Representative End User Clients
Representative Automation Clients
Representative Software Clients
With enough years in the process industry, some scenarios begin to look familiar, like déjà vu all over again. The promises of a new generation of technology follow a familiar cycle of hype, reality-check, and perhaps adoption. And even with adoption as a demonstration of success, the inertia of the installed base always wins in what becomes an evolutionary rather than a revolutionary transition.
History repeating itself seems to have been lost on many vendors in their evangelism of the wave of innovations besieging the industry. Greg Gorbach’s recent post does an excellent job of cataloging these revolutions under the banner of “analytics,” so I won’t repeat them all here.
But at the same time—in the context of a typical process plant where reality is defined by existing historians, data silos, and Microsoft Excel as the tool of choice for analytics—the long list of proposed innovations can be sound intimidating, overwhelming, and expensive to end users. Two things have compounded these issues.
First, the technologies —big data, predictive analytics, machine learning, cloud computing, IIoT or Industry 4.0, etc.—have eclipsed the narrative of benefit and impact. So the discussion of why should we adopt innovations has been replaced by a focus on what technology to use. Presumably the end point of the new offerings is improved outcomes in yields, margins, quality, and safety. But without an ISA standard to define what is and what isn’t machine learning or big data, or predictive analytics, the result is messaging long on technology enthusiasm and short on benefits and improvements to existing operations.
Second, the sheer pace of recent innovation means there has been too little focus on helping customers fit new offerings into their existing environments. Technology generations used to last decades, now it feels like months. As a result, customers hear they will have to move their data to the cloud, adopt new data governance and security models, hire data scientists with expertise in machine learning, hire developers for their big data, and make investments in the infrastructure required to support enterprise data lakes. By next week.
But starting with a typical process plant with historians and Excel, how does a customer get from there to here?
Enter Bill
So what does Bill Gates have to do with this? For those of you who participated in the PC revolution, do you remember the earliest days of Windows and the challenges for companies and partners that wanted to participate in the Windows ecosystem? Specific requirements included C++ programming expertise, full familiarity with the “Programming Windows” tome by Charles Petzold, and a week’s work required to build the most basic “Hello World” application. I remember it, and was fortunate to be part of the Microsoft Visual Basic team, the product that redefined Windows application development.
Instead of requiring a customer or partner to go through the disruption of hiring new people and building new skills, Visual Basic provided an evolutionary approach by introducing a new paradigm: Rapid Application Development. This approach established Visual Basic as the most popular development language for over a decade. As one reviewer said, “There is nothing like Visual Basic, and in the future there won’t be anything that isn’t like it”, and we continue to see this is true with the code and component libraries used today.
So could this happen again? Could the world of big data, predictive analytics, machine learning and cloud computing get turned inside out, from a revolutionary technology-centric approach to a user-focused and problem solving evolutionary approach? Put another way, instead of forcing customers to do lots of work to adopt innovations, can vendors instead deliver innovations in a process industry–specific application experience? And if so, what would the capabilities be of such a solution?
I believe we can, and I propose that we name this new software category “Rapid Insight Applications” (RIA), at least until ARC can come up with a better acronym. RIA is defined as a software application delivering on four requirements.
• RIA is an application – visual, interactive, intuitive – approach designed for use by existing employees, specifically plant engineers with the experience, expertise, and education to investigate alarms, write reports, and optimize production outcomes. An RIA empowers these users with productivity tools and features to help them assemble, cleanse, search, visualize, contextualize, investigate, and share insights from process data.
• RIA connects to customer data of all types as it is and where it is, instead of requiring the duplication or transformation of data. If manufacturing or business system data is on premise or in the cloud, or both, no problem. If data from field instruments is in one historian from one vendor, or in many historians from many vendors, that’s ok. The point is data silos may be bridged without requiring extensive extract/transform/load, data lake, or other investments.
• RIA enables access to innovations in hardware and software technologies in the form of features and transformative end-user experiences. There shouldn’t be a debate about if big data, predictive analytics, machine learning and scale out architectures are important components of modern software offerings: of course they are. Instead, the focus should be on how these innovations are delivered to end users as features and application experiences.
• Finally, RIA delivers a modern software experience to users and organizations. RIA should be easy to install, set up, and use. It must have low impact on IT organizations, and of course support collaboration among peers at one location or across the globe. And RIA deployment must be flexible, from on-premise workstations, servers and clusters to cloud platforms—and support the manageability, scalability, security, and extensibility required of modern enterprise software.
The benefits of implementing RIA include reduced costs to get started in achieving new business results with existing employees and systems through faster time to insights. Low up-front costs also mean faster ROI via quicker and simpler solution implementation. And once initial ROI is proved, follow-on investments can be made incrementally to build upon early successes.
As with the Windows and Visual Basic example, the RIA concept has been implemented before in the process industries with innovations such as PC-based HMI, where the hard work required to take advantage of innovation was turned around to be bring new technologies to existing users instead of vice versa. With RIA, hard work still needs to be done, but by vendors instead of end users.
This approach will enable process industry customers to achieve promised business results promised by the recent wave of innovations far sooner and at far lower costs.
About your Guest Blogger:
Michael Risse ([email protected]) is Vice President & CMO at Seeq Corporation, a company building productivity applications for engineers and analyst that accelerate insights from process manufacturing data. He was formerly a consultant in big data applications and platforms, and prior to that worked with Microsoft for 20 years. Risse is a graduate of the University of Wisconsin at Madison and lives in Seattle, Washington.
History repeating itself seems to have been lost on many vendors in their evangelism of the wave of innovations besieging the industry. Greg Gorbach’s recent post does an excellent job of cataloging these revolutions under the banner of “analytics,” so I won’t repeat them all here.
But at the same time—in the context of a typical process plant where reality is defined by existing historians, data silos, and Microsoft Excel as the tool of choice for analytics—the long list of proposed innovations can be sound intimidating, overwhelming, and expensive to end users. Two things have compounded these issues.
First, the technologies —big data, predictive analytics, machine learning, cloud computing, IIoT or Industry 4.0, etc.—have eclipsed the narrative of benefit and impact. So the discussion of why should we adopt innovations has been replaced by a focus on what technology to use. Presumably the end point of the new offerings is improved outcomes in yields, margins, quality, and safety. But without an ISA standard to define what is and what isn’t machine learning or big data, or predictive analytics, the result is messaging long on technology enthusiasm and short on benefits and improvements to existing operations.
Second, the sheer pace of recent innovation means there has been too little focus on helping customers fit new offerings into their existing environments. Technology generations used to last decades, now it feels like months. As a result, customers hear they will have to move their data to the cloud, adopt new data governance and security models, hire data scientists with expertise in machine learning, hire developers for their big data, and make investments in the infrastructure required to support enterprise data lakes. By next week.
But starting with a typical process plant with historians and Excel, how does a customer get from there to here?
Enter Bill
So what does Bill Gates have to do with this? For those of you who participated in the PC revolution, do you remember the earliest days of Windows and the challenges for companies and partners that wanted to participate in the Windows ecosystem? Specific requirements included C++ programming expertise, full familiarity with the “Programming Windows” tome by Charles Petzold, and a week’s work required to build the most basic “Hello World” application. I remember it, and was fortunate to be part of the Microsoft Visual Basic team, the product that redefined Windows application development.
Instead of requiring a customer or partner to go through the disruption of hiring new people and building new skills, Visual Basic provided an evolutionary approach by introducing a new paradigm: Rapid Application Development. This approach established Visual Basic as the most popular development language for over a decade. As one reviewer said, “There is nothing like Visual Basic, and in the future there won’t be anything that isn’t like it”, and we continue to see this is true with the code and component libraries used today.
So could this happen again? Could the world of big data, predictive analytics, machine learning and cloud computing get turned inside out, from a revolutionary technology-centric approach to a user-focused and problem solving evolutionary approach? Put another way, instead of forcing customers to do lots of work to adopt innovations, can vendors instead deliver innovations in a process industry–specific application experience? And if so, what would the capabilities be of such a solution?
I believe we can, and I propose that we name this new software category “Rapid Insight Applications” (RIA), at least until ARC can come up with a better acronym. RIA is defined as a software application delivering on four requirements.
• RIA is an application – visual, interactive, intuitive – approach designed for use by existing employees, specifically plant engineers with the experience, expertise, and education to investigate alarms, write reports, and optimize production outcomes. An RIA empowers these users with productivity tools and features to help them assemble, cleanse, search, visualize, contextualize, investigate, and share insights from process data.
• RIA connects to customer data of all types as it is and where it is, instead of requiring the duplication or transformation of data. If manufacturing or business system data is on premise or in the cloud, or both, no problem. If data from field instruments is in one historian from one vendor, or in many historians from many vendors, that’s ok. The point is data silos may be bridged without requiring extensive extract/transform/load, data lake, or other investments.
• RIA enables access to innovations in hardware and software technologies in the form of features and transformative end-user experiences. There shouldn’t be a debate about if big data, predictive analytics, machine learning and scale out architectures are important components of modern software offerings: of course they are. Instead, the focus should be on how these innovations are delivered to end users as features and application experiences.
• Finally, RIA delivers a modern software experience to users and organizations. RIA should be easy to install, set up, and use. It must have low impact on IT organizations, and of course support collaboration among peers at one location or across the globe. And RIA deployment must be flexible, from on-premise workstations, servers and clusters to cloud platforms—and support the manageability, scalability, security, and extensibility required of modern enterprise software.
The benefits of implementing RIA include reduced costs to get started in achieving new business results with existing employees and systems through faster time to insights. Low up-front costs also mean faster ROI via quicker and simpler solution implementation. And once initial ROI is proved, follow-on investments can be made incrementally to build upon early successes.
As with the Windows and Visual Basic example, the RIA concept has been implemented before in the process industries with innovations such as PC-based HMI, where the hard work required to take advantage of innovation was turned around to be bring new technologies to existing users instead of vice versa. With RIA, hard work still needs to be done, but by vendors instead of end users.
This approach will enable process industry customers to achieve promised business results promised by the recent wave of innovations far sooner and at far lower costs.
About your Guest Blogger:
Michael Risse ([email protected]) is Vice President & CMO at Seeq Corporation, a company building productivity applications for engineers and analyst that accelerate insights from process manufacturing data. He was formerly a consultant in big data applications and platforms, and prior to that worked with Microsoft for 20 years. Risse is a graduate of the University of Wisconsin at Madison and lives in Seattle, Washington.