Making IoT Deployments Work Effectively with Existing Systems
In my previous two blogs, I discussed how businesses focus on deployable IoT solutions versus PoCs (proof of concepts) and the value of bringing intelligence to the edge. This time, I would like to look at the importance of combining existing enterprise data with an IoT data stream.
Most enterprises have multiple constituencies of infrastructure, applications, employees, customers, suppliers, processes and policies that are needed to run the business. Any new systems, including those dealing with IoT, need to be architected to fit within this context. The real value of IoT lies in combining the IoT-generated data with other enterprise data, but a key challenge is how to best integrate them.
The data integration challenge needs to be solved at several levels: data transformation (from one protocol to other), routing (getting data to where it is needed), manipulation and analysis. Let’s explore this further by looking at the example of data analytics, an integral part of the IoT solution. After all, what is the point of collecting the IoT data in a Hadoop data lake if we’re not going to derive business value from it. Before the data scientists can build data models to derive intelligence from the IoT data, it requires several considerations:
Read the entire article here, Making IoT Deployments Work Effectively with Existing Systems
via the fine folks at Red Hat.