Tag Archive for: SaaS

ESG: Why Uptime of SaaS Vendors Matters

In a Software as a Service (SaaS) delivery model, service uptime is vital for several reasons. Besides the obvious of having access to the service over the internet at any given time and staying connected to it 24/7/365, there are additional reasons why service uptime is essential. One of them is quickly verifying the vendor’s software architecture and how it fits the web.

Locus is committed to achieving and maintaining the trust of our customers. Integral to this mission is providing a robust compliance program that carefully considers data protection matters across our cloud services and service uptime. After security, service uptime and multitenancy at Locus come as a standard and, for the last 25 years, have been the three most essential pillars for delivering our cloud software. Our real-time status monitoring (ran by an independent provider of web monitoring services) provides transparency around service availability and performance for Locus’ ESG and EHS compliance SaaS products. Earlier I discussed the importance of multitenancy in detail. In this article, I will cover the importance of service uptime as one measure to determine if the software vendor is running genuine multitenant software or not.

Service Uptime

If your software vendor cannot share uptime statistics across all customers in real-time, they most likely do not run on a multitenant SaaS platform. One of the benefits of SaaS multitenancy (that is frequently overlooked during the customer software selection process) is that all customers are on the same instance and version of the software at all times. For that reason, there is no versioning of software applications. Did you ever see a version number for Google’s or Amazon’s software? Yet they serve millions of users simultaneously and constantly get upgraded. This is because multitenant software typically provides a rolling upgrade program: incremental and continuous improvements. It is an entirely new architectural approach to software delivery and maintenance model that frees customers from the tyranny of frequent and costly upgrades and upsell from greedy vendors. Companies have to develop applications from the ground up for multitenancy, and the good thing is that they cannot fake it. Let’s take a deeper dive into multitenancy.

An actual multitenant software provider can publish its software uptime across all customers in real-time. Locus, for example, has been publishing its service uptime in real-time across all customers since 2009. Locus’s track record speaks for itself: Locus Platform and EIM have a proven 99.9+ percent uptime record for years. To ensure maximum uptime and continuous availability, Locus provides redundant data protection and the most advanced facilities protection available, along with a complete data recovery plan. This is not possible with single-tenant applications as each customer has its software instance and probably a different version. One or a few customers may be down, others up, but one cannot generally aggregate software uptime in any meaningful way. The fastest way to find if the software vendor offers multitenant SaaS or is faking it is to check if they publish online, in real-time, their applications uptime, usually delivered via an independent third party.

Legacy client-server or single-tenant software cannot qualify for multitenancy, nor can it publish vendor’s uptime across all customers. Let’s take a look at definitions:

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

Multitenant – Multitenancy 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.

Locus Multi-Tenant Software

A multitenant SaaS provider’s resources are focused on maintaining a single, current (and only) version of the software platform rather than being spread out in an attempt to support multiple software versions for customers. If a provider isn’t using multitenancy, it may be hosting thousands of single-tenant customer implementations. Trying to maintain that is too costly for the vendor, and sooner or later, those costs become the customers’ costs.

A vendor invested in on-premise, hosted, and hybrid models cannot commit to providing all the benefits of an actual SaaS model due to conflicting revenue models. Their resources will be spread thin, supporting multiple software versions rather than driving SaaS innovation. Additionally, suppose the vendor makes most of their revenue selling on-premise software. In that case, it is difficult for them to fully commit to a proper SaaS solution since most of their resources support the on-premise software. In summary, a vendor is either multitenant or not – there is nothing in between. If they have a single application installed on-premise of customer or single-tenant cloud, they do not qualify to be called multitenant SaaS.

Before you engage future vendors for your enterprise ESG reporting or EHS compliance software, assuming you already decided to go with a SaaS solution, ask this simple question:

Can you share your software uptime across ALL your customers in real-time? If the answer is no, pass.

Multitenancy Explained

And if the vendor suddenly introduces a “multitenant” model (after selling an on-premises or single-tenant software 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 actual multitenant providers? The first-mover advantage of multitenancy is a considerable advantage for any vendor. Still not convinced? Let me offer a simple analogy to drive home the point as to why service uptime and multitenancy matter: Tesla vs. Edison–War of Currents.

Multi-tenant architecture

The War of Currents was a series of events surrounding the introduction of competing electric power transmission systems in the late 1880s and early 1890s that pitted companies against one another and involved a debate over the cost and convenience of electricity generation and distribution systems, electrical safety, and a media/propaganda campaign, with the leading players being the direct current (DC) based on the Thomas Edison Electric Light Company and the supporters of alternating current (AC) based on Nikola Tesla’s inventions backed by Westinghouse.

Tesla and Edison The War of Currents

With electricity supplies in their infancy, much depended on choosing the right technology to power homes and businesses across the country. The Edison-led group argued for DC current that required a power generating station every few city blocks (single-tenant model). In contrast, the AC group advocated for a centralized generation with transmission lines that could move electricity great distances with minimal loss (multitenant model).

The lower cost of AC power distribution and fewer generating stations eventually prevailed. Multitenancy is equivalent to AC regarding cost, convenience, and network effect. You can read more about how this analogy relates to SaaS in the book by Nicholas Carr, “Big Switch.” It’s the best read so far about the significance of the shift to multitenant cloud computing. Unfortunately, the ESG/EHS software industry has lagged in adopting multitenancy.

Given these fundamental differences between different modes of delivering software as a service, it is clear that the future lies with the multitenant model.

Whether all customer data is in one or multiple databases is of no consequence to the customer. For those arguing against it, it is like an assertion that companies “do not want to put all their money into the same bank account as their competitors,” when what those companies are doing is putting their money into different accounts at the same bank.

When customers of a financial institution share what does not need to be partitioned—for example, the transactional logic and the database maintenance tools, security, and physical infrastructure and insurance offered by a major financial institution—then they enjoy advantages of security, capacity, consistency, and reliability that would not be affordably deliverable in isolated parallel systems.

Locus has implemented procedures designed to ensure that customer data is processed only as instructed by the customer throughout the entire chain of processing activities by Locus and its subprocessors. Amazon Web Services, Inc. (“AWS”) provides the infrastructure used by Locus to host or process customer data. Locus hosts its SaaS on AWS using a multitenant architecture designed to segregate and restrict customer data access based on business needs. The architecture provides an effective logical data separation for different customers via customer-specific “Organization IDs” and allows customer and user role-based access privileges. The customer interaction with Locus services is operated in an architecture providing logical data separation for different customers via customer-specific accounts. Additional data segregation ensures separate environments for various functions, especially testing and production.

Multitenancy yields a compelling combination of efficiency and capability in enterprise cloud applications and cloud application platforms without sacrificing flexibility or governance.

Want to learn more? Reach out to our product specialists today.

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    The Horrors of Excel for Data Management

    Locus has been preaching on the pitfalls of Excel for a long time. It’s no surprise that one of the worst imaginable errors in Excel that could’ve happened, did. Almost 16,000 COVID-19 cases in England went unreported because Public Health England hit the maximum row count in their version of Excel.

    This is not the only example of Excel being misused or being the wrong tool entirely for the job. Excel is not in any way a data management system for complex or vital data. When it comes to sustainability reporting and environmental data management, the evils of the grid are a force to be reckoned with. We have highlighted a few examples that will have you shivering.

    Excel Horrors - Evils of Autofill

    Case 1: The Evils of Autofill

    Take a look at this harmless-looking chart. It shows monthly electricity consumption for a facility set to report:

    Month  Monthly Electricity Consumption (MWh) 
    January 2019  133,500 
    February 2019  122,400 
    March 2019  138,900 
    April 2019  141,600 
    May 2019  141,601 
    June 2019  141,602 
    July 2019  141,603 
    August 2019  141,604 
    September 2019  141,605 
    October 2019  141,606 
    November 2019  141,607 
    December 2019  141,608 

    During review, the auditor notices a distinct trend from April to December, indicating false data overwritten by a stray double-click. Eventually, the auditor required re-entering all invoice data for dozens of facilities to correct the issue. Where the original data went and how autofill went astray remains a mystery.

     

    Excel Horrors - Phantom File Editor

    Case 2: The Phantom File Editor

    Imagine using a massive spreadsheet with lots of linked calculations for your annual sustainability report. One of the team engineers works on the file to input more data and get it ready for presentation. But in the final steps, they accidentally delete one of the formulas that sum up the indicators. The annual total looks great for the presentation since you’ve effectively removed a portion of your resource consumption, but afterwards you discover the conclusions were incorrectly calculated.  How did that error get introduced?  The spreadsheet has no auditing capabilities on the individual values, so you may never know.

    Excel supports multiple users editing one document simultaneously, but not well.  Multiple records are saved, edits are lost, and vital data vanishes, or at best is very hard to recover. The Track Changes feature is not infallible, and over reliance on it will cause hardship.

    Excel Horrors - Date of the Dead

    Case 3: Date of the Dead

    Excel has a frustrating insistence of changing CAS numbers into dates, even if they are something like “7440-09-7″ turning into September 7, 7400. If you’re not explicit in your cell formatting, Excel isn’t happy leaving values as they are.

     

    Excel Horrors - Imposter Numbers

    Case 4: Imposter Numerical Values

    You meant to type 1.5, but you typed “1..5” or “.1.5”. Does Excel reject these imposter numbers or let you know of a potential error? No, it’s stored in Text format. This can throw off any averages or sums you may be tracking. This minor identity theft can cause a real headache.

     

     

    Other Significant Cases:

    Other data quality issues with using Excel include, but are not limited to:

    • Locations with multiple variations of the same ID/name (e.g., MW-1, MW-01, MW 1, MW1, etc.)
    • Use of multiple codes for the same entity (e.g., SW and SURFW for surface water samples)
    • Loss of significant figures for numeric data
    • Special characters (such as commas) that may cause cells to break unintentionally over rows when moving data into another application
    • Bogus dates like “November 31” in columns that do not have date formats applied to them
    • Loss of leading zeros associated with cost codes and projects numbers (e.g., “005241”) that have only numbers in them but must be stored as text fields
    • The inability to enforce uniqueness, leading to duplicate entries
    • Null values in key fields (because entries cannot be marked as required)
    • Hidden rows and/or columns that can cause data to be shifted unintentionally or modified erroneously
    • Inconsistent use of lab qualifiers— in some cases, these appear concatenated in the same Excel column (e.g., “10U, <5”) while in other cases they appear in separate columns

    As you can see, the horrors of Excel are common, and terrifying. Without a proper system of record, auditing features, and the ability for data to vanish into the ephemera, Excel offers little in the way of data security and quality for organizations managing vital environmental and compliance data. Many are learning firsthand the superiority of database management systems over spreadsheets when it comes to managing data. Now is the time to examine the specific shortcomings of your current system and consider your options.

    Contact us today to learn how Locus makes complex data management a little less spooky!

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      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. 12 Ways SaaS Can Improve Your Environmental Data

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

      Contact us to learn more about these benefits

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        Why Companies Replace Their EHS&S Software Systems

        A recent NAEM study explored the main reasons EHS&S professionals look to replace their current software configuration. Among the most reported issues were overall performance, customer support, and software customization. The following infographic highlights both why EHS&S professionals are seeking new software, and wheat criteria are most important in shopping for a new software system.

        locus_infographic_why-companies-replace-software-1

        [sc_button link=”/why-locus/” text=”See why others choose Locus” link_target=”_self” color=”#FFFFFF” background_color=”#52a6ea” centered=”1″]

        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

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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.

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            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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              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

               

              Artificial Intelligence and Environmental Compliance–Revisited–Part 2: IoT

              More recently, big data has become more closely tied to IoT-generated streaming datasets such as Continued Air Emission Measurements (CEMS), real-time remote control and monitoring of treatment systems, water quality monitoring instrumentation, wireless sensors, and other types of wearable mobile devices. Add digitized historical records to this data streaming, and you end up with a deluge of data. (To learn more about big data and IoT trends in the EHS industry, please read this article: Keeping the Pulse on the Planet using Big Data.) 

              In the 1989 Hazardous Data Explosion article that I mentioned earlier, we first identified the limitation of relational database technology in interpreting data and the importance that IoT (automation as it was called at the time) and AI were going to play in the EHS industry. We wrote: 

              “It seems unavoidable that new or improved automated data processing techniques will be needed as the hazardous waste industry evolves. Automation (read IoT) can provide tools that help shorten the time it takes to obtain specific test results, extract the most significant findings, produce reports and display information graphically,” 

              IoT - Internet of Things

              We also claimed that “expert systems” (a piece of software programmed using artificial intelligence (AI) techniques. Such systems use databases of expert knowledge to offer advice or make decisions.) and AI could be possible solutions—technologies that have been a long time coming but still have a promising future in the context of big data. 

              “Currently used in other technical fields, expert systems employ methods of artificial intelligence for interpreting and processing large bodies of information.” 

              Although “expert systems” as a backbone for AI did not materialize as it was originally envisioned by researches, it was a necessary step that was needed to use big data to fulfil the purpose of an “expert”. 

              AI can be harnessed in a wide range of EHS compliance activities and situations to contribute to managing environmental impacts and climate change. Some examples of application include AI-infused permit management, AI-based permit interpretation and response to regulatory agencies, precision sampling, predicting natural attenuation of chemicals in water, managing sustainable supply chains, automating environmental monitoring and enforcement, and enhanced sampling and analysis based on real-time weather forecasts. 

              Parts one, three, 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!

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                Artificial Intelligence and Environmental Compliance–Revisited

                On 12 April 2019, Locus’ Founder and CEO, Neno Duplan, received the prestigious Carnegie Mellon 2019 CEE (Civil and Environmental Engineering) Distinguished Alumni Award for outstanding accomplishments at Locus Technologies. In light of this recognition, Locus decided to dig into our blog vault, share a series of visionary blogs crafted by our Founder in 2016. These ideas are as timely and relevant today as they were three years ago, and hearken to his formative years at Carnegie Mellon, which formed the foundation for the current success of Locus Technologies as top innovator in the water and EHS compliance space.

                Artificial Intelligence (AI) for Better EHS Compliance (original blog from 2016)

                It is funny how a single acronym can take you back in time. A few weeks ago when I watched 60 Minutes’ segment on AI (Artificial Intelligence) research conducted at Carnegie Mellon University, I was taken back to the time when I was a graduate student at CMU and a member of the AI research team for geotechnical engineering. Readers who missed this program on October 9, 2016, can access it online.

                Fast forward thirty plus years and AI is finally ready for prime time television and a prominent place among the disruptive technologies that have so shaken our businesses and society. This 60 Minutes story prompted me to review the progress that has occurred in the field of AI technology, why it took so long to come to fruition, and the likely impact it will have in my field of environmental and sustainability management. I discuss these topics below. I also describe the steps that we at Locus have taken to put our customers in the position to capitalize on this exciting (but not that new) technology.

                What I could not have predicted when I was at Carnegie Mellon is that AI was going to take a long time to mature–almost the full span of one’s professional career. The reasons for this are multiple, the main one being that several other technologies were absent or needed to mature before the promises of AI could be realized. These are now in place. Before I dive into AI and its potential impact on the EHS space, let me touch on these “other” major (disruptive) technologies without which AI would not be possible today: SaaS, Big Data, and IoT (Internet of Things).

                Locus Artificial Intelligence

                As standalone technologies, each of these has brought about profound changes in both the corporate and consumer worlds. However, these impacts are small when compared to the impact all three of these will have when combined and interwoven with AI in the years to come. We are only in the earliest stages of the AI computing revolution that has been so long in the coming.

                I have written extensively about SaaS, Big Data, and IoT over the last several decades. All these technologies have been an integral part of Locus’ SaaS offering for many years now, and they have proven their usefulness by rewarding Locus with contracts from major Fortune 500 companies and the US government. Let me quickly review these before I dive into AI (as AI without them is not a commercially viable technology).

                Big Data

                Massive quantities of new information from monitoring devices, sensors, treatment systems controls and monitoring, and customer legacy databases are now pouring into companies EHS departments with few tools to analyze them on arrival. Some of the data is old information that is newly digitized, such as analytical chemistry records, but other information like streaming of monitoring wireless and wired sensor data is entirely new. At this point, most of these data streams are highly balkanized as most companies lack a single system of record to accommodate them. However, that is all about to change.

                As a graduate student at Carnegie Mellon in the early eighties, I was involved with the exciting R&D project of architecting and building the first AI-based Expert System for subsurface site characterization, not an easy task even by today’s standards and technology. AI technology at the time was in its infancy, but we were able to build a prototype system for geotechnical site characterization, to provide advice on data interpretation and on inferring depositional geometry and engineering properties of subsurface geology with a limited amount of data points. The other components of the research included a relational database to store the site data, graphics to produce “alternative stratigraphic images” and network workstations to carry out the numerical and algorithmic processing. All of this transpired before the onset of the internet revolution and before any acronyms like SaaS, AI, or IoT had entered our vocabulary. This early research led to the development of a set of commercial tools and technological improvements and ultimately to the formation of Locus Technologies in 1997.

                Part of this early research included management of big data, which is necessary for any AI undertaking. As a continuation of this work at Carnegie Mellon, Dr. Greg Buckle and I published an article in 1989 about the challenges of managing massive amounts of data generated from testing and long-term monitoring of environmental projects. This was at a time when spreadsheets and paper documents were king, and relational databases were little used for storing environmental data.

                The article, “Hazardous Data Explosion,“ published in the December 1989 issue of the ASCE Civil Engineering Magazine, was among the first of its kind to discuss the upcoming Big Data boom within the environmental space and placed us securely at the forefront of the big data craze. This article was followed by a sequel article in the same magazine in 1992, titled “Taming Environmental Data,“ that described the first prototype solution to managing environmental data using relational database technology. In the intervening years, this prototype eventually became the basis of the industry’s first multi-tenant SaaS system for environmental information management.

                Locus - Big Data - IoT - AI

                Today, the term big data has become a staple across various industries to describe the enormity and complexity of datasets that need to be captured, stored, analyzed, visualized, and reported. Although the concept may have gained public popularity relatively recently, big data has been a formidable fixture in the EHS industry for decades. Initially, big data in EHS space was almost entirely associated with the results of analytical, geotechnical, and field testing of water, groundwater, soil, and air samples in the field and laboratory. Locus’ launch of its Internet-based Environmental Information Management (EIM) system in 1999 was intended to provide companies not only with a repository to store such data, but also with the means to upload such data into the cloud and the tools to analyze, organize, and report on these data.

                In the future, companies that wish to remain competitive will have no choice but bring together their streams of (seemingly) unrelated and often siloed big data into systems such as EIM that allow them to evaluate and assess their environmental data with advanced analytics capabilities. Big data coupled with intelligent databases can offer real-time feedback for EHS compliance managers who can better track and offset company risks. Without the big data revolution, there would be no coming AI revolution.

                AI and Water Management – Looking Ahead

                There has been much talk about how artificial intelligence (AI) will affect various aspects of our lives, but little has been said to date about how the technology can help to make water quality management better. The recent growth in AI spells a big opportunity for water quality management. There is enormous potential for AI to be an essential tool for water management and decoupling water and climate change issues.

                Two disruptive megatrends of digital transformation and decarbonization of economy could come together in the future. AI could make a significant dent in global greenhouse gas (GHG) emissions by merely providing better tools to manage water. The vast majority of energy consumption is wasted on water treatment and movement. AI can help optimize both.

                AI is a collective term for technologies that can sense their environment, think, learn, and take action in response to what they’re detecting and their objectives. Applications can range from automation of routine tasks like sampling and analyses of water samples to augmenting human decision-making and beyond to automation of water treatment systems and discovery – vast amounts of data to spot, and act on patterns, which are beyond our current capabilities.

                Applying AI in water resource prediction, management and monitoring can help to ameliorate the global water crisis by reducing or eliminating waste, as well as lowering costs and lessening environmental impacts.

                Parts two, three, 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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                  Company Email

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                  Locus is committed to preserving your privacy.

                  Tag Archive for: SaaS

                  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.”

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