Top 8 Things to Look For in Sustainability Software

Sustainability is a corporate necessity, and finding the right software to support company-wide sustainability goals and initiatives is imperative to streamlining this time-consuming activity.  This is especially true if you are managing inputs from many facilities/locations or have required or optional reporting requirements.  Not to mention, most corporate annual reports demand a summary of key sustainability initiatives as part of the corporate annual reporting process.

Here are some features to look for when selecting a sustainability software—to make sure your new software will actually help your company track and report its sustainability initiatives more accurately and efficiently.

1. Make sure software is accessible to everyone who needs to input data

 It is very important that data owners/data collectors throughout your facilities can directly enter their own relevant Key Performance Indicator (KPI) and greenhouse gas data—no more searching for data from disparate company groups, or searching through email for spreadsheets or invoices, and no more tracking down the field technician for the field log, or hunting for other assorted documentation.

This is especially important when dealing with company locations in various geographic regions. A well-designed software system can solve this most vexing problem: finding the relevant data.

 Check for the following features in any sustainability software you’re considering:
  • Data stored in one managed location
    All sustainability data should be stored in one place—whether text or numeric, and whether from an automatic data acquisition system, external database, hand-written field logs, or third-party documentation (e.g., air permits).
  • Streamlined reporting from centralized data
    Reporting is streamlined because all input is consolidated in one managed location.
  • Standardized terminology and units
    A centralized system enforces common terminology, units, and values (numbers vs. text) that are so important for final reporting. No one wants to get energy data from 10 different sources, all in different units, formats, and terminologies.
  • Built-in notifications and workflows
    Also, look for built-in reminders, notifications, and escalations to ensure the inputs are completed in a timely manner, and if deadlines are missed, you know exactly what is missing and who to contact.
Multiple data sources

Data can come from multiple sources, and your sustainability software should be able to handle them all—then consolidate this data into a single source of truth.

2. Make sure the software application includes quality assurance and third-party review tools

Any decent software can make data collection easy, but to truly improve your company’s sustainability initiatives, it must also have tools for quality assurance reviewers and third-party verifiers to easily review the information, track the reported values to source data, and understand how the data were processed.  Ultimately, the software also needs to allow the reporter to easily make updates or corrections as needed.  Because these data are reported to regulators or shareholders, accuracy is paramount.

Look for the following features to support transparency and auditing:

  • Visible and accessible calculations
    All embedded rules, queries, and calculations should be visible and traceable to anyone reviewing so they can check the calculations and raise a flag if issues are found.

    EPA equations

    Your sustainability software should make it easy to see and understand the formulas that produced any calculated data values.

  • Accessible and auditable source data and final values
    All source data and final reported values should be visible, traceable, and tracked. Watch out for “black box” calculations that will confound auditors and cost you in labor hours while you are determining how the reported value was obtained, what the data inputs were, and where the source data originated.
  • Complete audit trails
    Ensure audit trails are present for any changes in key data. You should be able to find out exactly who entered a value or who changed it. Be sure the software is keeping track and that everything is recorded and traceable to ensure the integrity of the process and reports. Good software will have an audit tool that tracks who did what, who is responsible for which datasets, and who changed which values and how.

3. Make sure the software includes tools for reporting to multiple regulatory or voluntary bodies

Many companies report to various regulatory or voluntary bodies, and the software you select should support all the major reporting requirements to avoid the need for separate calculations for some jurisdictions.

  • Enter once, report 10x
    Look for the concept of “enter once, report many times” when reviewing software applications. The gold standard is the capability for reporting methodologies and calculations configured for reporting to multiple agencies from a single dataset, all in a single tool.
  • Check support for your actual, specific needs
    Review your reporting requirements to see if the software handles them. Key reporting requirements include state or federal regulations, internal corporate social responsibility (CSR) and other sustainability reporting, the Carbon Disclosure Project (CDP), Global Reporting Initiative (GRI), and The Climate Registry (TCR).
  • Consider export formats
    Ensure the software includes exports to XML, which is a common format for EPA and ARB reporting, and an option for reporting to other agencies. Having such outputs easily generated from the software will save time and money during the reporting season.
Regulatory formats

Find out what formats you need for regulatory reporting, and make sure your software supports exporting in these formats.

4. Look for data verification flags so you don’t spend time fixing obviously bad data

If you normally report 500 metric tons of GHG per year and you are finding entries of 500,000 metric tons per year in your data, chances are, it’s just simple data entry errors.  However, no one wants to track these down months after the data entry event.  Look for software that will flag these anomalies on entry and force the user to fix them before you ever get to the data review step.

  • Ability to set validation rules
    Look for software that allows you to set rules to flag data entries that fall outside of expected thresholds, catching errors before they make it to QA personnel or auditors.
  • Options to specify acceptable ranges and add comments for unusual values
    Look for features that will help you avoid last-minute questions about the validity of your data. Look for the ability to specify an outlier range to flag values so that you can address them immediately before the report is due. Allow for the opportunity to enter a comment right alongside the flagged value, providing a record that the value was double-checked and is correct for a specified reason.

    Fuel warnings

    Immediate, inline alerts about outlier data values help prevent last-minute surprises.

5. Look for user-defined workflows to help you and your users step through sustainability reporting and tracking process

The sustainability software you select should help simplify data entry and reporting by supporting your preferred workflows.  Software with configurable workflows can be a huge help for both data entry personnel and managers reviewing data, by making the status of all data entry and reporting business processes abundantly clear.

  • Options for lockdown after manager review
    Look for the ability to include manager overrides to data entry and workflows that will lock the data entries to editing once reviewed. This will help ensure others are not modifying data while you are in the report preparation process.

    Edit workflows

    Options for managers to lock down data are important for preventing edits to data that is being prepared for reporting.

  • Quickly identify current workflow status
    Check for easy visual indicators of workflow status to ensure the process is on track to be completed by the reporting deadline.

    Workflow status

    There should be an easy way to see the current workflow status of any data in your system.

  • Easily modify workflow along the way
    Also look for the ability to easily modify the workflow if your original configuration was not optimal. Not everyone knows the best workflow for new software when they initially start using it.  The ability to modify the workflows—without needing a software developer—is an important feature to consider when choosing a sustainability software solution.

6. Look for robust audit trails to help solve “whodunit” issues

All software that handles critical or regulatory data should provide auditing on key data fields.  Find out the details of what is audited and how you will be able to access the audit information.

  • Full history of all changes
    Software should retain a history of values with every report change.
  • Who, when, what
    Look for a complete audit trail of who did what, and what was changed, and when. Tracking any modifications to values supports a rigorous audit and is sure to make your QC staff really happy.

    Workflow history

    Your software should be automatically recording a history of all changes at each step of your workflow.

7. Look at out-of-the-box data outputs—but also consider how easy (or hard) it will be to create specific reports for your corporate needs

Every software has built-in report and dashboards, but they may not meet all your needs out-of-the-box.  Assume some reports will need to be configured, and review the software accordingly.

  • Tracking specific KPIs
    Does the software provide an easy way to track year-to-year KPIs for internal evaluation or for preparation of public-facing sustainability reports?
  • Consider future reporting and visualization needs
    If you need a new report, chart, or other visualization of your data, will this request incur a custom software development charge, or is it an easy configuration?
  • Adapt dashboards to your needs
    Can you easily customize the software’s default dashboards?

    GHG emissions dashboard

    Look for options to easily configure reports, charts, and other visualizations that help you easily review summaries of your data.

8. Make sure the software has a robust notification engine

Software can shoulder the burden of getting people to do what they are supposed to do (reminders), alerting people to when an action is needed (notifications), sharing information (messaging) and sending them information (report notifications).  Be sure to review the strength of all notification features of the software, as this can be a huge help during reporting season—and it can lighten the burden on your inbox as well.

  • Multi-purpose notifications
    Look for routine workflow notifications to ensure you are notified when a workflow step is completed AND if a workflow step is ignored beyond the due date.
  • Actionable notifications
    Look for reporting notifications that will send the link (URL) to applicable users so they can quickly jump to the information in the software. No one likes knowing a report is ready, but then having to log in and search for it.
  • Group and individual notifications
    Ensure you can send notifications by individual user OR to user groups. It can be very tedious to select large numbers of individuals for routine notifications—it is much easier to select “all Facility XYZ EHS staff”.
  • Decide where to receive notifications
    Consider in-app messaging to keep important information in front of the users and spare their inbox.

Robust notification engine

Final thoughts: Imagine what implementation success looks like

While you are evaluating software options, use these points as a guide to make sure you choose a solution that will truly make a difference for your organization’s sustainability initiatives and reporting goals.

As more sustainability software solutions appear in the marketplace, it can be difficult for a company to discern which features really matter for its workflow.  Try a simple exercise—imagine what a perfect sustainability management business process would look like if you found the perfect software solution.  Consider the challenges you face now, and what it would look like if those problems were handled by your software.

Then, ask how well the sustainability software you’re considering will make this dream a reality.  The right software selection can help reduce operational risk, fulfill regulatory reporting requirements in less time and with less effort, and provide safeguards against bad data and missed deadlines.  All you have to do is ask the right questions.

The complete guide to evaluating EHS software

Get more tips for what to look for when evaluating EHS&S software!

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When it comes to EHS&S, the “&S” shouldn’t be an afterthought

Locus Technologies is proud to have attended this year’s NAEM EHS&S Forum in Toronto. We were represented by Wes Hawthorne, President of Locus, and forum first-timer, Aaron Edwards, Marketing Associate at Locus.

NAEM-Forum-booth-picture-2019

The forum gave us the opportunity to learn, both from our peers in discussions about EHS&S goals, and from the diverse lineup of respected speakers and presenters. You spoke and we listened. This year, the prevailing topic of discussion was the growth of expectation surrounding sustainability in organizations.

Sustainability initiatives are rapidly moving to the forefront of institutional policy at leading organizations. As consumers, investors, and shareholders are increasingly supporting more sustainable organizations, executives are expecting more impactful sustainability initiatives from their EHS&S departments. Not only that, but executives inherently expect sustainability initiatives to positively affect the bottom line. This means that today’s EHS&S professionals have to manage sustainability initiatives that are vital to company success as well as regulatory management and reporting, often with limited resources.

Our conversations at the NAEM Forum often revolved around the time-consuming nature of regulatory compliance interfering with the escalated focus on sustainability. Many of the professionals we spoke with are dealing with multiple EHS&S platforms, each used for a specific function. Time management is increasingly more essential to EHS&S managers, and juggling between uni-tasked platforms is detrimental to effective sustainability efforts.

Locus developers have designed our software to reduce the labor-intensiveness of regulatory compliance. We offer a configurable single-platform solution for decreasing the amount of time you spend managing KPIs. From available modules in waste management, audit tracking, GHG reporting, and more⁠—our configurable software allows more time to improve your company’s sustainability initiatives.

Sustainability is no longer an afterthought in the eyes of executives, consumers, investors, or shareholders. Having one robust software platform can greatly help EHS professionals integrate that “&S” seamlessly with their other responsibilities.

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EPA to set tougher requirements for lead in water

The Environmental Protection Agency (EPA) announced that it would impose stricter requirements on water utilities to manage lead and copper contamination in drinking water supplies. The EPA said that tackling water pollution is a core duty of the agency.

The proposed changes, the first affecting lead level in water since 1991, would also give utilities more time to replace lead pipes in their systems. Some environmental groups are not happy with the proposed rule because the change slows by 20 years the timeline for removing aging lead service pipes that could expose children to lead. Lead is a toxin known to harm developing brains. The rule slows down the removal of pipelines where lead levels exceed 15 μg/L to 33 years from the 13 years in the original law.

The new rule requires water utilities to identify and remove sources of lead when a water sample at faucet exceeds 15 micrograms per liter (μg/L). The EPA said water systems would also have to follow new, improved sampling procedures and adjust sampling sites to better target locations with higher lead levels.

Health advocates estimate that as many as six million or more lead water lines remain underground in U.S. cities and towns. Additional sampling and monitoring can help to identify affected areas, and ensure the quality of drinking water sources.

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Predicting Water Quality with Machine Learning

At Locus Technologies, we’re always looking for innovative ways to help water users better utilize their data. One way we can do that is with powerful technologies such as machine learning. Machine learning is a powerful tool which can be very useful when analyzing environmental data, including water quality, and can form a backbone for competent AI systems which help manage and monitor water. When done correctly, it can even predict the quality of a water system going forward in time. Such a versatile method is a huge asset when analyzing data on the quality of water.

To explore machine learning in water a little bit, we are going to use some groundwater data collected from Locus EIM, which can be loaded into Locus Platform with our API. Using this data, which includes various measurements on water quality, such as turbidity, we will build a model to estimate the pH of the water source from various other parameters, to an error of about 1 pH point. For the purpose of this post, we will be building the model in Python, utilizing a Jupyter Notebook environment.

When building a machine learning model, the first thing you need to do is get to know your data a bit. In this case, our EIM water data has 16,114 separate measurements. Plus, each of these measurements has a lot of info, including the Site ID, Location ID, the Field Parameter measured, the Measurement Date and Time, the Field Measurement itself, the Measurement Units, Field Sample ID and Comments, and the Latitude and Longitude. So, we need to do some janitorial work on our data. We can get rid of some columns we don’t need and separate the field measurements based on which specific parameter they measure and the time they were taken. Now, we have a datasheet with the columns Location ID, Year, Measurement Date, Measurement Time, Casing Volume, Dissolved Oxygen, Flow, Oxidation-Reduction Potential, pH, Specific Conductance, Temperature, and Turbidity, where the last eight are the parameters which had been measured. A small section of it is below.

Locus Machine Learning - Data

Alright, now our data is better organized, and we can move over to Jupyter Notebook. But we still need to do a bit more maintenance. By looking at the specifics of our data set, we can see one major problem immediately. As shown in the picture below, the Casing Volume parameter has only 6 values. Since so much is missing, this parameter is useless for prediction, and we’ll remove it from the set.

Locus Machine Learning - Data

We can check the set and see that some of our measurements have missing data. In fact, 261 of them have no data for pH. To train a model, we need data which has a result for our target, so these rows must be thrown out. Then, our dataset will have a value for pH in every row, but might still have missing values in the other columns. We can deal with these missing values in a number of ways, and it might be worth it to drop columns which are missing too much, like we did with Casing Volume. Luckily, none of our other parameters are, so for this example I filled in empty spaces in the other columns with the average of the other measurements. However, if you do this, it is necessary that you eliminate any major outliers which might skew this average.

Once your data is usable, then it is time to start building a model! You can start off by creating some helpful graphs, such as a correlation matrix, which can show the relationships between parameters.

Locus Machine Learning - Corr

For this example, we will build our model with the library Keras. Once the features and targets have been chosen, we can construct a model with code such as this:

Locus Machine Learning - Construct

This code will create a sequential deep learning model with 4 layers. The first three all have 64 nodes, and of them, the initial two use a rectified linear unit activation function, while the third uses a sigmoid activation function. The fourth layer has a single node and serves as the output.

Our model must be trained on the data, which is usually split into training and test sets. In this case, we will put 80% of the data into the training set and 20% into the test set. From the training set, 20% will be used as a validation subset. Then, our model examines the datapoints and the corresponding pH values and develops a solution with a fit. With Keras, you can save a history of the reduction in error throughout the fit for plotting, which can be useful when analyzing results. We can see that for our model, the training error gradually decreases as it learns a relationship between the parameters.

Locus Machine Learning - Construct

The end result is a trained model which has been tested on the test set and resulted in a certain error. When we ran the code, the test set error value was 1.11. As we are predicting pH, a full point of error could be fairly large, but the precision required of any model will depend on the situation. This error could be improved through modifying the model itself, for example by adjusting the learning rate or restructuring layers.

Locus Machine Learning - Error

You can also graph the true target values with the model’s predictions, which can help when analyzing where the model can be improved. In our case, pH values in the middle of the range seem fairly accurate, but towards the higher values they become more unreliable.

Locus Machine Learning - Predict

So what do we do now that we have this model? In a sense, what is the point of machine learning? Well, one of the major strengths of this technology is the predictive capabilities it has. Say that we later acquire some data on a water source without information on the pH value. As long as the rest of the data is intact, we can predict what that value should be. Machine learning can also be incorporated into examination of things such as time series, to forecast a trend of predictions. Overall, machine learning is a very important part of data analytics and the development of powerful AI systems, and its importance will only increase in the future.

What’s next?

As the technology around machine learning and artificial intelligence evolves, Locus will be working to integrate these tools into our EHS software. More accurate predictions will lead to more insightful data, empowering our customers to make better business decisions.

Contact us today to learn how machine learning and AI can help your EHS program thrive

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    Infographic: 6 Benefits of EHS on AWS

    In this infographic, we have outlined a few of the ways EHS programs benefit from having an AWS-hosted solution. Locus customers recently received these benefits as a result of moving our entire infrastructure to Amazon Web Services—the world’s leading cloud. Learn more about the move to AWS.

    Infographic: 6 Benefits of EHS on AWS

    Contact us to learn more about these benefits

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      Artificial Intelligence and Environmental Compliance–Revisited–Part 4: AI, Big Data + Multi-Tenancy = The Perfect System

      AI and Big Data to Drive EHS Decisions via Multi-tenant SaaS

      With data and information streaming from devices like fire hydrants, there is little benefit from raw data, unless a company owning the data has a way to integrate the data into its record system and pair it with regulatory databases and GIS. That is where the advancement in SaaS tools and data sources mashups has helped set the stage for AI as a growing need.

      Humans are not very good at analyzing large datasets. This is particularly true with data at the planetary level that are now growing exponentially to understand causes and fight climate change. Faced with a proliferation of new regulations and pressure to make their companies “sustainable” EHS departments keep adding more and more compliance officers, managers, and outside consultants, instead of investing in technology that can help them. Soon, they will be turning to AI technology to stay on top of the ever-changing regulatory landscape. 

      Locus - Big Data - IoT - AI

      AI, in addition to being faster and more accurate, should make compliance easier. Companies spend too much time and effort on the comprehensive quarterly or annual reporting—only to have to duplicate the work for the next reporting period. The integrated approach, aided by AI, will automate these repetitive tasks and make it easier than just having separate analyses performed on every silo of information before having a conversation with regulators.

      In summary, whether it is being used to help with GHG emissions monitoring and reporting, water quality management, waste management, incident management, or other general compliance functions, AI can improve efficiency, weed out false-positive results, cut costs and make better use of managers’ time and company resources.

      Complex data - Data redundancy

      Another advantage of AI, assuming it is deployed properly, concerns its inherent neutrality on data evaluation and decision making. Time and time again we read in the papers about psychological studies and surveys that show people on opposite sides of a question or topic cannot even agree on the “facts.” It should not be surprising then to find that EHS managers and engineers are often limited by their biases. As noted in the recent best-seller book by Nobel Memorial Prize in Economics laureate Daniel Kahneman, “Thinking, Fast and Slow,” when making decisions, they frequently see what they want, ignore probabilities, and minimize risks that uproot their hopes. Even worse, they are often confident even when they are wrong. Algorithms with AI built-in are more likely to detect our errors than we are. AI-driven intelligent databases are now becoming powerful enough to help us reduce human biases from our decision-making. For that reason, large datasets, applied analytics, and advanced charting and data visualization tools, will soon be driving daily EHS decisions.

      In the past, companies almost exclusively relied upon on-premise software (or single-tenant cloud software, which is not much different from on-premise). Barriers were strewn everywhere. Legacy systems did not talk to one another, as few of the systems interfaced with one another. Getting data into third-party apps usually required the information to be first exported in a prescribed format, then imported to a third-party app for further processing and analysis. Sometimes data was duplicated across multiple systems and apps to avoid the headache of moving data from one to another.  As the world moves to the multi-tenant SaaS cloud, all this is now changing. Customers are now being given the opportunity to analyze not just their company’s data, but data from other companies and different but potentially related and coupled categories via mashups. As customers are doing so, interesting patterns are beginning to emerge.

      The explosion of content—especially unstructured content—is an opportunity and an obstacle for every business today.

      The emergence of artificial intelligence is a game-changer for enterprise EHS and content management because it can deliver business insights at scale and make EHS compliance more productive. There are numerous advantages when you combine the leading multi-tenant EHS software with AI:

      • Ability to handle the explosion of unstructured content where legacy on-premise EHS solutions can’t.
      • AI can organize, illuminate, and extract valuable business insights if all your content is managed in one secure location in the cloud.
      • Locus helps you take advantage of best-of-breed AI technologies from industry leaders and apply them to all your content.

      We are seeing in the most recent NAEM white paper, Why Companies Replace Their EHS&S Software Systems, that people want the ability to integrate with other systems as a top priority.  Once the ability to share/consolidate data is available, AI is not far behind in the next generation of EHS/Water Quality software.

      This concludes the four-part blog series on Big Data, IoT, AI, and multi-tenancy. We look forward to feedback on our ideas and are interested in hearing where others see the future of AI in EHS software – contact us for more discussion or ideas! Read the full Series: Part One, Part Two, Part Three.

      Contact us to learn more about Locus uses IoT and AI

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        Locus Technologies goes all-in on AWS

        SAN FRANCISCO, Calif., 23 July 2019 — Locus Technologies (Locus), the market leader in multi-tenant SaaS water quality, environmental compliance, and sustainability management, today announced that it is going all-in on Amazon Web Services, Inc. (AWS), moving its entire infrastructure to the world’s leading cloud. By moving its flagship product EIM (Environmental Information Management) to AWS this month, Locus will complete its transition to AWS. Locus previously moved its Locus Platform (LP) to AWS in 2018.

        EIM is the world’s leading water quality management software used by many Fortune 500 companies, water utilities, and the US Government since 1999. Among its many features, EIM delivers real-time tools to ensure that water utilities deliver clean water to consumers’ taps and don’t discharge contaminated wastewater above allowable limits to groundwater or surface water bodies like streams, lakes, or oceans.

        EIM generates big data, and with over 500 million analytical records at over 1.3 million locations worldwide, it is one of the largest centralized, multi-tenant water quality management SaaS systems in the world. With anticipated growth in double digits stemming from the addition of streaming data from sensors and many IoT monitoring devices, Locus needed to have a highly scalable architecture for its software hosting. The unmatched performance and scalability of AWS’s offerings are just the right match for powering Locus’ SaaS.

        Because of the scope of its applications, Locus is expecting to leverage the breadth and depth of AWS’s services (including its database systems, serverless architecture, IoT streaming, blockchain, machine learning, and analytics) to automate and enhance the on-demand EHS compliance, sustainability, facility, water, energy, and GHG management tools that Locus’ software provides to its customers.

        Running on AWS’s fault-tolerant and highly performant infrastructure will help support Locus’s everyday business, and will scale easily for peak periods, where reporting demand such as GHG calculation engine or significant emissions incidents like spills can skyrocket scalability demand.

        By leveraging Amazon CloudFront, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Kubernetes Service (Amazon EKS) and AWS Lambda, Locus is migrating to a microservices architecture to create more than 150 microservices that independently scale workloads while reducing complexity in the cloud, thereby enhancing every element of the customer cloud experience. Locus built a data lake on Amazon Simple Storage Service (Amazon S3) and will leverage Amazon Redshift to analyze the vast amount of data it is storing in the cloud, delivering insights and predictive analytics that uncover chemicals trending patterns and predict future emissions releases at various locations.

        Locus intends to leverage AWS IoT services and Amazon Managed Blockchain by building a new native integration to help businesses generate value from the millions of events generated by connected devices such as real-time environmental monitoring sensors and environmental treatment systems controls. AWS IoT is a set of cloud services that let connected devices easily and securely interact with cloud applications like EIM and Locus Platform and other devices. Locus IoT Cloud on AWS allows customers to experience real-time emissions monitoring and management across all their connected sensors and devices. And for customers who want to allow multiple parties to transact (e.g. GHG trading) without a trusted central authority, AWS provides a fully managed, scalable blockchain service. Amazon Managed Blockchain is a fully managed service makes it easy to setup, deploy, and manage scalable blockchain networks that Locus intends to use for emissions management and trading.

        For example, a water utility company that maintains thousands of IoT-enabled sensors for water flow, pressure, pH, or other water quality measuring devices across their dispersed facilities and pipeline networks will be able to use Locus IoT on AWS to ingest and manage the data generated by those sensors and devices, and interpret it in real time. By combining water sensor data with regulatory databases, water utility companies will be able to automatically create an emergency shutdown if chemical or other exceedances or device faults are detected and as such, will be better prepared to serve their customers and environment.

        By combining the powerful, actionable intelligence in EIM and rapid responsiveness through Locus Platform with the scalability and fast-query performance of AWS, customers will be able to analyze large datasets seamlessly on arrival in real time. This will allow Locus’ customers to explore information quickly, find insights, and take actions from a greater variety and volume of data—all without investing the significant time and resources required to administer a self-managed on-premises data warehouse.

        “After 22 years in business, and after evaluating AWS for a year with our Locus Platform, we decided to switch and continue all our business on AWS. We are taking advantage of their extensive computing power, depth and breadth of services and expertise to develop an effective cloud infrastructure to support our growing business and goal of saving the planet Earth by providing and managing factual information on emissions management, all the while reducing operational costs of Locus’ customers,” said Neno Duplan CEO of Locus. “By operating on AWS, we can scale and innovate quickly to provide new features and improvements to our services – such as blockchain-based emissions management – and deliver exceptional scalability for our enterprise customers. With AWS, we don’t have to focus on the undifferentiated heavy lifting of managing our infrastructure, and can concentrate instead on developing and improving apps and services.”

        “By organizing and analyzing environmental, sustainability, and water quality information in the cloud, Locus is helping organizations to understand the impact of climate change on drinking water,” said Mike Clayville, Vice President, Worldwide Commercial Sales at AWS. “AWS’s unmatched portfolio of cloud services, proven operational expertise, and unmatched reliability will help Locus to further automate environmental compliance for companies ranging from local water utilities to multinational manufacturing corporations, to federal government research agencies. ”By choosing to go all-in on AWS, Locus is able to innovate and expand globally, developing new solutions that will leverage comprehensive analytics and machine learning services to gain deeper insights and forecast sustainability metrics that will help deliver clean drinking water to consumers around the world.”

        Read on GlobeNewswire

        Artificial Intelligence and Environmental Compliance–Revisited–Part 3: Multi-Tenancy and AI

        SaaS–Multi-Tenant Cloud Architecture

        Multi-tenancy offers distinct benefits over traditional, single-tenant software hosting. A multi-tenant SaaS provider’s resources are focused on maintaining a single, current version of the application, rather than having its resources diluted in an attempt to support multiple software versions for its customers. If a provider is not using multi-tenancy, it may be hosting or supporting thousands of single-tenant customer implementations. By doing so, a provider cannot aggregate information across customers and extract knowledge from large data sets as every customer may be housed on a different server and possibly a different version of software. For these reasons, it is almost impossible and prohibitively expensive to deliver modern AI tools via single-tenancy.

        Locus Multi-Tenant Software
        View Infographic | Download White Paper

        Multi-tenancy has other advantages as well. Because every customer is on the same version of the software and the same instance, machine-learning (a prerequisite for building an AI system) can happen more quickly as large datasets are constantly fed into a single system. A multi-tenant SaaS vendor can integrate and deploy new AI features more quickly, more frequently, and to all customers at once. Lastly, a single software version creates more of a sense of community among users and facilitates the customers’ ability to share their lessons learned with one another (if they chose to do that). Most of today’s vendors in the EH&S software space cannot offer AI, sustain their businesses, and grow unless they are a true multi-tenant SaaS provider. Very few vendors are.

        AI

        Almost 30 years after the publication of our paper on the hazardous data explosion, SaaS technologies combined with other advancements in big data processing are rising to the challenge of successful processing, analyzing, and interpreting large quantities of environmental and sustainability data. It is finally time to stop saying that AI is a promising technology of the future. A recent Gartner study indicates that about 20 percent of data will be created or gathered by computers by 2018. Six billion connected devices will acquire the ability to connect and share data with each other. This alone will fuel AI growth as we humans cannot interpret such massive amounts of data.

        Gone are the days where EHS software was just a database. There are two factors that are fueling the adoption of AI technologies for water quality management and  EHS compliance. First, there is a vast increase that we have mentioned of data that needs sorting and understanding (big data). Second, there is the move to true multi-tenant SaaS solutions, which enables the intake and dissection of data from multiple digital sources (streaming data) from multiple customers, all in real-time.

        AI has entered the mainstream with the backing and advocacy of companies like IBM, Google, and Salesforce, who are heavily investing in the technology and generating lots of buzzes (and we are seeing the consequent talent war happening industry-wide). It is remarkable to observe how quickly AI is proliferating in so many verticals, as CBS’s 60 Minutes segment showed us.

        For our purposes, let’s look at where AI is likely to be applied in the EHS space. The mission-critical problem for EHS enterprise software companies is finding solutions that both enhance compliance and reduce manual labor and costs. This is where AI will play a major role. So far, companies have largely focused on aggregating their data in a record system(s); they have done little to interpret that data without human interaction. To address the ever-changing growth in environmental regulations, companies have been throwing people at the problem, but that is not sustainable.

        Locus Artificial Intelligence

        AI and natural language processing (NLP) systems have matured enough to read through the legalese of regulations, couple them with company’s monitoring and emissions data, and generate suggestions for actions based on relevant regulations and data. Take, for example; a CEMS installed at many plants to monitor air emissions in real-time. Alternatively, a drinking water supply system monitoring for water quality. In each of these systems, there are too many transactions taking place to monitor manually to ascertain which ones are compliant and which ones are not? I see no reason why similar algorithms that are used for computerized trading (as described in the recent best-seller “Flash Boys”) to trade stocks in fractions of a second cannot be used for monitoring exceedances and automatically shutting down discharges if there is an approaching possibility of emission exceedance. It is an onerous task to figure out every exceedance on a case-by-case basis. Intelligent databases with a built-in AI layer can interpret data on arrival and signal when emissions exceed prescribed limits or when other things go wrong. The main driver behind applying AI to EHS compliance is to lower costs and increase the quality of EHS compliance, data management, and interpretation, and ultimately, to avoid all fines for exceedances.

        For example, a large water utility company has to wade through thousands of analytical results to look for outliers of a few dozen chemicals they are required to monitor to stay compliant. Some of these may be false-positives, but that still leaves some results to be investigated for outliers. Each of those investigations can take time. However, if a software algorithm has access to analytical results and can determine that the problem rests with a test in the lab, that problem can be solved quickly, almost without human interaction. That is powerful.

        Combing through data and doing this by hand or via spreadsheet could take days and create a colossal waste of time and uncertainty. Hundreds of billable hours can be wasted with no guaranteed result. Using AI-driven SaaS software to determine what outliers need investigation allows compliance managers, engineers, and chemists to focus their expertise on just these cases and thus avoid wasting their time on the remaining ones that the AI engine indicates need no further examination.

        Predictive analytics based on big data and AI will also make customer data (legacy and new) work harder for customers than any team(s) of consultants. A good analogy that came to me after watching 60 minutes is that the same way the clinical center in North Carolina used AI to improve cancer treatment for their patients, engineers and geologists can improve on selecting the site remedy that will be optimized for given site conditions and will lead to a faster and less expensive cleanup with minimum long-term monitoring requirements.

        A final example where AI will be playing a role is in the area of enterprise carbon management. SaaS software is capable of integrating data from multiple sources, analyzing and aggregating it. This aggregated information can then be distributed to a company’s divisions or regulatory agencies for final reporting and validation/verification, all in real-time. This approach can save companies lots of time and resources. Companies will be able to access information from thousands of emission sources across the states, provinces, and even countries where their plants are located. Because each plant is likely to have its set of regulatory drivers and reporting requirements, these would have to be incorporated into the calculation and reporting engine. After data from each plant is uploaded to a central processing facility, the information would be translated into a “common language,” the correct calculation formulae and reporting requirements applied, and the results then returned to each division in a format suitable for reporting internally and externally.

        Blockchain for EHS—Looking ahead

        And finally, another emerging technology, blockchain, will further augment the power of AI for EHS monitoring and compliance. While blockchain is in its infancy, its decentralized approach coupled with AI will bring another revolution to EHS compliance and water monitoring.

        Blockchain technology

        Parts one, two, and four of this blog series complete the overview of Big Data, IoT, AI, and multi-tenancy. We look forward to feedback on our ideas and are interested in hearing where others see the future of AI in EHS software – contact us for more discussion or ideas!

        Contact us to learn more about Locus uses IoT and AI

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          Stories and conversations from this summer’s radiological workshop

          Locus recently joined the nuclear power plant community in Orlando, FL for this year’s Radiological Effluents and Environmental Workshop. It’s always a pleasure to join other professionals in a space that encourages discussion, education, and awareness of industry processes and compliance.

          Locus Technologies at the NEI Radiological Conference, Orlando, 2019

          Bill Donaldson and Danny Moore of Locus Technologies.

          Each conference we attend is an opportunity to learn. Whether talking with current or potential customers, it’s always fascinating to hear some of the success and horror stories experienced in their daily operations. We’ve summarized a few of those conversations below. And since REEW takes place in the summer, there’s a theme.

          Locus SaaS does not have version numbers.Version Island

          Imagine you’ve spent years utilizing a certain feature of your radiological software. You’ve gone through the training process, the growing pains, and you are finally enjoying the fruits of that labor. Now imagine learning that the latest and greatest version being released has removed the feature that you’ve grown to rely on. You are now stuck on a version island. At this point, a costly and time consuming upgrade will cause more problems. Locus SaaS has no version numbers, meaning you will never need to upgrade.

          Ditch Excel and go off the gridOff the Grid

          At one time, the simple columns and rows in Excel seemed to provide a sufficient solution to prepare your REMP sampling data for reports. However, when you need to transfer data between systems, or create more sophisticated reports, those grids begin to feel like prison bars. Maybe it’s time to go off the grid and deploy a more modern solution that can connect and work side by side with your existing tools.

          Locus helps with decommissioningSunsetting (Decommissioning)

          Closing a power plant is a long and involved process that many attendees were in the process of dealing with or will be in the near future. This operational change can be the motivation to rethink the way radiological data is sampled and managed. Some software packages can be too big for the job. Locus offers a modular approach where you only pay for what you need. Choosing which system tools are relevant to the type of data sampling and resources available can minimize implementation cost and increase productivity.

          Locus' cloud securitySerene Security

          Many people we spoke with at REEW had serious security concerns. Locus takes those concerns seriously. We are SOC 1 and SOC 2 certified and have migrated our software to Amazon Web Services. All customer data is stored with AWS, one of the most advanced and secure cloud-hosting providers on the planet. Locus provides the ability to control user permissions, customizing access based on job duties. This provides a more granular approach to data security.

          Locus sample planningMaking Plans

          Using a sample planning application organizes sample events and allows for scheduling weeks, months, or years in advance. Many were interested in this powerful tool that is flexible enough to adapt when a reactor changes modes, allowing for one-time, ad-hoc samples. Mobile applications that integrate with planned samples and events minimize setup, ease data collection, speed up loading field data, and can expedite samples to the lab more efficiently.

          Your feedback has helped Locus build a solution that makes it easy to manage all your facility’s data for RETS/REMP, helping you meet your NRC reporting requirements. We enjoyed speaking with everyone at REEW and we look forward to seeing you again next year!

          [sc_button link=”https://www.locustec.com/applications/industry/nuclear/” text=”Learn more about Locus for Nuclear” link_target=”_self” background_color=”#52a6ea” centered=”1″ separator_style=”double”]

          About the author—Danny Moore, Locus Technologies

          Danny Moore, Marketing Manager, Locus Technologies

          Mr. Moore has spent the last decade designing and marketing for enterprise SaaS systems. In his career at Locus, he leads a team of marketing professionals in branding, content creation, social media engagement, and email outreach. Mr. Moore enjoys attending conferences as a Locus brand ambassador and sharing any feedback gained to improve product development.

          About the author—Bill Donaldson, Locus Technologies

          Bill Donaldson, Locus Technologies

          Mr. Donaldson has 5 years experience in SaaS systems, performing Product Management and QA/QC of Locus Mobile iOS application and Locus’ Environmental Information Management system (EIM). While completing his B.S., Mr. Donaldson held several paid internships, where he configured a Relational GeoDatabase and a Database Management System (DBMS), for biological data entry.

          Does your EHS software have a version number?

          Freedom from product release tyranny

          I love the article by Geoffrey Moore on the power of software as a service (SaaS) business model published on LinkedIn. In SaaS’s Real Triumph he writes: “by far the greatest contribution of SaaS is to free the enterprise from the tyranny of the product release model.”

          He cites the operational burden, enterprise-wide distraction and associated cost to roll out an enterprise software and then the subsequent hesitation to repeat that when a new release of that software becomes available as that deployment model is not sustainable nor affordable. Companies spend big dollars buying and then deploying EHS software that they know will be outdated in just a few years. Only IT personnel benefits from that model as it may extend their employment for a few years before IT department goes out of business for good. Moore points out the painful truth, stating: “you have paid maintenance of 18 to 20% per year for anywhere from five to ten years for the express purpose of not availing yourself of the innovation created during that time period.”

          Probably the main benefit of SaaS multi-tenancy (that is frequently overlooked during the software selection process) is no software versioning. This is because multi-tenant software typically provides a rolling upgrade program: incremental and continuous improvements. It is an entirely new architectural approach to software delivery and maintenance model. Companies have to develop applications from the ground up for multi-tenancy. Legacy client-server or single-tenant software cannot qualify for multi-tenancy. Let’s take a look at definitions:

          No version number

          Single-Tenant – A single instance of the software and supporting infrastructure serves a single customer. With single-tenancy, each customer has his or her own independent database and instance of the software. Essentially, there is no sharing happening with this option.

          Multi-Tenant – Multi-tenancy means that a single instance of the software and its supporting infrastructure serves multiple customers. Each customer shares the software application and also shares a single database. Each tenant’s data is isolated and remains invisible to other tenants.

          Benefits of SaaS Multi-Tenant Architecture

          The multi-tenant architecture provides lower costs through economies of scale: With multi-tenancy, scaling has far fewer infrastructure implications than with a single-tenancy-hosted solution because new customers get access to the same software.

          Shared infrastructure leads to lower costs: SaaS allows companies of all sizes to share infrastructure costs. Not having to provision or manage any infrastructure or software above and beyond internal resources enables businesses to focus on everyday tasks.

          Ongoing maintenance and updates: Customers don’t need to pay costly upgrades to get new features or functionality. 

          Configuration can be done while leaving the underlying codebase unchanged: Single-tenant-hosted solutions are often customized, requiring changes to an application’s code. This customization can be costly and can make upgrades expensive and time-consuming because the upgrade might not be compatible with customers changes to the earlier software version.

          Multi-tenant solutions are designed to be highly configurable so that businesses can make the application perform the way they want. There is no changing the code or data structure, making the upgrade process easy.

          Multi-tenancy ensures that every customer is on the same version of the software. As a result, no customer is left behind when the software is updated to include new features and innovations. A single software version also creates a unique sense of community where customers and partners share knowledge, resources, and learning. Smart managers work with their peers and learn from them and what they are doing. A multi-tenant SaaS provider’s resources are focused on maintaining a single, current (and only) version of the application, rather than spread out in an attempt to support multiple software versions for customers. If a provider isn’t using multi-tenancy, it may be hosting thousands of single-tenant customer implementations. Trying to maintain that is too costly for the vendor, and those costs, sooner or later, become the customers’ costs.

          A vendor who is invested in on-premise, hosted, and hybrid models cannot commit to providing all the benefits of a true SaaS model due to conflicting revenue models. Their resources are going to be spread thin, supporting multiple versions rather than driving innovation. Additionally, if the vendor makes the majority of their revenue selling on-premise software, it is difficult for them to fully commit to a true SaaS solution since the majority of their resources are allocated to supporting the on-premise software.

          Before you engage future vendors for your enterprise EHS software, assuming you already decided to go with SaaS solution, ask these questions:

          1. Does your software have version numbers? 
          2. Do you charge for upgrades and how often do you upgrade?

          If the answer is yes to any of these two questions, you should not consider that vendor as they are not true multi-tenant SaaS. You should not select that vendor if they answer “we are in the process of switching to multi-tenancy.” Multi-tenancy train departed a long time ago, and no EHS vendor who is single-tenant is not going to make that switch in time to make it work.

          And if they suddenly introduce a “multi-tenant” model (after selling an on-premises version for 10+ years) who in the world would want to migrate to that experimental cloud without putting the contract out to bid to explore a switch to well established and market-tested true multi-tenant providers? The first-mover advantage when it comes to multi-tenancy is a huge advantage for any vendor.

          Multi-tenant architecture