High-productivity architecture creates a faster path to ROI for IIOT - IoT Agenda
IoT in 2017: Retrospective lessons from the field – IoT Agenda
One of the biggest dampeners for industrial internet of things projects today is the lack of ROI. The journey from device to analytics to results can take years, and stakeholders have very little to show against the millions of dollars of investment, causing them a lot of distress.
A finished IIOT mechanism is a sum of many parts, such as sensor deployments, device-to-cloud integrations, communication protocols, security, analytics and application interfaces that need to be designed and integrated. Unless all of the pieces come together, the business objective will not be fulfilled. What’s the point of having a cloud strategy and storing petabytes of data when the recipient of the analysis does not get a glimpse of the outcome? What good is a predictive model if the only people who can understand the outcome are data scientists?
High-productivity architecture is the answer to many problems
Another aspect that affects ROI is the time to first outcome. Due to the large number of mini-projects within a large IOT project, the first outcome — which decisions can be based on and tested — can take several months, if not years, to observe. The large quantity of investments that go into IoT projects have a chance of nothing to show if the technology pieces do not fit well. This is a major cause of concern for executive sponsors, as they are subscribing to a potential blackhole.
Organizations can solve these problems by incorporating high-productivity platforms — which support straightforward management and automation of connected devices — in the architecture, which helps development teams prototype and create a product faster.
High-productivity architecture (HPA)
Rather than talking about one application platform, the idea of high-productivity architecture stems from the fact that a series of platforms need to be interconnected with independent modules integrated together in a way that solves the IoT problem in totality. For example, the IoT reference architecture by Azure and the C3 Integrated Development Studio both use highly productive platforms to build high-productivity architectures. The modules for device integration, data storage, analytics and consumption integrate with a focus on result-oriented iterations rather than lengthy software development cycles. Modules automate repetitive tasks at scale to remove the complexity of DIY and still ensure that technology teams can tweak them to suit their case and reach a shorter time to first outcome.
The key to the success of a high-productivity architecture is having a modular, repeatable, automated, integrated and business-oriented approach to problem solving. This ensures faster turnaround, easier buy-in and the ability to integrate with outside technology seamlessly, saving the effort to build the entire architecture from bottom-up. Moreover, lean teams can deliver greater progress in a short period of time, creating savings. The focus needs to be on fast development centered on a defined problem statement and use of already available platforms to solve a business case rather than hope for an outcome using a DIY strategy. This is where high-productivity architecture can really help.
Stacking devices to data to analytics
The typical high-productivity architecture that organizations use to solve an IoT problem would look something like this:
No-code for sensors’ data connectivity and process systems to integrate in a data lake on the cloud.
Easy to configure business rules engines to create event warnings based on prior knowledge.
Solution-oriented machine learning approaches identify and surface patterns with minimal rebuild and high reusability.
Integrated low-code application framework to connect to data sources and devices, provides secure authentication, engages services through push notifications and integrated business logic.
Highly engaging web and mobile frontend to talk the language of the field engineer and assists him in making faster and better decisions
Each of the modules are essential for a working IoT mechanism and have a level of automation which will expedite the integration and prototyping of the entire technology.
High-productivity architecture is designed to ensure that energy and investment is spent on deriving value for business. It is designed for faster iterations and more focus on outcome. Finally, it has a shorter learning curve for technology teams and employs the highest quality technologies so that there is no compromise.
High-productivity architecture creates a faster path to ROI for IIOT - IoT Agenda
IoT in 2017: Retrospective lessons from the field – IoT Agenda
One of the biggest dampeners for industrial internet of things projects today is the lack of ROI. The journey from device to analytics to results can take years, and stakeholders have very little to show against the millions of dollars of investment, causing them a lot of distress.
A finished IIOT mechanism is a sum of many parts, such as sensor deployments, device-to-cloud integrations, communication protocols, security, analytics and application interfaces that need to be designed and integrated. Unless all of the pieces come together, the business objective will not be fulfilled. What’s the point of having a cloud strategy and storing petabytes of data when the recipient of the analysis does not get a glimpse of the outcome? What good is a predictive model if the only people who can understand the outcome are data scientists?
High-productivity architecture is the answer to many problems
Another aspect that affects ROI is the time to first outcome. Due to the large number of mini-projects within a large IOT project, the first outcome — which decisions can be based on and tested — can take several months, if not years, to observe. The large quantity of investments that go into IoT projects have a chance of nothing to show if the technology pieces do not fit well. This is a major cause of concern for executive sponsors, as they are subscribing to a potential blackhole.
Organizations can solve these problems by incorporating high-productivity platforms — which support straightforward management and automation of connected devices — in the architecture, which helps development teams prototype and create a product faster.
High-productivity architecture (HPA)
Rather than talking about one application platform, the idea of high-productivity architecture stems from the fact that a series of platforms need to be interconnected with independent modules integrated together in a way that solves the IoT problem in totality. For example, the IoT reference architecture by Azure and the C3 Integrated Development Studio both use highly productive platforms to build high-productivity architectures. The modules for device integration, data storage
, analytics and consumption integrate with a focus on result-oriented iterations rather than lengthy software development cycles. Modules automate repetitive tasks at scale to remove the complexity of DIY and still ensure that technology teams can tweak them to suit their case and reach a shorter time to first outcome.
The key to the success of a high-productivity architecture is having a modular, repeatable, automated, integrated and business-oriented approach to problem solving. This ensures faster turnaround, easier buy-in and the ability to integrate with outside technology seamlessly, saving the effort to build the entire architecture from bottom-up. Moreover, lean teams can deliver greater progress in a short period of time, creating savings. The focus needs to be on fast development centered on a defined problem statement and use of already available platforms to solve a business case rather than hope for an outcome using a DIY strategy. This is where high-productivity architecture can really help.
Stacking devices to data to analytics
The typical high-productivity architecture that organizations use to solve an IoT problem would look something like this:
No-code for sensors’ data connectivity and process systems to integrate in a data lake on the cloud.
Easy to configure business rules engines to create event warnings based on prior knowledge.
Solution-oriented machine learning approaches identify and surface patterns with minimal rebuild and high reusability.
Integrated low-code application framework to connect to data sources and devices, provides secure authentication, engages services through push notifications and integrated business logic.
Highly engaging web and mobile frontend to talk the language of the field engineer and assists him in making faster and better decisions
Each of the modules are essential for a working IoT mechanism and have a level of automation which will expedite the integration and prototyping of the entire technology.
High-productivity architecture is designed to ensure that energy and investment is spent on deriving value for business. It is designed for faster iterations and more focus on outcome. Finally, it has a shorter learning curve for technology teams and employs the highest quality technologies so that there is no compromise.