IoT strategy, IoT implementation, IoT solution, IoT project, IoT and machine learning
3 weeks ago

When the expected impact of a recent innovation is so widespread as is the case with the Internet of Things, it is very easy to climb on the moving train, often unaware of where it will take you. As the applications of the IoT revolution pervade more industries every day, the urge to implement IoT in your own company might begin to surface. While this might be the ideal innovative push your company might need, it is imperative to strategies and plan the process of integration for numerous reasons. While the IoT universe is expected to connect more than 50 Billion devices by 2020, the fundamentals of a successful IoT strategy are the same, irrespective of the industry in which it is being applied. Here is a stepwise guide to perfecting the IoT strategy most applicable to your industry.




The first step is to define the problem or question that the IoT strategy will attack and try solving. “You have to start with a question and not with the data,” stresses Andreas Wiegand, lecturer at UC Berkeley. The fact that data gets collected is a good thing,” he adds, “but what we really need is to figure out what problems we can solve with it.” This is the base of all operations solely because of the sheer data that is generated as we go on connecting more and more devices on the internet. The IoT revolution’s backbone is the collection, analysis and interpretation of data (both real-time and contextual) in order to gain insight and set consequent behavioral parameters. Without a specific question like “How can I make my supply chain for product X in market area Y more efficient?”, one is prone to be overwhelmed by the sheer data that will be generated in the implementation of IoT architecture. Ideally, the IoT strategy must include both - a short-term and a long-term aim. A short-term goal must be optimized in order to test the viability of the architecture designed, additionally helping in implementing new technology and architecture on a small scale to get a better idea of its operation. Thus, the building block of a sound IoT strategy is the question and its related outcome. Once this is set, the expected outcomes and metrics for the same can be determined.




The process of establishing the relationship and the solution is imperative in generating the blueprint of the architecture that would formulate the backbone of the IoT strategy. Once the path to the solution is clear, one can go on to determine the collection of hardware, software and equipment (like Sensors, actuators, adaptors, and bridges) that may be required in order to achieve the set goals. These hardware and software combine to create a connected platform that can be monitored and maneuvered by executives. This would give the organization a clear idea of all the requirements for a successful integration, along with specific insights into the IoT ecosystem partners that would be required to connect all the dots.




The impact that IoT technology will have on your business is entirely dependent on the choice of metrics and data points that are made. Sensors attached in various “things” generate massive amounts of data that is collected, analyzed and interpreted. Some data requires real-time analysis whereas others need to be processed based on contextual data. A smart car, for example, might require different types of analysis based on the function: Engine health and efficiency would consist of real-time data analysis, whereas fuel efficiency and consumption analytics might require contextual data analysis based on historical data of the same. Thus, defining the exact data points and metrics might make or break your IoT strategy.




As discussed previously, different goals require different paths of analytics. While the data for engine temperature and fuel gauges in a smart car would require real-time analysis, data for engine efficiency and fuel consumption might require data interpretation based on contextual data. Pipelines created for the real-time analysis of data points are often referred to as Hot Paths, while data points for long-term batch processing are included in the architecture of Cold Paths. Sensor data that needs to be aggregated, processed and analyzed to find historical trends and patterns is integrated in the data pipeline which is specifically called Cold Path Analytics. Determining specific data points and metrics for Hot Path Analytics and Cold Path Analytics is critical to the IoT strategy as it would segregate the nature of data analysis within the IoT architectural framework.




While it is imperative to optimize hardware and software connectivity, security and data formats, a successful IoT strategy critically banks on the scope of extensibility. This is because the real value of IoT technology will be manifested over time in the intelligent algorithms and actionable insights. This is the feature that will activate the intelligent features of decision-making and actions specific to the product or device. Thus, in the IoT architecture’s Decision Intelligence Pipeline in the data analytics framework, there must be room left for adaptive analysis, machine learning and extensibility (associated with expanding analytics to larger frameworks in the future). This is what will allow the real beauty of IoT and machine learning to step forward and start making decisions based on contextual and real-time data.




While this step may sound a bit compulsive, it is very important to develop a Proof of Concept (POC) before the implementation and finalization of IoT strategy. The POC helps in validating the technological feasibility of the IoT solution. The development of a POC also associates to starting off with a short-term goal, as it allows technology and business staff to identify technological challenges early in the developmental process and therefore optimizes the final solution. This is something that is heavily stressed upon specifically because of the novelty associated with the implementation of IoT technology. out strategist in the IoT sector would suggest starting small, and implementing the technology in phases, so that staff gets used to the metrics, while also testing the architecture for any flaws or drawbacks. Expanding after perfecting the architecture is much easier than encountering problems on an extensive scale and trying to find solutions simultaneously. Ensuring the proper functioning and viability of the IoT solution is the sole purpose of the POC, and it should certainly be undertaken right before the implementation of the IoT project. Once the POC is created, a architectural blueprint should be ready, giving a clear picture of the entire project to the staff involved in implementation (See reference architecture image).


With a stepwise approach to perfecting your IoT strategy, you minimize the chances of technological hiccups both in the architecture of the IoT strategy, as well as the backend of data analytics which includes how well the staff understands and implements data processing. Be assured to be happily surprised with the impact of the IoT implementation if you follow these steps to perfect your IoT strategy.

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