Creating a Path to Faster ROI - High Productivity Platforms for Industrial IoT

It's not enough to understand one piece of the puzzle - the real ROI comes when you can quickly develop the kinds of applications that deliver complete solutions.

One of the biggest dampeners for Internet of Things (IoT) projects today is explaining the Return on Investment (ROI). Since software development can take years before the first set of outcomes surface, stakeholders have very little to show against millions of dollars of investment - which can cause a lot of distress.

Application development in the IoT world has become a less buzzy technology to talk about these days. There are sensor deployments, real-time analytics, edge-computing, communication protocols, AI and ML, all of which have stolen the limelight and are sucking up a bulk of the costs associated with these projects. However, what good is a predictive model if the only people who can understand the outcome are data scientists? 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?

These problems can be solved by incorporating high-productivity platforms in the architecture, which help development teams prototype and develop solutions faster.

What are High Productivity Platforms
High productivity platforms are built based on years of research and execution and incorporate best-of-breed frameworks. They are integrated with a focus on results-oriented iterations rather than lengthy software development cycles. Results can be generated faster and then be tested with the end-user, which can keep them involved.

How Can High Productivity Platforms Help?
One of the reasons for the failure of Industrial IoT initiatives has been the focus on individual technologies that solve a tiny piece of the puzzle, rather than looking at the big picture and solving for that. What this leads to is a big bunch of disparate technologies with nothing to tie them together, and hence vague answers to unknown questions and a lot of under-utilized data.

The key is to have a multi-layered integrated architecture with business-friendly packaging on the frontend, which ensures that the Field Engineers can make decisions based on it. To do this, a seamless application architecture is as important as all the smaller pieces under it. High productivity platforms are designed in such a way that integration is easy. Also, they have built in modules that can be tweaked and tuned without too much coding. This reduces the effort needed to build the entire architecture reduces by many times.

The need of the hour is to come up with mini-production like solutions that can tie-in together and show an end-to-end picture to the consumer of the analysis. The focus needs to be on fast development centered around a defined problem statement and use of already available technology platforms to solve a business case, rather than on hoping for an outcome using a DIY strategy.

An ideal high-productivity framework for solving an IoT problem would look something like this:
1. No-code data connectivity into sensors and process systems to integrate in a data lake on the cloud.
2. Solution-oriented machine learning approaches which can identify and surface patterns with minimal rebuild and high reusability.
3. Easily configurable business-driven rules engines which can consume the pattern recognition outcomes and surface them in a way that end-users understand it.
4. Integrated low-code mobile application framework that can provide authentication, engagement through push notifications and business logic integration to be surfaced on multiple devices.
5. Highly engaging web and mobile apps which talk the language of the field engineer and assists them in making better decisions faster.
6. A plug and play smart chatbot which gives the engineer the ability to get standard assistance at any time and anywhere.

In summary, an outcome-driven IoT strategy is backed by the ability for complete solutions to be up and running in a fast and iterative manner, and not just small individual pieces which don’t make sense as a whole. Companies tend to spend far too much time mastering one side of the spectrum without thinking that there will be no ROI until all the pieces fall into place.

Abhishek Tandon leads solution engineering initiatives, traveling all over the world to explain how Progress DataRPM can help.