IoT continues to deliver radical improvements in industrial enterprise operations. In fact, most of the IoT applications in production today address tried-and-true use cases like asset monitoring, energy management and predictive maintenance. In addition, IoT data is critical to creating the broad data-sets required for most AI/ML capabilities that can truly transform an organization. The benefits of these solutions are clear and measurable.
But realizing these benefits can be elusive since implementing industrial IoT solutions beyond an initial proof of concept (POC) can be challenging. Collecting and integrating data from hundreds of different asset types and thousands of devices from multiple manufacturers, then normalizing the data for application use can be a daunting task. According to Gartner, 85% of data driven projects (like AI and IoT) fail to move past preliminary stages, citing the lack of suitable data as a big factor.
Today, we’ll address one of the biggest challenges of scaling industrial IoT and AI applications: data acquisition and integration.
A single site might have hundreds of assets from a dozen different manufacturers. This alone can be overwhelming. But as many enterprises today operate multiple sites across the globe, the need for a unified data solution becomes even more critical. An initial proof of concept for an IoT application might include only a small subset of these assets. Once the POC is complete, the organization must scale the solution to monitor additional assets over multiple sites. However, as the business case is built, it quickly becomes evident that developing support for all the asset types and variations will take significant time and resources, negatively impacting the project’s ROI. In some cases, the roll out of the project is halted altogether.
Because data acquisition can be a significant stumbling block, many organizations turn to third-party solutions for data integration support. Especially when it comes to the acquisition and unification of industrial asset data, consider a solution that offers the following:
A manufacturing company wanted to reduce running cost and increase uptime for their main production line. This meant they needed to collect a different and much larger data set than what was already being captured from their conveyors, variable frequency drives and air handling units. As a complicating factor, the company would not accept any downtime from installing the new solution.
In order to solve the data collection challenge, they chose Omnio Edge. Omnio is a Copenhagen-based software company that specializes in the acquisition and unification of industrial asset data. The company was brought in by the consortium developing the solution. It was then decided to setup a gateway running Omnio Edge with pre-configured OT connectors during a short 30-minute company lunch break. As a result, the company is now collecting 300 million data points per day for its analytics solution – without creating any downtime due to the installation process.
The data unification capabilities from Omnio are now available with IBM Maximo Application Suite. Within the Maximo Application Suite, you’ll find an AI-powered remote asset monitoring application that improves the ability of maintenance teams to see, predict and prevent issues: IBM Maximo Monitor. Omnio integrates assets in a few easy steps and sends asset data in a unified format to Maximo Monitor. With these two systems working together, it’s significantly easier and faster to monitor more and more enterprise assets.
Discover how your organization can scale up enterprise asset management solutions faster and eliminate the challenges of asset data integration:
Explore ways to see, predict and prevent asset issues with AI-powered remote monitoring solutions