Successful IoT projects start with understanding these seven components of an Internet of Things-based system.
Ask an analyst about the emerging technologies with the greatest impact on the IT market over the next decade, and a common answer will be IoT.
The Internet of Things (IoT) is not a single technology but a collection of technologies, processes, and devices that promises to drive major changes in most organizations. IoT is about using data insights and automation to drive business decisions.
Think about a system that could gather real-time data about a train's pending maintenance while the train is traveling at 200 miles per hour. This could significantly reduce unplanned train maintenance and downtime, trim unexpected costs, provide greater alignment of resources and people, and drive increased revenue and profitability. These kinds of business opportunities indicate why analysts predict the IoT market could reach into the billions or possibly trillions of IoT sensors and devices. Bringing new data efficiency and other data analytics, the IoT market is also predicted to generate trillions of dollars.
In general, IoT systems consist of seven essential stages and technologies. To manage successful IoT projects, you should understand these components.
Not all IoT solutions will use all seven of these stages, and some will jump between stages based on the solution and the business needs. As such, these stages are a general guideline of what projects will require.
One of the most common IoT scenarios is that of predictive maintenance—the ability to prevent an equipment failure through proactive management. The concept of predictive maintenance is that if an organization were to know a piece of equipment would fail because of a faulty part, replacing the part in a timely manner could eliminate costly downtime.
While the promise of predictive maintenance exists, most organizations lack the necessary data insights or volume of data. Predictive maintenance requires a significant amount of data to understand failure patterns compared to normal operations. And to compound the challenge, many organizations don’t collect data in timely-enough intervals to be of use by an IoT system.
Even when an organization can collect an adequate amount of data often enough to be valuable, the business may nonetheless lack the insights underlying the algorithms to support complex event-processing systems.
So the most important starting point in an IoT project is the cold-path analytics stage. In this stage, all the data must be loaded into a machine learning solution with a team of data scientists carefully using the data to identify data patterns. This process is iterative: An algorithm will be developed and then tested against the data. Testing will often identify further changes required to properly detect data anomalies. Complicating this further, these steps must be completed for each IoT sensor and device so the health of that part of the equipment is understood.
Once the data patterns are understood and the algorithms created, the ML models can be connected to the hot-path analytics/complex event processing systems and edge computing systems. Edge and hot-path systems with ML models can then apply real-time data insights and alerts.
When getting started with IoT, an organization should begin with a data project. Consider the business goals and objectives. A common mistake is having business goals that are mismatched with the frequency of data gathering. If a goal requires, for example, real-time insights, but the equipment provides data only every few hours, the goal conflicts with operations. Likewise, having only a small amount of historical data isn’t sufficient to create machine learning models to support business goals.
Successful IoT projects start with an end goal. IT managers and business executives alike should be able to answer questions such as these:
Rarely do most organizations have the right data collection, the right sensors, and the right tools in place to meet the business goals. Organizations should start with the data and sensors available as initial starting points and then add additional sensors and devices and expand the data as subsequent steps. This will create the initial cold-path analytics and ML models. These models can then be expanded and enhanced as additional data and sensors become available.
If you’re trying to establish a successful IoT project, consider these questions in the planning and initial pilot phases:
One of the challenges in getting started with IoT technology is getting past the “things” aspect. IT practitioners should look at the specific business problem to be solved. Business goals should also be broken into smaller pieces to demonstrate the value a technology can provide.
Returning to our example of proactive maintenance for a moving train, the ultimate business goal is to prevent maintenance from disrupting service. Solving a specific problem requires many sensors, a significant amount of data, and deep knowledge of every piece of equipment.
If one or two smaller goals can be tested, though, it could establish the business case for continued investment. In the case of the train, identifying a single accelerometer might be valuable to identify operators that provide a smoother riding experience. Identifying just this part of the total train ride and process would open operator training opportunities and improve customer experience. Once an initial business case is proven, additional sensors and “things” can be added to expand insights.
Ultimately, successful IoT projects focus on the business and how IoT can drive innovation. These technologies, combined with processes, policies and analytics, can reduce operational costs, improve safety, and drive profitability while also driving business offers that help the organization stay ahead of the competition.