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November 2015 • SMT Magazine 61 dustries. From health care to energy generation, from transportation to fitness equipment, and from building automation to insurance, the possibilities are virtually endless. In most of these industries, content (or the data collected) is not the problem. There are plenty of smart people collecting lots of useful data out there. To date this has mainly been an IT problem. The IoT is generating mas- sive amounts of data from remote, field-based equipment spread literally across the world and sometimes in the most remote and inhospitable environments. These distributed acquisition and analysis nodes (DAANs) embedded in other end products are effectively computer systems with software drivers and images that often connect to sev- eral computer networks in parallel. They form some of the most complex distributed systems and generate some of the largest data sets the world has ever seen. These systems need remote network-based systems management tools to automate the configurations, maintenance, and upgrades of the DAANs and a way to efficiently and cost-effectively process all of that data. Complicating matters is that if you reduce the traditional IT topology for most of the or- ganizations collecting such data to a simple form, you find they are actually running two parallel networks of distributed systems: the embedded network that is connected to all of the field devices (DAANs) collecting the data and the traditional IT network where the most useful data analysis is implemented and dis - tributed to users. More often than not, there is a massive fracture between these two parallel networks within organizations, and they are incapable of interoperating. This means that the data sets cannot get to the point(s) where they are most useful. Think of the power an oil and gas com- pany could achieve by collecting real-time data on the amount of oil coming out of the ground and running through a pipeline in Alaska and then being able to get that data to the account- ing department, the purchasing department, the logistics department, or the financial depart- ment—all located in Houston—within minutes or hours instead of days or months. Placing near infinite storage and comput- ing resources from the cloud that are used and billed on-demand at the fingertips of users pro- vides solutions to the challenges of distributed system management and crunching huge data sets of acquired measurement data. Big data tool suites offered by cloud providers make it easy to ingest and make sense of these huge measure- ment data sets. That said, the application of big analog data is the precursor to the rise of the Industrial In- ternet of Things (IIoT). By making machines smarter through local processing and commu- nication, the IIOT will solve problems in ways that were previously inconceivable. But as the saying goes, "if it was easy, everyone would be doing it." The complexity arisen from the three technological trends further accentuates the need for massive investment that no one company alone can make and concerted effort across the board. Bringing this vision would re- quire overcoming three key challenges: Security Both the systems and the communications within the IIoT need to be secure, or billions of dollars' worth of assets are at risk. Stan- dards bodies, consortiums, and co-ops, such as the Industrial Internet Consortium and the North America Electric Reliability Corporation (NERC), in many industries are working to de- velop the standards needed to ensure security, but there is still much work to be done. Com- mANAGING bIG DATA From AN ANALoG WorLD " by making machines smarter through local processing and communication, the IIoT will solve problems in ways that were previ - ously inconceivable. but as the saying goes, "if it was easy, everyone would be doing it." " FeaTure