In this special guest feature, Steve Stover of Predixion Software shares his thoughts about what is the best path for realizing the potential of IoT. Steve is currently the VP of Marketing and Product Management with Predixion Software. Over the last 20 years, Steve has provided product and technology leadership to deliver Big Data and Analytics solutions at companies including Dell and Teradata.
Across the globe, half of all enterprises have implemented or are planning to implement an IoT (Internet of Things) business initiative, according to Forrester Research. In the high tech manufacturing sector, about 66% of enterprises are investing in IoT. With its dramatic business benefits, and advances in big data and analytics, it seems almost inevitable that if you are looking for a competitive edge you will be looking into IoT.
But what is the best path for realizing the potential of IoT? Unfortunately, there is no standardized cookbook for building an IoT application. You need to create your own recipe by first identifying a specific, meaningful business goal you would like to achieve. And then find the best way to leverage your IoT data with advanced analytics for connected assets in order to generate insights and actions that achieve that goal.
One business goal often associated with IoT is reducing the costs and increasing the value of equipment and machinery. The combination of IoT sensors, data and real-time analytics enables organizations to use predictive maintenance to achieve that goal.
Instead of risking costly downtime by repairing failures after they occur, or spending resources on unnecessary preventive maintenance and parts, organizations can anticipate precisely when and how to most efficiently maintain equipment. In this way they can significantly reduce capital expenditures, increase utilization and value, and avoid the cascading effects of critical equipment failure across the organization.
The key to achieving IoT benefits in cases like this is placing analytics “close to the edge” where sensors and data meet. This is where the real business impact takes place. This is where the organizations can use IoT to reduce costs, minimize risk, increase revenue and innovate.
However, placing analytics close to the edge can be challenging. Traditional analytic tools cannot handle the volume or speed of IoT edge data. In addition, many devices and machines are in remote areas with limited or periodic connectivity. Aggregating or filtering the data and then forwarding it creates latency and data blind spots.
Critical IoT use cases require real time actions performed on the edge.
One characteristic of enterprise software needed to embrace the needs of IoT is that it be an advanced analytics platform expressly for IoT that can run on the device, on the gateway and in the cloud – so the analytics are exactly where you need them, when you need them in real time. Further, it should empower organizations to rapidly create, adapt and improve predictive models for specific IoT applications. It should also streamline and simplify the process of implementing IoT initiatives, eliminating the need to understand the inner workings of machine learning such as shaping, managing and transforming data.
Most importantly, the software should provide the critical capability to produce insights and actions at the edge, whether that’s on a mobile device, in an app or dashboard, or on a device or machine. So no matter what recipe you choose for your IoT initiative, you can generate rapid results and meaningful business value.