Tag Archive for: SaaS

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.

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    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

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    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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      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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        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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          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

           

          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 Explosionarticle 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!

          Contact us to learn more about Locus uses IoT and AI

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            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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              The announcements by several EHS software vendors this Fall caught my attention. After offering their software on-premisses for over a decade, suddenly many are discovering and planning to introduce multi-tenant Software-as-a-Service (SaaS) while promising to continue to maintain their current on-premises or single tenant offerings. In essence, they are introducing multi-tenancy as if it were a new version of their software. This plan is not going to work! Let me explain why.

              Most public announcements begin something like this: In the next several years we plan to expand our software offerings and offer our customers the option to move from their current on-premises solution to the cloud. However, is this even possible? What they consider the “cloud” may not be a true multi-tenant cloud. That train departed years ago, and most of the current EHS software vendors missed it. While multi-tenancy has been a game changer in the tech industry, many are uncertain of exactly what makes an application “multi-tenant” or why it matters.

              Locus Multi-Tenant Software

              There is a considerable degree of (intended) confusion in the EHS software space when it comes to the definition of a real cloud or better said, multi-tenancy. Companies that are considering SaaS solutions for EHS software hear all sorts of things from EHS software vendors hoping to tap into the momentum of cloud computing. Many go as far as saying “sure; we can do multi-tenant, single-tenant, whatever tenant you need!” –anything to win the job. These vendors do not understand the real cloud.

              Multi-tenancy is a significant shift in computing and requires an all-new approach to the software architecture and the delivery model from the ground up. It is transformational, and customers who intend to buy the next generation of EHS software should spend the time to understand the differences. More importantly, multi-tenancy is a principle, not a software version or an upgrade. It is not an evolutionary step; instead, it is a revolution in the software delivery model and it matters in the long run for the customer.

              Multi-tenant architecture

              Figure 1: The single-tenant model cannot easily be switched or “upgraded” to multi-tenant. The software architecture does not allow it for easy switch the same way as single family home cannot be “remodeled” to become a multi-tenant highrise. What differentiates multi-tenant application architecture is its effectiveness in achieving the same goal in a scalable and sustainable fashion.

              Can anyone imagine companies like eBay, Salesforce, Google, Workday, or Amazon offering a “single-tenant” solution side by side to their multi-tenant clouds? I argue that any EHS software vendor who offers a single-tenant solution of any type, cannot be a serious contender in multi-tenant SaaS.

              EHS software vendors with on-premise software applications or single-tenant web-enabled offerings are seduced by the seemingly low barriers to entry into the SaaS market with an architecture that leverages virtualization. This approach allows a software company to quickly offer subscription-based services of their legacy product to their initial customers. In the long run, however, this multi-instance approach just won’t scale economically. A recent wave of ownership change of EHS software companies is the best indicator that sold companies became victims of their initial success. A SaaS provider who leverages virtualization puts the long-term viability of the business at risk as more efficient SaaS competitors come to dominate the market.

              Multi-tenant architecture

              Figure 2: Single-tenant requires many more vendor resources. The resource costs are eventually passed to customers. Each upgrade of the application will require each customer to upgrade independently and the ability to implement tenant management tools and tenant-specific customizations is significantly limited. The benefit of multi-tenancy is that instead of 100 copies of the OS, 100 copies of the database, and 100 copies of apps, it has 1 OS, 1 DB and 1 app on the server with significantly less vendor resources required to manage it. And it is those savings that are on a long term passed to customers.

              Multi-tenant architecture

              Figure 3: Multi-tenant model requires less resources and easier (and rolling) upgrades (i.e. no version number necessary). Only one software instance and hardware stack for multiple tenants. All customers are always on the latest version of software. Locus Technologies figured this out in 1999. And they contribute their phenomenal success since then exactly to the multi-tenancy. They could scale up infinitely without adding proportional cost. Others cannot.

              Multi-tenant architecture

              Figure 4: “Can’t we create a separate stack for just this one customer? I promise it’s just this one…” Even a single installation for one “special” customer, breaks the multi-tenant model. Don’t do it.

              I would also add that single-tenant (hybrid) cloud applications are worse than on-premise installment. Why? Because they are fake clouds. In single tenancy, each customer has his or her independent database and instance of the software. These instances may reside on the same or different servers. In this model, a customer is, in fact, outsourcing maintenance of their application (software and hardware) to a vendor (or their consultant) that is not likely equipped to perform these tasks. No single vendor in the EHS software industry is large enough to undertake maintenance of the single-tenant infrastructure on behalf of their customers regardless of how inexpensive hardware or software virtualization may be. Even if they offer their hosting on Microsoft Azure Cloud or Amazon Web Services (AWS), they still cannot guarantee multi-tenancy as these solutions address only hardware challenges.

              The Economist magazine in 2004 described it: “Those forerunners also promised a software revolution by hosting the software applications of companies. But they failed because they simply recreated each client’s complex and unwieldy datacentre in their own basements, and never overcame the old problems of installation and integration with other software. With each new customer, the old ASPs had, in effect, to build another datacenter; there were few economies of scale.”

              To improve their position in a shifting marketplace, on-premises EHS vendors have found a way to market their solutions as “cloud-based” when they are not backed by the fundamental principle of what that means. Considering the large investment that is associated with the purchase or licensing of EHS software, it is critical for customers to be able to tell a true cloud product from a fake one. But how can you spot a fake?

              Just ask the EHS software vendor these four questions:

              1. Do you support both single-tenant and multi-tenant deployments of your software?
              2. Does your software have version numbers? 
              3. Do you charge for upgrades?
              4. Can we install your software on our infrastructure?

              If the answer to any of these questions is yes, the vendor is not committed to only multi-tenant architecture, and you should not move to their “cloud.”

              Multi-tenancy is the only proven SaaS delivery architecture that eliminates many of the problems created by the traditional software licensing and upgrade model where software is installed as a single-tenant application on a customer’s premises or at a customer’s or vendor’s data center. In contrast, in multi-tenancy, all customers access the same software on one or a set of linked servers.

              Multi-tenancy requires a new architectural approach. Companies have to develop applications from the ground up for multi-tenancy. Once companies commit their limited financial resources to one architecture, it becomes nearly impossible for them to switch to the multi-tenancy model, no matter how many resources they have available. Moreover, for this reason, I am skeptical that many current vendors will be able to make a switch to multi-tenancy fast enough.

              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 will be very difficult for them to fully commit to a true SaaS solution since the majority of their resources will be allocated to supporting the on-premise software.

              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? Even Google and Microsoft are playing a catch-up game with Amazon’s AWS when it comes to cloud hosting business. The first mover advantage when it comes to multi-tenancy is a huge advantage for any vendor.

              In summary, an EHS software vendor can be either truly multi-tenant or not. If a vendor has installed their software on somebody’s else hardware and runs multiple instances of that software (even if the code base is the same) they are not and will never be true multi-tenant.

              Multi-tenant architecture

              Figure 5: Where do you want your software to reside? In multi-tenant or single -tenant infrastructure? If multi-tenancy is attempted on old infrastructure or legacy application upgrade watch out. After vendor built the first few floors of that skyscraper, there is no easy way to replace the foundation. You will be lucky if they end up like the tower of Pisa or Millennium tower in San Francisco. To keep the tower alive they will have to do constant underpinning of the foundation and restrict access to the structure. And you, the customer, will pay for it. That is what many customers of single-tenant EHS vendors are facing today.

              Therefore, when considering a SaaS solution, make sure that the vendor is a true SaaS vendor who is solely committed to the multi-tenant SaaS delivery model and has invested in a true multi-tenant platform. This is the only way to reap all the benefits that a SaaS model has to offer.

              IoT is considered one of the fastest growing trends in technology and has a potentially huge impact to automate how we manage water quality, air emissions and other key environmental performance indicators for data monitoring.

              In the following webinar, we focused on how various industries can benefit from integration and interoperability of a multi-tenant cloud platform and Internet of Things (IoT) platforms for managing, organizing and monitoring the structured and unstructured data coming from various different sources. Once in the platform, a centralized data repository is created that is suitable for analyzing the key environmental indicators for management, sustainability and environmental compliance.

               

               

              With the deployment of IoT through several automated technologies like sensors, programmable logic controllers (PLCs) and other internet and mobile connected devices and other instruments, a masses of real time data is generated that not only needs to be stored but analyzed and managed at interception along with several other sources of structured and unstructured data from different sources. All this data generated by different streaming devices need to be connected through a central place in a scalable cloud-based application to manage compliance and help in real time monitoring of data to come up with for effective solutions for a smarter environment management and sustainability initiatives.

              Internet of Things - IoT

              IoT technology for environmental monitoring is a booming industry.  The IoT growth for the water industry alone is forecasted to be  $20.10 billion by 2021 that indicates the massive volumes of data will be generated that will need to be to be monitored, managed and analyzed in intelligent, well designed software systems. Excel spreadsheets and ad hoc in-house data systems will not be up to the challenge.

              What is the core business problem challenge this webinar will help the audience to solve?

              IoT - Internet of ThingsMany companies are concerned that the sheer volume of data will render the information useless, unless all sources of data mentioned above can be turned into actionable information. This challenge can be addressed via the deployment of a highly scalable, end-user configurable, SaaS-based multi-tenant cloud infrastructure, coupled with environmental data management and compliance software applications.  This configuration can connect all the incoming data and create a central data repository that is easily accessible and available for use with responsive data analytics.

              A multi-tenant SaaS application can help to process the flow of information in an efficient and effective manner, providing better business and information analysis and interpretation by using various integrated tools. Such applications can help in real time monitoring and provide timely alerts for management and compliance.

              Coupled with business analytics, the masses of data can be turned into concise and meaningful information for system users. This approach will solve the problem of managing too much information coming into the organization, and allow it to turn the streaming data into intelligent information to support desired decision-making.

              What are the top takeaways attendees can expect to learn?

              • Learn how to create a single centrally accessible system for recording data from various data monitoring sources.
              • Learn how integrating IoT technologies and a multi-tenant SaaS cloud application can allow companies to switch from periodic monitoring on a prescribed schedule to continuous real-time monitoring, without increasing monitoring cost and thereby reducing operational costs.
              • Learn how to reduce compliance cycle time and benefit from smarter management solutions.
              • Leverage the benefits of cloud computing using real-time tools like GIS applications and rich business analytics for reporting and analysis, timely alerts, etc.

               

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