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Getting Started With ESG Is Less Daunting Than You Think

One of the most frequent questions we get asked when it comes to ESG is, “Where do I begin?”. For many companies, the process of getting started with a new ESG program is the most difficult step. With nearly 1,700 frequently evolving ESG reporting protocols available, it can be daunting just to determine where to begin. This uncertainty associated with ESG reporting can unfortunately paralyze any progress for several organizations. The good news is that ESG doesn’t have to be an ‘all or nothing’ effort. In fact, getting started is a simple and straightforward process.

Regardless of what ESG reporting program you choose (or eventually choose), there are many common elements that can form the basis of your organization’s ESG program. Although social and governance KPIs have been undergoing rapid evolution recently, environmental KPIs have been comparatively stable. Environmental KPIs tend to be quantitative with established calculation methodologies, whereas the definitions and determinations as to what is important regarding societal and governance factors and how to measure them are still being evaluated globally. Considering this, many companies elect to start their ESG reporting program using monitoring and collecting environmental data.

Additionally, almost all reporting programs include the concept of a baseline, or a time period against which future ESG metrics are compared. Developing the baseline requires a good understanding of your organization’s current ESG performance, which of course requires a good set of data. Universal data that is required for any ESG reporting program includes data on greenhouse gas, water quality and consumption, waste, and energy consumption. The bedrock of an ESG program starts with the collection, management, and reporting of these data. This information can also help to inform further decisions for your ESG program, including which framework is most appropriate for your organization.

As part of this effort, you should make sure you are collecting and calculating your ESG metrics with software that supports the required complexity of environmental data. Often the companies who suggest a turnkey solution to ESG reporting are not only lacking in social and governance data, but are woefully underprepared and unequipped to handle environmental data as well. With over 25 years of experience in creating software for environmental reporting, Locus Technologies is equipped to help organizations collect and report ESG data in a way that others aren’t.

Combat Green Skepticism with Accurate ESG Data

Greenwashing, or the presenting of misinformation to create a sustainable image, is common among organizations. While many consumers may not be aware with the term greenwashing, they are aware of how common it is. In fact, consumers are so aware of this trend that they’re overwhelmingly skeptical of all organizations presenting themselves as sustainable. Four out of every five consumers have expressed skepticism of organizations claiming to be sustainable. So, how does your company express your sincere desire to take steps that are sustainable for the environment? With accurate and transparent data.

With 2/3 of consumers seeking out companies that emphasize sustainable practices, the temptation to greenwash is certainly enticing. Sometimes it comes in the form of making irrelevant claims, like saying a product is free of something that is banned (like CFCs). Other times it comes in the form of half-truths, like saying that a product is sustainably sourced, despite the manufacture of the product being unsustainable or harmful to the environment. Any way you look at it, the demand for sustainable products is so high, as is the temptation to greenwash. The truest, and least disputable way to combat greenwashing is by collecting and reporting your data accurately.

Sustainability is now a broad umbrella term that encompasses not only environmental practices, but social and corporate governance as well, better known as ESG. This broadness reflects a change in consumer attitudes from generation to generation, perceiving more than environmental practices as important while holding environmental practices to the microscope. In fact, over 75% of Gen X consumers say that they have to trust a brand before purchasing from them, and over 85% of Millennial and Gen Z consumers say they same. This trust encompasses everything from the use of organic ingredients to company wellness practices, and is reinforced with buying practices. To do environmental, social, and corporate governance right, organizations have taken a data-first approach.

With Locus Technologies, you can take concrete steps towards achievable ESG goals. By taking a fully-digital approach, your organization can make the transformation by maintaining full visibility of raw sustainability data, calculations, and other factors, and also keeping data easily accessible and traceable. Reports are fully traceable back to the source, and are indisputable, allowing for increased trust from consumers or anyone else who has a stake in this information. Given that 7/10 consumers are willing to pay a premium to sustainable-minded companies who are fully transparent with their efforts, this move can provide a significant return on investment in the short term.

The benefits of data centralization also go beyond combatting greenwashing. A fully-digital and streamlined process will improve your ability to handle the data appropriately, and will ease any auditing and reporting responsibilities moving forward, making the entire process cheaper and faster.

With brand loyalty and purchasing decisions being reliant on sustainable decisions, the move to accurate and transparent data management is key. By implementing Locus Technologies ESG software, your organization can employ cutting-edge solutions to combat greenwashing by promoting your sustainability goals and actions transparently and accurately.

Quicker Data Searching with Natural Language Processing

The recent year of lockdowns pushed many daily activities into the virtual world. Work, school, commerce, the arts, and even medicine have moved online and into the cloud. As a result, considerably more resources and information are now available from an internet browser or from an application on a handheld device. To navigate through all this content and make sense of it, you need the ability to quickly search and get results that are most relevant to your needs.

You can think of the web as a big database in the cloud. Traditionally, database searches were done using a precise syntax with a standard set of keywords and rules, and it can be hard for non-specialists to perform such searches without learning programming languages. Instead, you want to search in as natural a matter as possible. For example, if you want to find pizza shops with 15 miles of your house that offer delivery, you don’t want to write some fancy statement like “return pizza_shop_name where (distance to pizza shop from my house < 15 miles) and (offers_delivery is true). You just want to type “what pizza shops within 15 miles of my house offer delivery?” How can this be done?

Enter the search engine. While online search engines appeared as early as 1990, it wasn’t until Yahoo! Search appeared in 1995 that their usage became widespread. Other engines such as Magellan, Lycos, Infoseek, Ask Jeeves, and Excite soon followed, though not all of them survived. In 1998, Google hit the internet, and it is now the most dominant engine in use. Other popular engines today are Bing, Baidu, and DuckDuckGo.

Current search engines compare your search terms to proprietary indexes of web page and their content. Algorithms are used to determine the most relevant parts of the search terms and how the results are ranked on the page. Your search success depends on what search terms you enter (and what terms you don’t enter). For example, it is better to search on ‘pizza nearby delivery’ than ‘what pizza shops that deliver are near my house’, as the first search uses less terms and thus more effectively narrows the results.

Search engines also support the use of symbols (such as hyphens, colons, quote marks) and commands (such as ‘related’, ‘site’, or ‘link’) that support advanced searches for finding exact word matches, excluding certain results, or limiting your search to certain sites. To expand on the pizza example, support you wanted to search for nearby pizza shops, but you don’t want to include Nogud Pizza Joints because they always put pineapple on your pizza. You would need to enter ‘pizza nearby delivery -nogud’. In some ways, with the need to know special syntax, searching is back where it was in the old database days!

Search engines are also a key part of ‘digital personal assistants’, or programs that not only perform searches but also perform simple tasks. An assistant on your phone might call the closest pizza shop so you can place an order, or perhaps even login to your loyalty app and place the order for you. There is a dizzying array of such assistants used within various devices and applications, and they all seem to have soothing names such as Siri, Alexa, Erica, and Bixby. Many of these assistants support voice activation, which just reinforces the need for natural searches. You don’t want to have to say “pizza nearby delivery minus nogud”! You just want to say “call the nearest pizza shop that does delivery, but don’t call Nogud Pizza”.

Search engine and digital personal assistant developers are working towards supporting such “natural” requests by implementing “natural language processing”. Using natural language processing, you can use full sentences with common words instead of having to remember keywords or symbols. It’s like having a conversation as opposed to doing programming. Natural language is more intuitive and can help users with poor search strategies to have more successful searches.

Furthermore, some engines and assistants have artificial intelligence (AI) built in to help guide the user if the search is not clear or if the results need further refinement. What if the closest pizza shop that does delivery is closed? Or what if a slightly farther pizza place is running a two-for-one special on your favorite pizza? The built-in AI could suggest choices to you based on your search parameters combined with your past pizza purchasing history, which would be available based on your phone call or credit charge history.

Searching in Locus EIM

The Locus team recently expanded the functionality of the EIM (Environmental Information Management) search bar to support different types of data searches. If a search term fits several search types, all are returned for the user to review.

Functionality searches: entering a word that appears in a menu or function name will return any matching menu items and functions. For example, searching for ‘regulatory exports’ returns several menu items for creating, managing, and exporting regulatory datasets.
Help searches: entering a word or phrase that appears in the EIM help files will return any matching help pages. For example, ‘print a COC’ returns help pages with that exact phrase.
Data searches: entering a location, parameter, field parameter, or field sample will return any matching data records linked with that entity. For example, searching for the parameter ‘tritium’ returns linked pages showing parameter information and all field sample results for that parameter. Searching for the location ‘MW-1’ returns linked pages showing all field samples, groundwater levels, field measurements, and field sample results at the location.

EIM lets the user perform successful searches through various methods. In all searches, the user does not need to specify if the search term is a menu item, help page, or data entity such as parameter or location. Rather, the search bar determines the most relevant results based on the data currently in EIM. Furthermore, the search bar remembers what users searched for before, and then ranks the results based on that history. If a user always goes to a page of groundwater levels when searching for location ‘MW-1’, then that page will be returned first in the list of results. Also, the EIM search bar supports common synonyms. For example, searches for ‘plot’, ‘chart’, and ‘graph’ all return results for EIM’s charting package.

By implementing the assistance methods described above, Locus is working to make searching as easy as possible. As part of that effort, Locus is working to add natural language processing into EIM searches. The goal is to let users conduct searches such as ‘what wells at my site have benzene exceedances’ or perform tasks such as ‘make a chart of benzene results’ without having to know special commands or query languages.’

How would this be done? Let’s set aside for now the issues of speech recognition – sadly, you won’t be talking to EIM soon! Assume your search query is ‘what is the maximum lead result for well 1A?’

First, EIM extracts key terms and modifiers (this is called entity recognition). EIM would extract ‘maximum’, ‘lead’, ‘result’, ‘well’, and ‘1A’, while ignoring connecting words such as ‘the’ or ‘for’.
Then, EIM categorizes these terms. EIM would be ‘trained’ via AI to know ‘lead’ is mostly used in environmental data as a noun for the chemical parameter, and not a verb. ‘Result’ refers to a lab result, and ‘well’ is a standard sampling location type.
EIM then runs a simple query and gets the maximum lead result for location 1A.
Finally, EIM puts the answer into a sentence (‘The maximum lead result at location 1A is 300 mg/L on 1/1/2020’) with any other information deemed useful, such as the units and the date.

A similar process could be done for tasks such as ‘make a chart of xylene results’. In this case, however, there is too much ambiguity to proceed, so EIM would need to return queries for additional clarifications to help guide the user to the desired result. Should the chart show all dates, or just a certain date range? How are non-detects handled? Which locations should be shown on the chart? What if the database stores separate results for o-Xylene, m,p-Xylene, plus Xylene (total)? Once all questions were answered, EIM could generate a chart and return it to the user.

Natural language is the key to helping users construct effective searches for data, whether in EIM, on a phone, or in the internet. Locus continues to improve EIM by bringing natural language processing to the EIM search engine.

About the Author—Dr. Todd Pierce, Locus Technologies

Dr. Pierce manages a team of programmers tasked with development and implementation of Locus’ EIM application, which lets users manage their environmental data in the cloud using Software-as-a-Service technology. Dr. Pierce is also directly responsible for research and development of Locus’ GIS (geographic information systems) and visualization tools for mapping analytical and subsurface data. Dr. Pierce earned his GIS Professional (GISP) certification in 2010.

Streamline and Save on Your Title V Reporting

Simplify your air quality data management and reporting with Locus’ unified software solution. Our Air Quality application resolves most common issues with managing and submitting your site emissions data. Locus handles all required regulatory data from your facilities in one centralized platform and makes it possible to streamline your tracking and reporting requirements for programs such as Title V, GHG, Fenceline, and LCFS.

Streamline and Save on Your DMR Reporting

The DMR tool in Locus’ Environmental Information Management (EIM) software solves the problem of time-consuming, labor-intensive, and expensive manual report generation by automating the data assembly, calculations, and formatting of Discharge Monitoring Reports. Depending on the type of discharge and the regulatory jurisdiction, you may be required to report information such as analytical chemistry of pollutants, flow velocity, total maximum daily load, and other parameters. For companies that report on multiple facilities, producing a DMR also becomes a major expense.

Thanks to Locus’ DMR tool, companies can generate DMRs within minutes with validated data in approved formats, with all of the calculations completed according to regulatory requirements. Companies can set up EIM for its permitted facilities and realize immediate cost and time savings during each reporting period. Locus users have saved over $2,000,000 on DMR reporting.

Locus continues to enhance the Discharge Monitoring Report (DMR) tool, recently implementing calculations needed to handle reporting of divalent metals. New formats, such as Florida DEP ezDMR, are regularly being added, so customers can meet their reporting requirements.

5 Ways To Save With Locus

For over 20 years, Locus environmental software customers have saved enormously on their setup and and data entry costs. This infographic highlights the aggregate savings of all users based on conservative estimates of time and cost for different aspects related to our software.

Sustainability is More Important Now Than Ever

The global economy is currently being tested on a magnitude that we have never witnessed before. The effects of COVID-19 have pushed the limits of individuals, and the organizations that they run. As we collectively face short-term problems related to the pandemic, long-term effects of climate change have, in some ways, been magnified. When the dust settles, and we tackle COVID-19, we will still be facing the consequences of climate change. It is now, however, not after COVID-19 is controlled, that organizations must make steps towards tackling environmental issues. On a positive note, there is a return on investment in sustainability, and there are pragmatic ways of achieving sustainable goals.

The connection between saving money and resources and investing in sustainability is well known. Year after year, sustainable projects result in billions of dollars in savings for the companies investing in them. By 2030, return on investment in sustainability will be $26 trillion. And while those companies investing in sustainability have better numbers, they’re continuing to push for higher sustainability goals, as are government agencies. Companies not making these investments are not only missing out financially, but they are falling behind when it comes to long-term preparedness. Without a doubt, the organizations who are acting first have the leg up. When it comes to sustainability, two proverbs attributed to Benjamin Franklin are as true as when he first said them. “An ounce of prevention is worth a pound of cure”, and “a penny saved is a penny earned.” Pair those variables with the ever-growing awareness and importance of sustainability by the green investor and the green consumer and you have a powerful combination.

Nearly 80,000 emission-reducing projects by 190 Fortune 500 companies reporting data showed nearly $3.7 billion in savings in 2016 alone. – WWF | worldwildlife.org

With climate-related issues comprising the top five long-term risks in terms of likelihood, the need for investing in sustainability becomes all the more apparent. This sentiment is mirrored in a recent Bloomberg article, where Bill Gates suggests that the most difficult long-term problem facing the world today is climate change, and the effect it has on the environment. While outlining several difficulties, he points to one shining light in the fight to sustain a healthy climate: the acceleration and innovation of technology over the past two decades created to tackle the problem. Not many understand more than Locus the fight to maintain, and reduce, and use resources wisely. Locus has, for over two decades, provided advanced tools to improve sustainability on a grand scale.

Several organizations have taken advantage of the sustainability software solutions Locus provides. One example is Del Monte Foods, one of the largest producers of food in the world. They partnered with Locus for sustainability data management, eliminating errors in old data and better monitoring resource usage and cost. They also use Locus’ sustainability app to visualize and report data on the fly. They are tackling sustainability from a practical standpoint, addressing real data, not a nebulous idea. And they have been better off for acting early instead of waiting.

In the end, we must address the problems that face us. We need to tackle COVID-19 and how it affects our organizations, but be mindful that every quarter and every year that sustainability goals are pushed back, there are dollars being lost seeking out attainable improvements to our environment. Not only that, but every step that isn’t taken towards sustainable goals is a step behind other organizations making practical investments in their future and the wellbeing of everyone.

See the Sustainability App.

AI for EHS&S: Three Essential Steps to Get Started

Regardless of the size of your organization or the industry you’re in, chances are that right now artificial intelligence can benefit your EHS&S initiatives in one way or another. And whether you are ready for it or not, the age of artificial intelligence is coming. Forward-thinking and adaptive businesses are already using artificial intelligence in EHS&S as a competitive advantage in the marketplace to great success.

With modern EHS&S software, immense amounts of computing power, and seemingly endless cloud storage, you now have the tools to achieve fully-realized AI for your EHS&S program. And while you may not be ready to take the plunge into AI just yet, there are some steps you can take to implement artificial intelligence into your EHS&S program in the future.

Perhaps the best aspect of preparing for AI implementation is that all of the steps you take to properly bring about an AI system will benefit your program even before the deployment phase. Accurate sources, validated data, and one system of record are all important factors for any EHS&S team.

Accurate Sources

Used alongside big data, AI can quickly draw inferences and conclusions about many aspects of life more efficiently than with human analysis, but only if your sources pull accurate data. Accurate sources data will help your organization regardless of your current AI usage level. That’s why the first step to implementing artificial intelligence is auditing your data sources.

Sources pulling accurate data can be achieved with some common best practices. First, separate your data repository from the process that analyzes the data. This allows you to repeat the same analysis on different sets of data without the fear of not being able to replicate the process of analysis. AI requires taking a step away from an Excel-based or in-house software, and moving to a modern EHS&S software, like Locus Platform that will audit your data as it is entered. This means that anything from SCADA to historical outputs, samples, and calculations can be entered and vetted. Further, consider checking your data against other sources and doing exploratory analysis to greater legitimize your data.

Validated Data

AI requires data, and a lot of it—aggregated from multiple sources. But no amount of predictive analysis or machine learning is going to be worth anything without proper data validation processes.

Collected data must be relevant to the problem you are trying to solve. Therefore, you need validated data, which is a truly difficult ask with Excel, in-house platforms, and other EHS&S software. Appropriate inputs, appropriate ranges, data consistency, range checks (to name a few)—are all aspects of data that is validated in a modern EHS&S software like Locus Platform. Without these checks inherent to a platform, you cannot be sure that your data, or your analyses are producing useful or accurate results.

Possibly the best reason to get started with AI is the waterfall effect. As your data uncovers hidden insights and starts to learn on its own, the more accurate your new data will be and the better your predictions will become.

One System of Record

A unified system of record and a central repository for all data means that you see an immediate increase in data quality. Starting with AI means the end of disconnected EHS&S systems. No more transferring data from one platform to another or from pen and paper, just fully-digitized and mobile-enabled data in one platform backed up in the cloud. You also gain the added benefit of being able to access your data in real-time, incorporate compliance/reporting on the fly, and save time and resources using a scalable solution instead of a web of spreadsheets and ad-hoc databases.

Whether you are ready for AI or not, investing in these otherwise useful steps are necessary for any program looking to harness the power of artificial intelligence. When you are ready to take that next step, you will be well on the path to AI implementation, with a solid data infrastructure in place for your efforts.

To learn more about artificial intelligence, view this NAEM-hosted webinar led by Locus experts, or read our study on predicting water quality using machine learning.

Infographic: 12 Ways SaaS Can Improve Your Environmental Data

Software as a service (SaaS) databases offer several unique features that allow you to manage your environmental data more thoroughly and efficiently. This infographic highlights twelve key features of SaaS databases for environmental software.

This infographic was created based on a four part series of blog posts on the same topic, which can be read here.

PennJersey Environmental Consulting selects Locus EIM SaaS-based software for its environmental compliance data

Locus’ EIM Solution Will Streamline PennJersey Consulting’s Entire Environmental Laboratory Data Validation and Reporting

MOUNTAIN VIEW, Calif., 11 December 2018 — Locus Technologies, (Locus), the industry leader in EHS, sustainability, and compliance management software, is pleased to announce that PennJersey Environmental Consulting (PennJersey), a leading environmental site assessment and remediation firm located in Milford, New Jersey, has selected Locus EIM SaaS-based software to more efficiently track analytical data, automate its field data collection, laboratory analyses, and overall enterprise data consolidation for its clients.

“With Locus EIM, our professionals and staff will be positioned to manage our laboratory data more efficiently. We were especially drawn to the ability to automate the laboratory data validation to assure the quality and usability of the data. EIM will provide our clients greater efficiency and allow us to focus on providing the timely and cost-effective solutions to their assessment and remediation challenges,” said Rodger Ferguson, President of PennJersey.

“Our deep understanding of the EHS compliance market enables us to quickly address environmental mandates, such as PennJersey’s tracking and management of soil, air, and groundwater data, with precision,” said Neno Duplan, Founder and CEO of Locus. “Locus EIM can indicate what levels of target compounds are in the soil, air, or water samples, how the data are trending, and provide real-time alerts to abnormalities. Overall, our solution ensures better monitoring, real-time analysis, aggregations and reporting of data that leverages a modern SaaS-based platform.”