The Industrial Internet of Things is on track to change the way businesses work. Embedding sensors into machinery and physical devices is only the beginning of the Industrial Internet of Things development process. To extract maximum value from the devices that IIoT solutions have connected to the digital world, business IT leaders must take a ‘big data’ approach. The data streams of sensor devices will be a critical point of differentiation to keep in mind when defining the business objectives of your IIoT solution.

Before even defining how to extract industrial analytics you will also need to have given data storage and management serious consideration. Once data is being collected and monitored, you can decide how to utilize the data to learn more about the system itself, anticipate potential problems and continuously provide added value to the overall system. There are many different tools available to analyze data generated by IIoT solutions. Which works best for you will depend on the amount of data, speed of the data and level of sophistication needed to interpret the data. If the system is designed properly—with the right types of administration tools, user interface and data storage—analytic tools can be added incrementally. Through this step by step process users will gain access to more complex analytics functions as they become more familiar with manipulating the system’s data.

Let’s take a look at the four ways IIoT apps can enable industrial analytics.

Data filtering

The rules of an IIoT app’s analytics engine should align with key industry- and business-specific metrics. Users should be able to easily customize the results of their analytics dashboard by adjusting filters that are based on these key performance indicators. This will allow them to extract data that is precise enough to answer their most challenging questions.

Data visualizations

Visual analytics is essential for an IIoT solution to support real-time decision making. The analytics dashboard of every user should include data visualizations that enable users to quickly identify data trends, outliers and anomalies, as well as any other critical data points. These tools require more than simple pie charts that can only parse through structured data. Visual analytics features that are interactive enable users to solve operational challenges that only business intelligence experts once could solve.

Predictive analytics

These features and functions are of particular importance for IIoT solutions that are focused on improving operational efficiency. Your data storage and management system will need to support real time data extraction and processing to maximize productivity gains. An Industrial IoT solution’s analytics dashboard can enable users to anticipate equipment failures, improve repair services, automate the industrial process and more.

Feedback loop

Users should be able to submit feedback to quickly identify and resolve bugs and ensure upgrades to the system meet users’ expectations. A process for managing incoming feedback should also be defined prior to launch to ensure that response is swiftly addressed without bogging down the IT function.

The many challenges that come with manipulating big data and optimizing information flow are not the only critical factors to consider when developing an IIoT solution. There are 9 key consideration factors in all that business IT leaders should address. Enrich your understanding of what enterprise leaders must consider when building an Industrial IoT solution by reading our blog series step-by-step and downloading this accompanying practical guide.


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