decisions on data instead of feelings happens at multiple levels and doesn’t occur overnight. Even when you speak the “language of data” fluently, technical, cultural, and procedural pitfalls can trip you up if you’re not prepared. Here are some of the obstacles to watch for on the path to become data driven.
Avoid preconceived notions and sweeping changes
Even when you have become efficiently data driven, you will need to continually ask questions and look at your performance from different angles to evolve. Plan for this being a continuous improvement cycle and remember that small changes may be a better approach to get acceptance than sweeping alterations. In the world of software development, we talk about ‘iterations.’ Frequent cycles where theories are tested and small improvements, building upon each other, are continuously made. This same ‘continuous improvement’ cycle should be familiar to EHS and part of your data analytics approach.
Don’t limit future analysis opportunities
You may be focused on just getting off the ground today, but tomorrow you’ll want more data and more analysis. Consider your upfront investment of time vs. the cost to change later. Do the extra work up front to enable the “ideal future state” for your EHS analytics needs. How likely is missing data? Is your data set looking at information that’s often skewed, like on-time completion percentage of tasks? Consider these scenarios early in the process. Avoiding problems is much easier than fixing them later on.
Don’t get lost in the weeds
While you want to avoid future problems, remember to focus on your outcomes and don't let yourself get caught up in the details. It’s exciting to see all of your EHS variables in one place. It’s tempting to start building endless graphs and charts but, if it’s not getting you any closer to the answers you need, you’re probably in the weeds. Keep asking the question: “Does this really help us achieve our stated EHS goals?”.
Avoid “analysis paralysis”
Having more data available can sometimes be scary and it can cause you to overthink a problem to the point that you can’t make a decision. Setting due dates and prioritizing decision making criteria can help you know how much is too much when it comes to analyzing EHS data. In the end, data-driven processes will mitigate risk, not eliminate it. Data analytics and business intelligence can inform decision making, not replace it.
It’s clear that actionable business intelligence is essential to EHS management, where strong performance can reduce costly incidents and the likelihood of fines and penalties, improve moral and brand perception, and increase confidence with investors. While these benefits are well known, and the direct and indirect costs are well understood, effective data analytics capabilities have remained elusive for many EHS leaders. Why?
Decisions to make IT investments are usually made on the basis of a Business Case. How will costs be reduced? Risks mitigated? Efficiencies gained? By how much? How quickly? Answering these questions is essential to a successful pitch to senior management. Unfortunately, EHS leaders don’t always speak the same language as business leaders do and they often lack the experience to make the business case. These tips can get you started in the right direction.
Selecting an EHS analytics platform that meets your needs must also be part of your ROI discussion. Platforms like Power BI and Tableau have become popular and can handle the collection, integration, analysis, and presentation of all types of data, but they come with a steep learning curve, especially for those who aren’t already fluent in the language of data. These type of systems must also be integrated with your EHS systems, something that may require the support of your inhouse IT resources.
Platforms that integrate with your operational data “out-of-the-box” are beneficial as they negate the need to curate EHS data manually and can provide more consistent and accurate data for analysis. Systems should include out-of-the-box indicators of EHS performance, but also be flexible enough to accommodate your unique analytics needs.
For Dakota Software clients, Dakota Insights allows users to easily build and share customized dashboards that draw from the day-to-day operational data already managed in the ProActivity Suite. For those starting from scratch, or dissatisfied with their current EHS analytics capabilities, Dakota Insights provides a holistic view of EHS performance and, coupled with our regulatory content-driven ProActivity Suite, enables true end-to-end EHS management.
Contact us today to get started, or to take the important next step, on your data-driven journey.
Director of Marketing
