I’m all about IoT and digitalization anymore. This is the next movement following the automation trend I championed some 15 years ago.
Last month, I started receiving emails about predictions for 2018. Not my favorite topic, but I started saving them. Really only received a couple good ones. Here they are—one from Cisco and one from FogHorn Systems.
From Cisco blog written by Cisco’s SVP of Internet of Things (IoT) and Applications Division, Rowan Trollope, comes several looks at IoT from a variety of angles. There is more at the blog. I encourage you to visit for more details.
Until now, the Internet-of-Things revolution has been, with notable outlier examples, largely theoretical and experimental. In 2018, we expect that many existing projects will show measurable returns, and more projects get launched to capitalize on data produced by billions of new connected things.
With increased adoption there will be challenges: Our networks were not built to support the volumes and types of traffic that IoT generates. Security systems were not originally designed to protect connected infrastructure against IoT attacks. And managing industrial equipment that is connected to traditional IT requires new partnerships.
I asked the leaders of some of the IoT-focused teams at Cisco to describe their predictions for the coming year, to showcase some of these changes. Here they are.
IoT Data Becomes a Bankable Asset
In 2018, winning with IoT will mean taking control of the overwhelming flood of new data coming from the millions of things already connected, and the billions more to come. Simply consolidating that data isn’t the solution, neither is giving data away with the vague hope of achieving business benefits down the line. Data owners need to take control of their IoT data to drive towards business growth. The Economist this year said, “Data is the new oil,” and we agree.
This level of data control will help businesses deliver new services that drive top-line results.
– Jahangir Mohammed, VP & GM of IoT, Cisco
AI Revolutionizes Data Analytics
In 2018, we will see a growing convergence between the Internet of Things and Artificial Intelligence. AI+IoT will lead to a shift away from batch analytics based on static datasets, to dynamic analytics that leverages streaming data.
Typically, AI learns from patterns. It can predict future trends and recommend business-critical actions. AI plus IoT can recommend, say, when to service a part before it fails or how to route transit vehicles based on constantly-changing data.
– Maciej Kranz, VP, Strategic Innovation at Cisco, and author of New York Times bestseller, Building the Internet of Things
Interoperable IoT Becomes the Norm
The growth of devices and the business need for links between them has made for a wild west of communications in IoT. In 2018, a semblance of order will come to the space.
With the release of the Open Connectivity Foundation (OCF) 1.3 specification, consumer goods manufacturers can now choose a secure, standards-based approach to device-to-device interactions and device-to-cloud services in a common format, without having to rely on, or settle for, a proprietary device-to-cloud ecosystem.
Enterprise IoT providers will also begin to leverage OCF for device-to-device communications in workplace and warehouse applications, and Open Mobile Alliance’s Lightweight Machine-to-Machine (LwM2M) standard will take hold as the clear choice for remote management of IoT devices.
In Industrial IoT, Open Process Communication’s Unified Architecture (OPC-UA) has emerged as the clear standard for interoperability, seeing record growth in adoption with over 120 million installs expected as 2017 draws to an end. It will continue to grow into new industrial areas in 2018 driven by support for Time Sensitive Networking.
– Chris Steck, Head of Standardization, IoT & Industries, Cisco
IoT Enables Next-Gen Manufacturing
Manufacturing is buzzing about Industrie 4.0, the term for a collection of new capabilities for smart factories, that is driving what is literally the next industrial revolution. IoT technologies are connecting new devices, sensors, machines, and other assets together, while Lean Six Sigma and continuous improvement methodologies are harvesting value from new IoT data. Early adopters are already seeing big reductions in equipment downtime (from 15 to 95%), process waste and energy consumption in factories.
– Bryan Tantzen, Senior Director, Industry Products, Cisco
Connected Roadways Lay the Groundwork for Connected Cars
Intelligent roadways that sense conditions and traffic will adjust speed limits, synchronize street lights, and issue driver warnings, leading to faster and safer trips for drivers and pedestrians sharing the roadways. As these technologies are deployed, they become a bridge to the connected vehicles of tomorrow. The roadside data infrastructure gives connected cars a head start.
Connected cities will begin using machine learning (ML) to strategically deploy emergency response and proactive maintenance vehicles like tow trucks, snow plows, and more.
– Bryan Tantzen, Senior Director, Industry Products, Cisco
Botnets Make More Trouble
Millions of new connected consumer devices make a nice attack surface for hackers, who will continue to probe the connections between low-power, somewhat dumb devices and critical infrastructure.
The biggest security challenge I see is the creation of Distributed Destruction of Service (DDeOS) attacks that employ swarms of poorly-protected consumer devices to attack public infrastructure through massively coordinated misuse of communication channels.
IoT botnets can direct enormous swarms of connected sensors like thermostats or sprinkler controllers to cause damaging and unpredictable spikes in infrastructure use, leading to things like power surges, destructive water hammer attacks, or reduced availability of critical infrastructure on a city or state-wide level.
– Shaun Cooley, VP and CTO, Cisco
Blockchain Adds Trust
Cities are uniquely complex connected systems that don’t work without one key shared resource: trust.
From governmental infrastructure to private resources, to financial networks, to residents and visitors, all of a city’s constituents have to trust, for example, that the roads are sound and that power systems and communication networks are reliable. Those working on city infrastructure itself can’t live up to this trust without knowing that they are getting accurate data. With the growth of IoT, the data from sensors, devices, people, and processes is getting increasingly decentralized—yet systems are more interdependent than ever.
As more cities adopt IoT technologies to become smart—thus relying more heavily on digital transactions to operate—we see blockchain technology being used more broadly to put trust into data exchanges of all kinds. A decentralized data structure that monitors and verifies digital transactions, blockchain technology can ensure that each transaction—whether a bit of data streaming from distributed air quality sensors, a transaction passing between customs agencies at an international port, or a connection to remote digital voting equipment—be intact and verifiable.
– Anil Menon, SVP & Global President, Smart+Connected Communities, Cisco
Sastry Malladi, CTO of FogHorn Systems, has shared his top five predictions for the IIoT in 2018.
1. Momentum for edge analytics and edge intelligence in the IIoT will accelerate in 2018.
Almost every notable hardware vendor has a ruggedized line of products promoting edge processing. This indicates that the market is prime for Industrial IoT (IIoT) adoption. With technology giants announcing software stacks for the edge, there is little doubt that this momentum will only accelerate during 2018. Furthermore, traditional industries, like manufacturing, that have been struggling to showcase differentiated products, will now embrace edge analytics to drive new revenue streams and/or significant yield improvements for their customers.
2. Additionally, any industry with assets being digitized and making the leap toward connecting or instrumenting brownfield environments is well positioned to leverage the value of edge intelligence.
Usually, the goal of these initiatives is to have deep business impact. This can be delivered by tapping into previously unknown or unrealized efficiencies and optimizations. Often these surprising insights are uncovered only through analytics and machine learning. Industries with often limited access to bandwidth, such as oil and gas, mining, fleet and other verticals, truly benefit from edge intelligence.
3. Business cases and ROI are critical for IIoT pilots and adoption in 2018
The year 2017 was about exploring IIoT and led to the explosion of proof of concepts and pilot implementations. While this trend will continue into 2018, we expect increased awareness about the business value edge technologies bring to the table. Companies that have been burned by the “Big Data Hype” – where data was collected but little was leveraged – will assess IIoT engagements and deployments for definitive ROI. As edge technologies pick up speed in proving business value, the adoption rate will exponentially rise to meet the demands of ever-increasing IoT applications.
IIoT standards will be driven by customer successes and company partnerships
4. IT and OT teams will collaborate for successful IIoT deployments
IIoT deployments will start forcing closer engagement between IT and operations technology (OT) teams. Line of business leaders will get more serious around investing in digitization, and IT will become the cornerstone required for the success of these initiatives. What was considered a wide gap between the two sectors – IT and OT – will bridge thanks to the recognized collaboration needed to successfully deploy IIoT solutions and initiatives.
5. Edge computing will reduce security vulnerabilities for IIoT assets.
While industries do recognize the impact of an IIoT security breach there is surprisingly little implementation of specific solutions. This stems from two emerging trends:
a) Traditional IT security vendors are still repositioning their existing products to address IIoT security concerns.
b) A number of new entrants are developing targeted security solutions that are specific to a layer in the stack, or a particular vertical.
This creates the expectation that, if and when an event occurs, these two classes of security solutions are sufficient enough. Often IoT deployments are considered greenfield and emerging, so these security breaches still seem very futuristic, even though they are happening now. Consequently, there is little acceleration to deploy security solutions, and most leaders seem to employ a wait-and-watch approach. The good news is major security threats, like WannaCry, Petya/Goldeneye and BadRabbit, do resurface IIoT security concerns during the regular news cycle. However, until security solutions are more targeted, and evoke trust, they may not help move the needle.
Just when I was saying last week that the The Industrial Internet Consortium (IIC) had been very busy, I interviewed Eric and Wael about this newly published the IIC Industrial IoT Analytics Framework Technical Report (IIAF). It is the first IoT-industry technical document to include a complete set of instructions that IIoT system architects and business leaders can use to deploy industrial analytics systems in their organizations.
People I talked with used to think that the Industrial Internet of Things was all about sensors, or the Internet, or Things. Actually, it is nothing without databases and analytics. And here is the IIC to provide a framework for systems architects.
From the news release:
IDC has predicted that by 2020 one tenth of the world’s data will be produced by machines. Yet without an analytics blueprint, that data could sit unused, never being analyzed and turned into useful insights. The IIAF is a first-of-its-kind blueprint for system architects and designers to map analytics to the IIoT applications they are supporting, to ensure that business leaders can realize the potential of analytics to enable more-informed decision making.
“Using analytics to provide insights is the holy grail of industrial IoT,” said Wael William Diab, IIC Industrial Analytics Task Group Chair, IIC Steering Committee Member and Senior Director at Huawei. “The IIC IIAF takes a holistic approach by developing the foundational principles of industrial analytics as well as looking at the complete picture from design considerations to creation of business value and functionality. This entire ecosystem approach is valuable to both business leaders as well as technologists, engineers and architects looking to deploy IIoT systems.”
The IIC IIAF is the first document to offer a broad scope of requirements and concerns for industrial analytics applied to IIoT systems. It shows IIoT system architects the steps involved in developing analytics for IIoT systems with state-of-the-art information, including definitions and information flows that shows how the technologies can be applied to the applications. Guidance is provided how and where to deploy industrial analytics based on the characteristics of the applications and outcome expectations. In addition, the IIAF looks at emerging technologies including artificial intelligence (AI) and big data, which are expected to play an increasingly important role in industrial analytics.
“Industrial Analytics is changing rapidly, from data lake to stream processing and machine learning. Our framework provides a common understanding and encourages interoperability across the IIoT ecosystem,” said K. Eric Harper, IIC Industrial Analytics Task Group Chair, IIC Steering Committee Member and Senior Principal Scientist at ABB. “With this foundation, it is more likely that applications will be able to adopt new technologies and techniques in the future without substantial rework.”
Analytics have been applied to other many other fields such as finance and retail to improve the customer experience and increase corporate revenue. The major differentiation in industrial settings is the physicality of the systems. For example, if IIoT systems are not configured correctly, or if their maintenance schedule is wrong, the systems can cause physical harm. Analysis and improvement of operational maintenance across multiple systems must be performed with extreme diligence, and are as important to technology leaders as they are to business leaders looking to increase profits.
“Industrial analytics are the engine that takes data from industrial systems and creates value and insight to get business results,” said Will Sobel, IIC Industrial Analytics Task Group Chair and Chief Strategy Officer at VIMANA. “The sophistication of analytical methods in other domains, such as finance and media, have been evolving at a breakneck pace, but little has been done to apply these techniques to industrial systems. The IIAF provides the special considerations one needs to consider before one uses these technologies in an industrial system.”
When analytics are applied to machine and process data, they help optimize decision-making and enable intelligent operations. These new insights and intelligence can be applied across all levels of any enterprise in any industry if the appropriate data can be collected, curated and analytics are applied correctly.
“In transforming machine raw data into actionable information, industrial analytics plays a crucial role in the industrial Internet just like refineries that turns crude oil into high energy fuel. The actionable information from the analytics is the fuel that drives the optimization of industrial operations and production, the creation of new revenue streams and the enablement of new business models,” said Shi-wan Lin, IIC Technology Working Group Chair and CEO and Co-Founder, Thingswise, LLC.
The full IIC Industrial IoT Analytics Framework Technical Report and list of IIC members who contributed can be found on the IIC website.
OSIsoft LLC, which now self-identifies as a leader in operational intelligence, has announced that SoftBank Group (“SoftBank”) has acquired a significant minority equity interest in the company.
The company is mostly known for its data historian, PI, which is used throughout industry. Over the past few years it has taken on some significant investments evidently in order to fund further growth as data and data analytics command center stage in the Internet of Things ecosystem.
The transaction is part of SoftBank’s strategy to invest in companies laying the foundational infrastructure for the next stage of the Information Revolution. SoftBank believes OSIsoft embodies this strategy, having been a longtime leader in enabling large industries to harness the massive amounts of data generated by their operational technology assets.
As part of the investment, OSIsoft and SoftBank will collaborate on developing new products and services enabling digital transformation and the Industrial Internet of Things (IIoT), and strengthening OSIsoft’s presence in new markets.
“We are helping some of the world’s most innovative companies get smarter about how they use their data. Our industrial customers and utilities demand the highest levels of service and reliability. That’s why our focus has been, and will always be, on customer success and satisfaction,” said Dr. J. Patrick Kennedy, CEO and founder of OSIsoft. “Collaborating with SoftBank will enable us to bring the concept of data infrastructure to more markets like telecommunications as well as strengthen our commitment to our customers. We’re very much looking forward to working together.”
“Industrial IoT is a central force in the digitization of the industrial economy. OSIsoft powers the key underpinnings of this digital transformation. OSIsoft’s relentless customer focus and masterfully engineered data infrastructure solutions are a direct outcome of Dr. Kennedy’s vision and passion,” said Deep Nishar, Managing Director at SoftBank. “We are excited to partner with the OSIsoft team to help realize their mission in new markets and customer segments.”
SoftBank purchased the significant minority interest in OSIsoft from Kleiner Perkins Caufield & Byers (Kleiner Perkins), TCV and Tola Capital. Kleiner Perkins and TCV first invested in OSIsoft in 2011 while Tola invested in 2012. Last year, Mitsui & Co. became an investor in OSIsoft and maintains its ownership stake.
“OSIsoft is one of those rare success stories hiding in plain sight. The company’s PI System is pervasive across industries: you probably experience the impact of its technology several times a day without realizing it. OSIsoft has also cultivated an impressive and enthusiastic fan base of loyal customers,” said David Mount, a partner in the Green Growth Fund at Kleiner Perkins. “This has been a very positive investment for us and we’ve been very proud to be part of the company’s growth. SoftBank is an ideal partner to bring OSIsoft’s PI System technology to more markets and customers.”
“When we partnered with OSIsoft in 2011, it was already a forerunner in what we now call IIoT, a market that’s expected to reach over $120 billion by 2021,” said Jake Reynolds, General Partner at TCV. “During our 6-year investment in OSIsoft, we have been amazed at the company’s growing presence and importance in this burgeoning market. We are pleased that OSIsoft has the opportunity to partner with SoftBank during its next phase in making the world’s industrial infrastructure more connected, efficient, flexible, and sustainable.”
One of the world’s most widely-used technologies for digital transformation, OSIsoft’s PI System captures data from sensors, manufacturing equipment and other devices and transforms it into rich, real-time insights to reduce costs, improve overall productivity and/or create new services. Over 1,000 leading utilities, 95 percent of the largest oil and gas companies and more than 65 percent of the Fortune 500 industrial companies rely on the high-fidelity insights from the PI System to run their businesses. Worldwide, the PI System actively handles over 1.5 billion sensor-based data streams.
The company primarily focuses on six core markets—oil and gas; utilities; pharmaceutical development and production; metals and mining; pulp and paper; and water—and is rapidly expanding into food and beverage, facilities and transportation. Wind developers leverage the PI System to increase power production and reduce maintenance costs while mining operations use it to meet their goals for environmental compliance. Water utilities save millions of liters of water a day by pinpointing leaks within the PI System. The PI System can be found inside solar farms, breweries, data centers, cruise ships, leading research laboratories, power grids, stadiums and smart devices everywhere.
OSIsoft has also fostered an extensive partner ecosystem, including over 450 interfaces and connectors to translate and deliver PI System data to popular enterprise applications and cloud platforms. More than 300 hardware manufacturers, software developers and system integrators produce PI System-based products and services.
“We have examined many data-intensive companies who assert that they will change the face of enterprise computing leveraging big data. OSIsoft has been steadily transforming the industrial sector for decades using a quantum of data heretofore not even envisaged by competing companies. OSIsoft provides the essential bridge between operational and information technology,” said Sheila Gulati, Managing Director of Tola Capital. “Partnering with the OSIsoft team on their strategic growth has been a tremendously rewarding endeavor. The Kennedy culture puts people first through their unwavering commitment to their customers and employees: this has provided the basis for their winning formula.”
A long-time dream of enabling operators to see the profit impacts of process changes is a giant step closer to reality.
Much of my early career involved the intersection of engineering and profitability. No surprise that I valued my conversations with Peter Martin over the years. He has long been a proponent of just such technology and workflow.
Now at Schneider Electric (but still Foxboro), he has an organizational stability that may get the job done. Enter “EcoStruxure Profit Advisor.”
Developed through a partnership with Seeq, a leading provider of software and services that enable data-driven decision making, EcoStruxure Profit Advisor uses Big Data analytics to measure the financial performance of an industrial operation in real time, from the equipment asset level of a plant up to the process unit, plant area, plant site and enterprise levels. On-premise or cloud-enabled, it works seamlessly with any process historian to mine both historical and real-time data. It then processes that data through Schneider Electric’s proprietary segment-specific accounting algorithms to determine real-time operational profitability and potential savings.
Controlling Business Variables in Real Time
“While many companies are getting really good at controlling the efficiency of their operations in real time, they’re still managing their business month to month. That just doesn’t work anymore,” said Peter Martin, vice president of innovation, Schneider Electric Process Automation. “Business variables are changing so quickly—sometimes by the minute—that by the time companies receive updates from whatever enterprise resource planning systems they use, the information is no longer relevant to the business decisions they need to make or should have made. If they want to change the game, they need to control their other real-time business variables, including their safety, their reliability and especially their operational profitability. Profit Advisor allows them to do that.”
Because current cost accounting systems only measure the financial performance of the industrial operation at the overall plant level, it is difficult for companies to truly understand the financial impact—positive and negative—operational changes have on business performance. To address that need, Profit Advisor allows plant personnel to see and understand the ROI and business value their actions, activities and assets are contributing to the business in real time. It empowers the workforce to make better business decisions with a variety of data analytics, which can be displayed in various formats, to help drive operational profitability improvements, safely.
Innovating at Every Level to Deliver Value-focused IIoT
“Our customers are struggling with many issues, including the sheer speed of business and how to manage and use emerging technology to their advantage,” said Chris Lyden, senior vice president, Process Automation, Schneider Electric. “Everyone wants to talk about all this new technology without focusing on what value it can deliver. From our perspective, the digitization of industry is a real opportunity for our customers. We’re taking a value-focused approach to IIoT because we know our ability to innovate at every level can help our customers control their productivity and profitability in real time. That’s the only reason we should be talking about IIoT to begin with.”
Profit Advisor layers real-time accounting models onto the Seeq Workbench to become a scalable, repeatable and easy-to-implement solution for multiple segments, enabling customers to both measure and control their profitability. And because it can be integrated with Schneider Electric’s simulation and modelling software in a digital twin environment, users are further enabled to forecast profitability under different conditions or if changes to the operation are made.
Overall, the software provides
- Historical Data Review: Profit Advisor can evaluate the historical performance of the plant to assess its operational profitability, helping plant personnel analyze and understand how the
operation performed during different conditions. It enables the workforce to identify true performance-improving initiatives. And since it can be tied to individual pieces of equipment, it can provide that information down to even the smallest asset in the operation.
- Real Time Performance Indication: Profit Advisor can indicate current performance and inform plant personnel when their operating decisions are making the business more profitable. Actual ROI and return on improvements will be visible, enabling plant personnel to concentrate and refine their efforts to the actions that provide the greatest financial returns. It also enables plant personnel to determine which parts of operation are constraining operational profitability and accurately estimate the business value their decisions might actually create.
- Profit Planning: Profit Advisor empowers process engineers to predict the profitability of the changes they are proposing, which will substantially minimize project risk and help to eliminate waste.
Check out this YouTube video.
Schneider Electric, the global specialist in energy management and automation, has added a new enterprise-wide IIoT plant performance and control optimization software to its PES and Foxboro Evo process automation systems and Foxboro I/A Series distributed control system. Leveraging Expertune PlantTriage technology, EcoStruxure ControlAdvisor, a native smart decision-support tool, provides plant personnel actionable real-time operating data and predictive analytics capabilities so they can monitor and adjust every control loop across
multiple plants and global sites 24/7. The software empowers them to optimize the real-time efficiency of the process throughout the plant lifecycle and to contribute directly to improved business
Data Science has gotten us to the point of collecting servers full of manufacturing data. We can do some analytics. But there are miles to go before we sleep.
This press release crossed my email stream last week. I haven’t time to interview the founder–that will come later. But here is a teaser.
Data Science Pioneer Drew Conway Closes $2.5M in Seed Funding to Bring Machine Learning to Industrial Operations
New venture Alluvium delivers “Mesh Intelligence” to close the machine-to-human gap
Alluvium, developers of Mesh Intelligence solutions that harness machine learning insights for real-time applications in industrial use cases, today announced $2.5 million in seed funding led by investors IA Ventures, Lux Capital, and Bloomberg Beta. The machine learning venture is running pilot projects of its Mesh Intelligence technology in fleet management, and oil and gas, among other vertical industrial applications.
Alluvium aims to conquer one of big data’s greatest unsolved challenges for complex industrial operations with expert human operators. Alluvium’s breakthrough Mesh Intelligence solution frees the data from these proprietary systems, transforms it into rich information streams, and provides real-time insights to human operators for immediate action.
“The commoditized big data stack is fundamentally broken for complex industrial operations,” said Drew Conway, Founder and CEO at Alluvium. “Modern industrial assets and hardware are continuing to be instrumented by OEMs who have not considered how these heterogeneous streams of machine data should be leveraged in the overall workflow and data strategy of the organization. And the modern analytics ‘stack’ — where data is moved and crunched in back end systems — does not meet the real-time requirements of human operators at the edge.”
Conway, who earned his PhD at NYU, is a leading expert in the application of computational methods to social and behavioral problems at large-scale. He started his career in counter-terrorism as a computational social scientist in the U.S. intelligence community and is known for his venn diagram definition of data science as well as applying data science to study human decision making.
At the core of Alluvium’s Mesh Intelligence platform is unique technology for extracting data from all elements of complex industrial operations — tablets, sensors, as well as industry-specific assets — with no expectations of compute resources or network bandwidth. This breakthrough allows machine learning processing to occur at the edge of systems where human operators need data most — in-real time.
“The early days of big data were about capturing and storing the vast amounts of new information streaming from devices in manufacturing, transport, medicine and more,” said Mike Olson, co-founder and Chief Strategy Officer at Cloudera, and a seed investor in Alluvium. “As that technology has matured, the more important and more interesting problem has become: What can we learn from all that data? Alluvium is focused on extracting meaning from streaming data coming from hardware that instruments all sorts of industries. The company augments human expertise with its powerful machine learning technology to make customers smarter and help them operate better.”
Independent research and surveys show the massive economic opportunity for IoT and machine learning across industrial use cases. A report by Jabil found that “$1.9 trillion dollars of economic value could be created by the use of IoT devices and asset tracking solutions.” For U.S. oil and gas suppliers — an industry where Alluvium has had significant early traction — the daily cost of unplanned downtime at a refinery can reach $1.7 million per day, and the daily cost of unplanned downtime for liquid natural gas drillers can top $11 million per day. A recent McKinsey report found that “car data monetization could be as high as $750 billion by 2030” — which has far-reaching implications for fleet management. Analyst firm Gartner forecasted more than 6 billion connected devices will be in use worldwide in 2016 supporting more than $265 billion in services. And in a 2015 “Moving Toward the Future of the Industrial Internet” report by GE and Accenture, 84% of executives expected Big Data to shift the competitive landscape within the next year.
“Bringing machine intelligence into the physical world is an incredibly difficult task,” said Shivon Zilis, partner at Bloomberg Beta. “We were excited to back Alluvium because of their unique insights into how complex industrial systems could be transformed by predictive engines.”
Read Alluvium Founder’s Perspective on Starting the Company