Four myths about IIoT data strategy that manufacturers still believe

Myths around the challenges of implementing IIoT systems and building smart factories have made the prospect of adoption unnecessarily intimidating. In some cases, industrial organizations have avoided the most effective IIoT implementations available to them simply due to false understandings of the technology. While IIoT adoption does require a new approach to managing and analyzing data collected in real-time, this isn't as difficult an obstacle as many have been led to believe. Let's take a look at four common myths about IIoT systems and the realities behind them: The traditional databases that most industrial organizations already have in place (Microsoft SQL Server, Oracle, etc.) are wholly inappropriate for use with IIoT systems, given the tremendous volume and complexity of data in question. When industrial businesses mistakenly implement IIoT infrastructures using traditional databases (and this happens often), they soon discover them to be expensive to scale, unable to process the vast amount of incoming data, or incapable of handling the more complex queries required to realize the IIoT's benefits.