Last month, I was fortunate to be able to attend the HxGN Live 2019 conference, where Hexagon CEO Ola Rollén explored the idea of how data can save the world. While on the surface this may seem like a fluffy, feel good but over the top headline for a keynote, the reality is that data has been at the heart of humankind’s discoveries and inventions for thousands of years. Today unlocking and leveraging the power of data is also why more than 90% of companies surveyed worldwide are pursuing Internet of Things strategies. Empowered by data from connected machines and sensors, organizations can unlock greater productivity, reduce waste and identify new solutions and opportunities that can all ultimately contribute to a more sustainable and efficient planet. Declining costs of connectivity technologies and improvements in wireless communications have made it easier than ever to connect machines and sensors. By 2025, IDC predicts that amount of data generated annually will reach 175 zettabytes (or 175 trillion gigabytes) worldwide and a substantial portion of this will be created by connected machines. This data volume will be beyond the scope of every human being to consume and process. If data is key to saving the world, effective data management will be key to making sure that we have the right information at the right time to leverage it effectively. In the IoT world, this means that: Machine data flows must be automated and rules-driven to ensure that the right data gets to the right applications at the right time. Machine data must be able to be easily integrated with the enterprise workflow – in other words, machine data must be able to interact with data from the heterogeneous machine network and enterprise business applications to create new insights. The need for direct human intervention in transporting machine data into the enterprise workflow or other applications must be minimized or eliminated all together. Today, recent estimates find that up to 73% of data in the enterprise is lost, untouched or never utilized. For IoT deployments, the difficulty lies not in connecting machines and sensors – but in developing the custom applications and firmware that can extract, transport and manage the data in the IoT machine network. At Machinechat, our focus is on solving this problem to make it easier and faster for IoT solution architects to deploy projects and accelerate their path to ROI. Because the world needs saving – and we don’t have any time to waste. Want to learn more? Sign up to be part of our upcoming beta.

Back in 2011, Cisco predicted that by 2020, there would be more than 50 billion devices connected. It's 2019 already and while we're still about 25 billion devices shy of that objective, I thought it would be a good time to reflect on how much progress has been made in IoT - and the challenges that still have yet to be overcome. The Good Connectivity chips, modules and gateways: Today, IoT solution architects and developers have a wide range of options in this arena. Cost and size continue to go down, making it easier and easier for IoT ideas to go from the drawing board to a proof of concept. We've even seen IoT modules that can fit in a device the size of a hearing aid. Nowadays, virtually anything can be connected. Wireless communications: From Wi-Fi to bluetooth to LP-WAN to LTE/cellular, wireless communications are becoming more robust, cheaper and able to travel even longer distances than ever before. The Challenging Integration complexity: To fully realize ROI from IoT projects, most companies must figure out how to integrate existing and new machine and sensor investments with business applications. All of this boils down to a lot of programming and customization that require additional technical skillsets and additional cost. It's no surprise then that the top reasons why most IoT POCs fail include budget or schedule overruns and lack of expertise. Lack of standards: Unlike IT equipment, most machines have been developed on proprietary platforms and software. Standardization is something that has been talked about for years - but getting machine manufacturers to agree on a common standard may be many light-years away and will still not address the issue of existing industrial machine investments, many of which still may have a decade or more of field usability. Security: For most CIOs and IT admins, the invasion of billions of connected machines can seem like an endless assault from the Night King and his army of wights. In fact, a 2017 report by the BBC found that most enterprises underestimated the number of connected devices by anywhere from 30-40%. Since most existing machine platforms were not designed with the sophisticated security and processing capabilities as IT equipment and most device OEMs are still in the early stages of addressing security in their new products, security continues to be a top concern for organizations considering an IoT deployment. The Potential AI and machine learning: For companies to realize the full potential of AI and machine learning, IoT data provides a perfect opportunity to enhance these new technologies and improve the ecosystem of IoT innovations at the same time. IoT platforms and software solutions: In the not too distant past, it seemed as if everyone and their grandmother was introducing an IoT platform. Most of these solutions were all inclusive, expensive and proprietary; the result was that customers still had to spend significant technical resources and time to integrate these solutions with their systems, hardware and data. With many organizations today converging around AWS and Azure as platforms, new software solutions are focused on delivering robust functionality for specific elements of the IoT system architecture. This means that the promise of IoT is not only accessible to the largest companies - but to small and middle market enterprises as well. Data Management: Data is at the heart of creating value from connecting machines. Data management recognizes that the volume of data being created in IoT networks requires a different and more automated approach to minimize data loss and increase IoT business velocity. Today, its estimated that nearly 70% of data goes unused. Just imagine how the businesses of tomorrow could benefit as more effective tools for managing data in the IoT network emerge. Today, more and more industries and companies are looking at IoT innovations to help them reduce costs and improve customer and business outcomes. Here at Machinechat our focus is on delivering ready-to-deploy network-integrated data management solutions that will enable IoT solution architects and developers to integrate machine data faster. We believe that solving this problem -- getting the right data to the right applications at the right time - will go a long way to helping IoT solution architects deploy more successfully (and more cost-effectively). Want to learn more? Sign up to stay up to date on the latest news from Machinechat!

There’s a (not so unknown) secret in the world of IoT and it’s this: We are still a long way off from the 50 billion devices that Gartner initially predicted would be connected by 2020: The lack of standards and interoperability turns even the simplest IoT project with modest goals into complex custom embedded firmware and protocol-aware application software development projects. Only the largest and richest companies are benefitting from IoT today as they are the only ones who can afford the budget and resources required to invest and execute custom software development. Even then, it can cost millions of dollars to develop custom firmware, software applications and platforms that are not easily scalable beyond their own walls. Small and mid-sized companies - who could most benefit from the efficiencies, ROI and competitive advantages that an IoT strategy can deliver – are also the ones who don’t have the financial and resource capacity to gamble the months, dollars and manpower on an IoT project Operational data is still trapped in proprietary application or platform silos that require custom software development or human intervention for information to be transported into the enterprise workflow. More than a decade after Cisco declared that the IoT was “born,” the result of these circumstances is that only one out of every 10 IoT projects are field deployed and even fewer are considered successful. While the range of sensors, powerful low-power processors, inexpensive wired and wireless communications technology options are greater than ever before, integration of heterogenous machine data with line-of-business applications is one of the key elements of IoT deployment that remains complex, time-consuming and expensive. For the advantages of IoT to be accessible to every one (organization, new or old machine or sensor, developer, and business application), we believe that integration solutions must be: Simple and ready-to-deploy. Read-to-deploy means that you can integrate existing sensors, machines and applications without the need to first develop custom firmware and software applications. Ready-to-deploy means minutes not months. Focused on configuration instead of coding. In the field, sensors, machines and applications talk different protocols and exchange data in different formats. Configuration instead of coding enables the right data to go to the right applications at the right time while eliminating the need for custom firmware or application software development. Hardware-agnostic. Developing custom firmware directly on sensors, machines or IoT gateways to integrate with business applications seems like an obvious and easy choice to many IoT developers. However, this creates vendor lock-in and limits reuse of investment. A network-integrated IoT data management solution enables IoT data management across all the sensor and machines in the network. At Machinechat, our commitment is to building solutions that reflect the values we think every IoT solution should be – ready and easy-to-use, affordable, robust and hardware-agnostic. That’s because we believe that for the next billion sensors to be deployed, our job isn’t just to create solutions that work well, but solutions that allow IoT to be accessible to every one – and not just some. Want to learn more? Sign up to get the latest information and news about Machinechat.

As more and more organizations are adopting an IoT strategy, effectively harnessing and managing data from the sensors and machines is becoming a critical piece to ensuring overall IoT deployment success. For data management to be effective, enterprises must be able to acquire and integrate data from these machines and sensors into multiple on-premise and cloud-based business applications. In other words, getting the right data into the right applications at the right time. There are three primary methods for building a data management application: Building data management into the sensor or machine software itself. Transporting all data into the cloud and building data management applications in the cloud. Integrating data management into network infrastructure. Here are the reasons why integrating data management into the network infrastructure makes the most sense: Preserves the functionality and security of both existing and new machine investments. Most IoT deployments involve integrating both new and existing machines and sensors, all of which are built on different hardware architectures and firmware. Moving data management into the network infrastructure eliminates the complexity in modifying the software of each and every sensor or machine. Reduces transactional cloud costs. Simply put, not all data needs to go to the cloud. Given that cloud based hosting and storage are based on volume and frequency, enterprises can quickly find themselves developing cost heartburn by sending all data to the cloud to be managed. Network-integrated data management enables data to be processed at the right location (locally, to an on-premise application or in the cloud) creating greater efficiency while reducing cloud costs. Eliminates manual data management. While the average human being creates under just 1 GB of data a day, a single sensor in an industrial machine can generate up to 6GB or more in the same time. This makes manual data management an impossible task; not surprisingly, Forester estimates that up to 73% of data generated in the enterprise goes unused and untapped. A rules-based network-integrated data management approach can allow for changing machine and sensor states to be automatically acted upon because let's face it - just putting data into a dashboard isn't enough. Enables IoT data management at the point of performance – the Network. Putting the responsibility for enabling data management on directly on a sensor or machine doesn’t make sense when you consider that device OEMs have a limited visibility of how their device may be deployed in an IoT deployment with heterogenous devices. Transporting all the data into the cloud to be sorted can get expensive and still will not eliminate the need for IoT developers, system integrators and IT admins to develop custom cloud applications that address the business objectives and goals of each IoT deployment on a project-by-project basis. Simply put: IoT data management is a business enterprise problem and as such, solving it is most efficiently done at the network level. Here at Machinechat our focus is on helping IoT solution architects and developers Cross the IoT Chasm by making the task of enabling network-integrated data management easier with ready-to-deploy software that allows them to configure data management workflows within the network in minutes versus months spent developing custom programming. Want to learn more? Click here to stay up to date on Machinechat or sign up to participate in our upcoming Beta!

As we find ourselves less than 12 months away from 2020 (when the number of connected devices is supposed to surpass 20 billion), here is our list of the top three IoT development trends we think will emerge in 2019: Trend #1: Controlling the Clout (and the Cost) of the Cloud Say "IoT" and the very next phrase most developers think of is "cloud". And there's no question that the marketplace is full of cloud-based applications and services. But as we recently witnessed at NexGen 2018, system integrators and MSPs are starting to realize that not everything needs to be there and not surprisingly, a new slew of vendors are starting to enter the market with innovative solutions where no cloud is necessary. While the cloud will still be a necessary part of an IoT project, we predict that IoT deployers will seek to better manage their cloud costs and dependencies by adding more localized solutions where it makes sense. Trend #2: Developers Want More Specific Software Solutions (vs Broad Proprietary Platforms) It seemed like just last year (actually it was last year), that everyone in the IoT world was wondering what platform would take the lead in uniting the machine network. Fast forward to the end of 2018, and a recent Gartner report indicated that less than 10% of all platform providers had revenues of more than $10M annually. The reality is that IoT is complex - different machines, operating on proprietary hardware and software platforms - and even the best IoT platforms require that developers and end-user customers spend months (or even years) of additional coding and integration to create a fully functional deployment. That's a lot of effort to commit to for a five- or six- figure annual invoice and even more, when you consider the number of legacy machines that may need to be reconfigured, decommissioned or excluded to create a workable final deployment solution. Cost, integration pain points and legacy equipment are all issues that we believe will contribute to more developers seeking out specialized and more affordable software solutions that can help them address IoT deployment challenges and cross the chasm between POC and roll-out. Trend #3: It's All About the Data (Management) IoT is more than just connecting a device. It's about what you can do with the data. With AI, machine-learning and Big Data analytics all gaining momentum, we believe the next logical question for system integrators, developers and MSPs to address will be making sure what specific device data gets to the appropriate location (or user) in the appropriate format at the appropriate time (and that it's stored as needed) and how well specific device data is integrated into the enterprise workflow. While some will look to embed this intelligence right into new machines, we believe that this leaves the multitude of existing deployments and legacy equipment out in the cold. In heterogeneous industrial machine environments (where equipment can have a lifecycle that spans a single decade or more), being able to deploy solutions that leverage and manage the data off these devices will become key to IoT success. To keep up to date on our latest posts and product developments, click here.

Today, it’s easier than ever for organizations to build an IoT proof of concept (POC). With most cloud service providers offering free or low cost first-year services, and a plethora of open source software libraries and low cost prototyping hardware platforms, getting an IoT idea from a product manager’s roadmap sketch to POC is relatively inexpensive and easy. Despite this wealth of low-cost development resources, recent studies have found that more than 70% of IoT initiatives never make it beyond the POC stage. Why? The IOT Chasm For an IoT solution to be successfully adopted into the real-world ecosystem, it must allow an organization and its customers to consistently achieve enhanced business value. These include reduction in costs through improved quality, productivity, safety, compliance and minimized downtime. In addition, successful IoT projects can help organizations identify new revenue streams through services that enhance the customer experience. Not surprisingly, a 2017 Cisco survey found that the top reasons why most IoT POCs failed were related to a failure to meet schedule, cost and business outcome expectations. Simply put, the same tools OEMs and system integrators use to build an IoT POC may not be sufficient to create a field-ready solution that can deliver the business outcomes and ROI that customers and stakeholders need. We call this gap the IoT Chasm. Failure to Bridge the IoT Chasm Has Significant Consequences High failure rates in bridging the IoT Chasm can have significant consequences throughout the ecosystem. Several man-years of precious time and millions of dollars in investment are lost. A system integrator or solution provider may have invested tens of thousands of dollars to build a proof-of-concept but never win the real project. A product leader may risk his or her career when projects of this scale do not deliver ROI or fail to meet customer performance expectations. Organizations can lose time to market advantage or lose the entire market opportunity to a competitor. Success Requires Looking Before You Leap Product development teams and system integrators must address key business issues that impact the real costs, complexity and ROI of IoT initiatives. This is especially relevant for industrial sectors where machines and devices typically have long lifecycles in the field (as opposed to consumer devices) and the cost to rip and replace is much higher. Some of these questions include: Identifying and prioritizing stakeholders’ needs What business outcomes (and for which stakeholders) must be achieved to justify project investment? What applications must be developed or re-worked and what data will need to be extracted and integrated? What data needs to reside in the cloud or locally? How will this impact software design and costs to the customer, technical support teams and your organization? What machine network environments will the solution operate in? Does the solution need to work with existing deployments? Will the solution need to be adaptable to future new products? How can changes, re-certifications and workforce training be minimized? If the POC is for productization on new products, will existing customers upgrade? When will they upgrade? How much does it cost and how long will it take to acquire new customers for the new products? All these questions influence almost every design decision you will make in turning the POC into a production system and comes with time, cost and complexity tradeoffs during development. Crossing the IoT Chasm Finally, product managers and system integrators must take into account a customers’ deployment tolerance – the people, time and money resources they are able and willing to invest can directly impact how field-ready your IoT POC or solution must be before introducing it to the marketplace. Crossing the IoT Chasm means understanding the gap between your business outcomes, your customers and stakeholders desired business outcomes and how much in technical, time and financial resources you will need to invest to deliver a real-world ready IoT solution. In some cases, you can cross the IoT chasm step-by-step and in other situations, it may require a giant leap. Regardless of which option you choose, you must stay focussed on the expected business outcomes that you expect out of your IoT initiatives. At Machinechat, our focus is on introducing the next-generation of ready-to-deploy IoT solutions that will enable OEMs, system integrators and IT administrators to narrow the IoT chasm. To learn more or stay tuned on our journey, click here.

IoT represents a goldmine market opportunity for system integrators and MSPs. As more and more enterprises are rolling out IoT projects that involve integrating hundreds or thousands of devices and sensors into the IT infrastructure and organizations’ data workflows, system integrators and MSPs can play a critical strategic role in helping to define, develop, deploy and support these initiatives – while also improving their own delivery of services and building new business models that can provide recurring revenue. CHALLENGES OF BUILDING A SCALABLE IOT SERVICES BUSINESS Building a Machine Network Is Different & More Complex than Networking IT Equipment Industrial and commercial machines were never originally intended to operate like computers or servers and integrating them into an IT infrastructure is not as simple as “plug and play.” Many were built on legacy proprietary hardware and software platforms that were not designed with the intent that they collaborate in a heterogeneous machine environment, much less via the Internet or a cloud. This requirement alone can make taking on an IoT project daunting. With thousands of different types of sensors and machines – all of which speak their own language and have differing product and maintenance lifecycles, trying to integrate them all into an effective information workflow for even one customer project can require months or years of work and requires a deep and diverse list of skillsets. Machine Data Storage and Processing Capabilities Are Much More Limited than Traditional IT Machines Most machines were also not built to hold data for extended amounts of time, and as such contain very limited storage or data processing capabilities. Attaching external IoT device servers or gateways, can be helpful to provide a tunnel for data to be securely transported from the device to the cloud. Using gateways still require that developers spend significant time writing additional code to automate what data gets extracted from the device and enabling that data to be connected to an application, or in some cases, multiple applications. And leaving all the data to be sorted out in a cloud-based application (or multiple applications) can quickly become an expensive proposition for both you and your customer once your project is beyond the POC stage. As many MSPs and SIs are already realizing, not all data needs to be sent to the cloud. Of course, knowing what data to send where, when and to whom is another added complexity when it comes to implementing an IoT solution. Cloud-Based Off the Shelf Apps Are Good for Proof of Concept But May Not Get You to a Field-Ready Solution Today, there are several off-the-shelf solutions that make visualizing the data from your POC easier. However, there is a significant gap between going from an attractive demo POC that presents well in the boardroom to a field-ready solution that can be deployed into the enterprise. A field-ready IoT solution or project is pursued with the intent that it will deliver increased efficiency within the existing workflow. That means that it’s not enough to just extract data and present it in a dashboard that still requires human beings to look at and interpret – device data must be intelligently harnessed and integrated with information from the enterprise to automate actionable insight. When the machine is down is it because the wi-fi access point isn’t working, it isn’t plugged in or could it be a device failure? Depending on the answer, the person who must solve the situation could either be your IT guy, the machine operator, or your manufacturer’s technical support that needs to be called. IoT is supposed to solve that – but going from a nice looking POC to a fully field-ready solution requires substantially more programming and additional capabilities that can make the time-to-rollout much longer while tying up your team resources (thus prolonging or displacing your time to revenue and meaningful profitability). And as any experienced SI and MSP knows, initial customer feature requirements often don’t remain static as time passes. (Our prior experiences have taught us that if anything, the list of must-haves gets longer and more complex.) Bottom line: the very challenges that can make an SI or MSP critical to the success of any organization’s IoT project are also the same things that can quickly turn an SI or MSP’s IoT services business into a mild form of indentured servitude to a few. On the upside, do it right and you’ve positioned yourself for a long-term, scalable business that can deliver recurring revenue over multiple years (versus leaving yourself to the mercy of IT cycles). HOW SYSTEM INTEGRATORS CAN POSITION THEMSELVES FOR SUCCESS IN AN IOT WORLD 1. Recognize that IoT is often a strategic VP or C-level business decision and not just an IT decision. This means not just addressing IT’s goals and fears, but also understanding the workflow needs and ROI goals of the different departments and levels of an organization that will be impacted by the deployment. This approach will transform you from being perceived as a services vendor (who is often judged just on price) to a valued and trusted business partner who is invested in their long-term corporate objectives. 2. Plan for longevity and flexibility. Most industrial and commercial machines have long product lifecycles – anywhere from 10 to 20 years or more. That means that your team’s proposed solution must not just be flexible to adapt for new changes in the IT infrastructure but also have the ability to support hundreds (and in the longer-term, possibly thousands) of machines for a long, long, long time. 3. Understand that connectivity is the easy part; focus on the data. At its heart, uncovering IoT’s value starts with harnessing machine data to improve business continuity and productivity throughout the enterprise. Implementing a solution that embeds flexible intelligent data management is the key to more efficiently recognizing ROI for both you and your customer. 4. Avoid becoming too cloud-dependent. Today, there’s a lot of emphasis on putting nearly everything onto the cloud (from applications to data). But the reality is that most machine data and applications don’t need to go beyond the local enterprise to be useful. Service providers that go too cloud heavy risk running up operational costs as these projects move to wider spread field deployment, as well as increasing security and privacy concerns. 5. Your time and your people are worth money, so use both wisely. It can be easy to tie up your time and team for months in trying to build your own custom solution for a customer, but this can also limit the scalability of your business in the long-run. Machine-centric off-the-shelf solutions that are robust enough to help you create field-ready POCs can help you and your customer shorten the time from POC to roll-out, saving everyone time, resources and money. In addition, using these solutions can help you spend more time on solving your customer’s business problems (and less time on trying to get the tactical side working). There’s no question that pursuing an IoT path is not for the faint of heart. But with experts predicting that the number of connected machines will more than double to 20.4 billion by 2020, system integrators and MSPs who position themselves wisely now – with the right mindset, focus on data and efficient use of in-house and off-the-shelf resources – stand to benefit greatly in the new digital economy. The next generation of IoT solutions will move system integrators and OEMs from using stone-aged methods and tools to advanced machine-centric tools that make building and deploying IoT even easier. Want to learn more? Click here to stay up to date with Machinechat and our upcoming product launch.

To fully realize the financial and productivity benefits of IoT deployment, organizations must go beyond simply connecting existing and new machines to the network or cloud. For IT administrators, system integrators and managed service providers, this means designing solutions that enable data coming from the heterogeneous machine network to become part of an automated enterprise workflow. Here we discuss the fundamental principles of data management that need to be addressed and why it's important to achieving IoT deployment success. IoT is about empowering machines to enhance our lives and business outcomes - whether those goals are making sure managing a city's natural resources to minimize waste, providing critical care to a patient at the right time, minimizing downtime on a factory floor or ensuring a home or office environment is safe and comfortable. To do this we need machines to more effectively communicate with a) people; and in the not to distant future, b) other machines. The language machines speak in is called data. But not all data is relevant to every audience nor does it need to be transmitted continuously. Simply put, in the ideal IoT scenario, a machine would be able to push data (or communicate) as needed after answering the following: Is this data that needs to be communicated? Who or what is this data important to? Is it a specific person or machine or a group of persons and machines? Or can the machine initiate an automated action itself? When do they (persons or machines) need the data? (Does this data require immediate action, more information and from who or what, or can it wait?) How should this data be communicated and in what format(s)? Does this data need to be stored? If so, where and for how long? While the above questions may seem a bit simplistic, their potential impact is significant and the answers are more complex to implement than they may first appear. Enabling connected machines to intelligently manage their data is key to making IoT work as it was intended. Without intelligent data management, we will be only connecting machines with the outcome of creating more work and stress for mankind. Combined with AI and machine learning, machines will also be able to act on data received from other sources to do a multitude of tasks - from self diagnosing and repairing to automatically adjusting their own operational actions/responses. At MachineChat, our focus is on delivering solutions that make implementing intelligent data management - whether it's to a new machine or ones that are already deployed - fast and easy. To stay up to date with our latest news, sign up here.

Say "Internet of Things" and the second phrase that typically follows is "cloud." Cloud computing is great for IT applications, e.g. your HR software. Your HR doesn't care what's on the other side of her browser so long as she can create the new hire records. The HR software can be running on a local server, desktop or in the cloud on another continent. Once your HR person finishes creating new hires in the system there isn't much data that goes into the application. However, the scenario is very different with IoT. Unlike IT applications that predominantly interface with human users, IoT applications interface with machines that generate data in much higher orders of magnitude compared to their IT counterparts. Many companies are realizing that relying too heavily on the cloud when it comes to operating and maintaining an IoT project in the cloud at production scale can be expensive and inefficient. Storing All Your Data in the Cloud is Expensive and Inefficient The average person creates less than one GB of data per day between work and personal activities - and most of us average 8 hours of rest out of a 24 hour cycle. In comparison, one connected car generates on average 25 GB of data per hour. Needless to say, unlike people, industrial machines - many of which are "on" 24/7 -- generate a lot of data. Further, unlike people, machines cannot differentiate between what data is important or actionable without programming or human intervention. A recent Forrester report found that up to three-fourths of all data within an enterprise goes unused. It doesn't take much to figure out that sending every temperature or on/off reading from a sensor or machine to the cloud and paying for every byte transferred and stored isn't a common sense strategy. Further, while cloud-scale storage systems give you the notion that storage space is virtually unlimited and cheap, transferring all the data from all your machines and sensors into the cloud for analysis and/or storage can get real expensive and inefficient very fast. A More Efficient Cloud Starts With Managing Machine Data Intelligently If not all data needs to be in the cloud, then it makes sense that managing machine data should occur closest to the machines that generate the data. Bringing data management intelligence into the network edge allows IoT solution architects and device design engineers to steer the right data to the right applications while enforcing data retention policies. The bottom line is faster time to deployment, reduced operational costs and improved ROI. Here are three issues to consider when building your data management solution Decide what data needs to go where. What data can stay local and what data needs to be fed into a cloud-based application? Implement various compression methods for cloud-bound data. Depending on the type of data and IoT application, there are various methods for more efficiently compressing or consolidating data that is cloud-bound. Establish automated storage mechanisms for managing different datasets in the cloud. Not all data needs to be stored the same way or for the same amount of time once it gets to the cloud. Depending on the type of data and your company's data retention strategy, data in the cloud may be stored in a time series, NoSQL or SQL database. Here at Machinechat, we're working on the next generation of hardware-agnostic intelligent data management solutions that will enable IoT solution developers and architects to reduce man-years of development and eliminate complexity in deploying IoT projects successfully into the enterprise and Cross the IoT Chasm. To keep up to date on how we plan to accelerate connecting the next billion IoT devices, sign up for our mailing list here. Like what you read? Controlling the clout and cost of the cloud is just one of three IoT development trends that will impact 2019.

This is Part One of a three-part blog series addressing how IoT solution architects and OEMs can accelerate time to revenue and broad adoption of their IoT-enabled solutions. Network connectivity is the first step towards making a machine, a device or a sensor a part of an IoT solution; it serves as the tunnel for data to be transported between the machine and enterprise software applications. Millions of dollars and thousands of man-years are spent in developing highly reliable products for mission critical industrial environments that demand long product lifecycles and must operate with near-zero downtime. Despite all this effort and expertise, when it comes to adding network connectivity to machines, nearly 75% of all IoT initiatives fail to go beyond the POC stage. Adding network connectivity to machines, devices or sensors continues to be a complex and expensive task that often results in schedule and budget overruns. This post shows you how to add network connectivity to machines while minimizing costs and eliminating complexity. With multiple wired and wireless technology options and thousands of form factors, features and price points to choose from, it can seem like settling on a connectivity solution would be relatively easy. But the inverse is true. Choose or commit to the wrong network connectivity strategy and your team could find itself enslaved to a hamster wheel of on-going certification, firmware and OS updates, or worse, spending hundreds of man years designing a solution that later is found to not perform in the field and sends your support team scrambling to retrofit a solution. Adding network connectivity into existing and new machines is both time-consuming and complex. For connectivity solutions to work, development teams must: Standardize on or develop an IoT connectivity module that can work in different types of customer deployment scenarios. Develop drivers, embedded network stacks, configuration and connectivity management applications, security applications and test to make sure that they do not alter or negatively impact the existing machine application. Alter the machine's PCB and machine casing to accommodate the IoT modules, including any additional power consumption or processor needs Obtain and maintain needed wireless certifications and updates. While many industrial products average lifespan is 7-10 years or longer, wireless standards often change every two to three years. This means dedicating development team resources to keeping up with certifications and updates that can impact your supply chain and product design roadmap. Commit development and support resources to handling on-going firmware and OS updates. In many cases, building connectivity into a device requires a development team to significantly alter its existing hardware and software design, or worse, can force them to throw away decades' worth of investments in an existing field-proven machine platform to create a new connected product that has yet to see the light of the day. All of this adds up to more cost, complexity and uncertainty to the project. Use Off-the-Shelf Network Connectivity Solutions To Minimize Costs and Accelerate Time to Market Not surprisingly, many solution architects choose to use off-the-shelf IoT modules and gateways in their designs. There are several key benefits to using off-the-shelf network connectivity solutions: Pre-certified and field-tested in other applications Reduces the need for additional budget, time and resources for network stack and network applications development and on-going maintenance. Advanced modules and gateways provide additional security capabilities and management interfaces that simplify field deployments. IoT connectivity solutions come in two forms: Embedded IoT modules and gateways which are built into a machine or sensor; and External IoT device servers and gateways which are attached outside of a machine. Embedded IoT Modules and Gateways Embedded IoT module solutions range widely from ones that provide simple certified network connectivity to more expensive and advanced embedded gateways that deliver security, multiple integration options, connectivity management and other applications. In addition, their small footprint allows embedded IoT modules to be incorporated into a wider range of IoT applications than external IoT device servers. Nowadays, embedded IoT modules can range in size from ones small enough to fit inside a hearing aid to ones as large as a U.S. half dollar bill. Key advantages of embedded IoT modules and gateways include: Fully integrated solution with no external boxes, cables or power supplies to ship and support Lower cost compared to external IoT device servers and gateways Embedded IoT modules and gateways need modifications to existing PCBs, power supplies and machine casing to incorporate the module. One key risk factor in using an off-the-shelf embedded IoT modules or gateways is vendor lock-in. You are committing to a partnership with a specific IoT module supplier as part of your product design. If your supplier's roadmap, pricing or supply chain changes, this can have critical consequences for your business and product development roadmap. External IoT Device Servers and Gateways External device servers or gateway typically provide certified wireless radio module(s), network OS, stack, drivers and configuration tools in a ready-to-use enclosed casing that can be attached to a single machine or in some cases, a few machines. Like embedded IoT modules, they vary in terms form factor, features and pricing. Key advantages of external IoT device servers and gateways include : Eliminating the need for redesigning the machine's existing hardware, software and enclosure. Replacing an external device server or gateway is much cheaper than replacing the logic board or the entire machine. Adding network connectivity to machines already deployed in the field Enabling a range of network connectivity options versus a single hardwired embedded solution to support a wide range of customer deployment scenarios Reducing your risk of vendor lock-in. Since most industrial machines tend to have lifecycles of 7-10 years or longer, an external IoT device server or gateway is most likely a more adaptable and cost-effective choice for most industrial IoT OEMs and system integrators. The Bottom Line: Keep Connectivity Simple The bottom line? In the near future, machine connectivity will be considered a standard option - one that customers will expect to receive at no additional cost. So when it comes to connectivity, keeping it as simple as possible. Separating frequently changing network technology away from your stable core machine logic is often the best way to go and ensures that your machine stays reliable and cost-effective while IoT-enabled. Offloading network connectivity to off-the-shelf networking hardware and software enables your team to focus on your core business logic, minimizing costs, accelerating time-to-market and ultimately enhance ROI. Machinechat is focused on designing next-generation ready-to-use data management solutions that enable OEMs, system integrators and MSPs to Cross the IoT Chasm and bring the next billion devices online - faster, smarter and more successfully. Want to learn more? 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