Solution Architect's Guide to IoT, Part 2: Automate Data Management

Updated: May 24, 2019


This is Part Two of a three-part blog series addressing how industrial OEMs can accelerate time to revenue and broad adoption of their IoT solutions.

"Data is the new oil." --Kevin Plank, Founder & CEO of UnderArmour

Data is the new competitive advantage and IoT is about gaining access to your operational assets and data. To participate in this digital transformation, OEMs from across all industrial and commercial market segments - from commercial building security systems to industrial water filtration pumps to hospital equipment - are under pressure to develop connected solutions.


In Part One of this series, we discussed the different approaches development teams could use to add network connectivity. But adding network connectivity to machines is just the start. For an IoT project to deliver meaningful ROI, data must be transformed into actionable insights to meet the goals of reducing operational costs, increasing productivity, improving customer outcomes and delivering new paths to revenue.




Given that the volume of data created by machines is much greater than is possible for humans to manage and analyze, development teams must build intelligence that allow for machines to automatically direct the right data at the right time to the right application (or applications) so that appropriate actions or analysis can be performed. Data management applications play a critical role in automating this step.

Today, IoT solution architects, OEMs and system architects have three methods of addressing this:


Build data management into the machine itself.

Most industrial machine and sensor platforms were not designed with the processing capabilities to run multiple applications. For OEMs, incorporating a data management application into their machine design often results in them having to choose between two equally expensive and costly options:


  • Investing years and additional cost in redesigning field-proven machine hardware and firmware to operate in an IoT world; or

  • Scrapping decades worth of product design work to create a new IoT platform from scratch – forcing their customers to rip and replace entire ecosystems and risking the potential loss of a long-time customer to a competitor.


Given that not all enterprises are the same - and the sheer volume of customer enterprise scenarios - the result of either option is an incomplete solution that can require OEM field support teams and their system integrator partners to spend additional months and resources to address integration and deployment issues.


Direct data into the cloud to be sorted out in cloud-based applications.

Directing data into the cloud still requires that solution architects and system integrators build multiple APIs per device type that allow machine data to be connected to cloud-based applications. Not all data belongs in the cloud and this can quickly become an expensive proposition for end-user customers or system integrators who must deal with on-going storage, transactional and other cloud costs. In addition, additional programming may still need to be done to integrate data from heterogeneous devices and cloud-based applications so that data visualization of the entire enterprise can be realized.


Develop data management intelligence at the network edge.

Just as moving networking complexity out of the device can eliminate development complexity, offloading IoT data management into the network at the edge enables solution architects to preserve the robust functionality of an existing machine platform, eliminate design complexity and at the same time, cost-effectively and efficiently handle data from heterogeneous machines within the enterprise. Components of an effective edge-based data management solution are:


  • Data mapping that allows for data to be packaged and sent to one or multiple applications or stakeholders.

  • Data scheduling that allows for select data to be transmitted as needed.

  • Ready-to-use, customizable data visualization tools that allow for data to be drawn from multiple machines, sensors, sources and applications

  • Customizable rules-based engine for automating certain machine actions and notifications under specific datasets

  • Data storage and data lifecycle management

By bringing data management intelligence to act as the ombudsman translator between the OT machine interface and the IT-based enterprise and cloud applications, IoT solution architects can enable faster IoT deployment and achieve greater ROI.


Machinechat is focused on designing next-generation ready-to-use data management solutions that enable OEMs, system integrators and MSPs to Cross the IoT Chasm and bring the next billion devices online - faster, smarter and more successfully. Want to learn more? Click here to sign up for our newsletter.

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