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Four Reasons Why Network-Integrated Data Management Makes Sense

As more and more organizations are adopting an IoT strategy, effectively harnessing and managing data from the sensors and machines is becoming a critical piece to ensuring overall IoT deployment success.

For data management to be effective, enterprises must be able to acquire and integrate data from these machines and sensors into multiple on-premise and cloud-based business applications. In other words, getting the right data into the right applications at the right time. There are three primary methods for building a data management application:

  • Building data management into the sensor or machine software itself.

  • Transporting all data into the cloud and building data management applications in the cloud.

  • Integrating data management into network infrastructure.

Here are the reasons why integrating data management into the network infrastructure makes the most sense:

  • Preserves the functionality and security of both existing and new machine investments. Most IoT deployments involve integrating both new and existing machines and sensors, all of which are built on different hardware architectures and firmware. Moving data management into the network infrastructure eliminates the complexity in modifying the software of each and every sensor or machine.

  • Reduces transactional cloud costs. Simply put, not all data needs to go to the cloud. Given that cloud based hosting and storage are based on volume and frequency, enterprises can quickly find themselves developing cost heartburn by sending all data to the cloud to be managed. Network-integrated data management enables data to be processed at the right location (locally, to an on-premise application or in the cloud) creating greater efficiency while reducing cloud costs.

  • Eliminates manual data management. While the average human being creates under just 1 GB of data a day, a single sensor in an industrial machine can generate up to 6GB or more in the same time. This makes manual data management an impossible task; not surprisingly, Forester estimates that up to 73% of data generated in the enterprise goes unused and untapped. A rules-based network-integrated data management approach can allow for changing machine and sensor states to be automatically acted upon because let's face it - just putting data into a dashboard isn't enough.

  • Enables IoT data management at the point of performance – the Network. Putting the responsibility for enabling data management on directly on a sensor or machine doesn’t make sense when you consider that device OEMs have a limited visibility of how their device may be deployed in an IoT deployment with heterogenous devices. Transporting all the data into the cloud to be sorted can get expensive and still will not eliminate the need for IoT developers, system integrators and IT admins to develop custom cloud applications that address the business objectives and goals of each IoT deployment on a project-by-project basis. Simply put: IoT data management is a business enterprise problem and as such, solving it is most efficiently done at the network level.

Here at Machinechat our focus is on helping IoT solution architects and developers Cross the IoT Chasm by making the task of enabling network-integrated data management easier with ready-to-deploy software that allows them to configure data management workflows within the network in minutes versus months spent developing custom programming.

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