How IoT Helps in Monetizing Your Data through Predictive Analytics

The synthesis of a complex world, the huge escalation of data and the extreme desire to stay at the forefront of competition have prompted industries to concentrate on utilizing analytics for driving strategic business decisions. IoT is a significantly growing industry that will continue to advance in every sector of life. However, here we are focusing on a specific area where IoT is developing considerably right now that is predictive analytics.

Data and analytics can play a big role in minimizing inefficiency and simplifying business operations. If IoT is the strong pillar of IT infrastructure, IoT Analytics is the solution that will help you to generate meaningful insights from the huge data that comes out every day. According to a  study by Gartner, by 2020, “more than half of big new business processes as well as systems will include most of the elements of the IoT”. IoT systems require Analytics for proper functioning, as crucial decisions are taken by enterprises based on such analytics.

A completely automated IoT Analytics helps in utilizing real-time data to look out for specific patterns and send alerts to the concerned teams. From allowing businesses to make consumer-oriented marketing decisions to assist them to tackle key operational inefficiencies, analytics is completely changing the conception towards the importance of data.  The utilization of data analytics in IoT investments will enable the business units to acquire an insight into customer preferences and choices. This would tend to the growth of services and offers as per the customer requirements and expectations. This, in turn, will enhance the ROI earned by the organizations.

Predictive Analytics Use Cases

When we remove data from things or machines, the data generally rests in a database that sometimes goes of no use. Whereas, with the power of IoT and predictive analytics, we are capable to put that unused data to utilize and make real-time business decisions that optimize profits.  Let’s take a look at few examples of how we can utilize predictive analytics to monetize data.

Managing risks through analytics

One of the major keys to using data and analytics is risk management. Industries today are subjected to huge risks from structured data- likewise databases and unstructured data. IoT enables organizations to handle risk and monitor whether or not we move ahead with things like a loan application, insurance, or even stock bets.

We are capable to connect sensors and networks to be able to get insightful data that can be assembled, categorized and monitored for the strategic benefits of the business. By leveraging risk analytics, organizations can realize themselves in a better position to measure and estimate risk. Managers should see risk analytics from an enterprise point of view and should build ways to pull data across various organization levels and functions into one central platform.

If we are capable to handle our data to reduce the risk involved in our operations, then IoT and predictive analytics become a robust and valuable tool for an organization’s complex processes.

Predictive Maintenance

The IoT is having an extreme effect on the manufacturing sector, leading to improved automation, more organized operations and the generation of valuable new business models. While the application of digital technologies can carry benefits across the value chain, it is perhaps in the area of predictive maintenance that the most remarkable impact can be derived.  This manufacturing process involves heavy and costly machinery that is a key part of the complete process.

If a machine suddenly breaks down, the entire process getting disturbed, resulting in a strike to revenue and an increase in costs. IoT can incorporate into these machines and send crucial data and information back to a cloud. The utilization of sensors and data analysis means companies can recognize patterns of equipment condition and performance and precisely estimate when a failure might occur. Such caution eliminates unplanned downtime, delivering significant productivity benefits.

For instance, when there is any pipeline damage, IoT technology can recognize this and gives an alert, triggering maintenance that there may be a potential failure. This can be considered at the end of the day or in the middle of operations where productivity draws up and costs start rising up.

Demand Sensing

Businesses are executing demand-sensing solutions to be able to interpret and plan according to changes in the market. If the information is utilized precisely, enterprises can reduce the number of inventory shortages or unused of any kind.

In addition to that, we can interpret what promotions to utilize along with what areas will have a positive or negative demand reaction to basic indicators. Generally, this type of data will open up the opportunity to make decisions that improve efficiency and thus enhance profits for the company. Demand sensing tends to better replenishment planning (that means the exact inventory in the exact locations), makes it more probable that the products customers are looking for will actually present on the shelf (and the customer won’t look for a competitor’s product and for a new preference) and increases promotional planning.

There are two distinct factors to understand how we precisely evaluate the demand for a given time or place. There are some internal as well as external data factors to consider. Some internal factors are not restricted to, current orders, promotions as well as customer service. We can also utilize external factors likewise competitors and weather current events. Along with these elements we utilize machine learning and IoT to transfer data to a cloud, monitor it and come out with the best prediction of demand to utilize for real-time business decisions.

By embedding data analytics into the business core strategy, managers can simplify internal business processes, recognize unfolding consumer trends, understand and monitor arising risks and build mechanisms for continuous feedback and improvement. Driving analytical transformations will thereby allow organizations to get a competitive edge and remain at the forefront of digital disruption.