Analytics and intelligence play a key role in the overall IoT system, where collected data is turned into information and augments business decisions with actionable insights. Data collected from IoT devices can help enterprises reduce maintenance costs, avoid equipment failure, improve business operations and perform targeted marketing. Synchronizing an enterprise IoT ecosystem with core engines, such as artificial intelligence, machine learning, predictive analytics and digital counterparts, will be the key to unlocking the potential of IoT at enterprise level.
IoT is all about the data flowing between devices and gateways and to central platforms. According to Gartner research, there will be 25 billion connected things connected to internet by 2020. The amount of data generated by these things will be unimaginably huge. Finding pieces of useful information over this pile of data will be nothing less than finding a needle in a haystack. This is where artificial intelligence will play a big role, filtering that huge cluster of data which will result in intelligent business friendly insights.
In one of the top use cases, the IoT and AI combination can be in the field of security. Artificial intelligence can be used to determine regular access patterns in any vulnerable environment to help security control systems to avoid any security failures.
It’s often necessary to identify correlations between a large number of sensor inputs and external factors that are rapidly producing millions of data points. Considering the frequency at which IoT devices generate data, a computing technique that can make best use of this information becomes inevitable. The evolution of the IoT and machine learning combination is the result of millions of data points generated at a great frequency by IoT devices. Machine learning works on huge amounts of historical data to produce cognitive decisions. Thus, the combination of IoT with machine learning becomes a great enabler of business optimization.
Predictive analytics and maintenance can create huge impacts on business economics using IoT data. Predictive analytics facilitate automated, consumable replenishments in the consumer segment. Using predictive analytics based on IoT data, failures and downtimes of the machinery in manufacturing facilities can be prevented. Enterprises can mitigate the damaging economics of unplanned downtime. According to statistics, using predictive analytics can reduce maintenance costs by 30-40%.
Using digital replicas of physical entities and systems as part of an IoT architecture allows organizations to start simulations and compensate ecosystems as and when needed. Digital twins can generate predictions and insights into the operations of machines which will be very useful when part of existing business processes. Triggering appropriate remedial business processes and workflow is one of the purposes of digital twin projections.
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