The Internet of Things (IoT) is set to generate vast amounts of new data, with some predictions citing that over 100 billion “things” will send out data over the Internet by 2020. Without new analysis methods, this sheer volume at best risks the value of data being lost and, at worst, may overwhelm a business completely. To make sure we create value from all this data, we need to concentrate on generating insights that businesses can use.
So what is an insight in IoT?
Insight is defined as “an accurate and deep understanding” of something. In IoT, insights use data to generate an accurate and deep understanding of the processes that drive value across a business. These insights can influence key business decisions and enable feedback in a way that was previously impossible.
In looking at what insights are needed, a business needs to first understand where it has a gap in its knowledge that is costing money, reducing efficiencies or preventing new markets from being explored. This could be anything from being able to calculate when it is cheaper to replace a piece of equipment rather than continue to maintain it, to understanding why some regions perform better than others.
How does IoT insight apply in the real world?
To illustrate this, let’s look at a building’s HVAC (heating ventilation and air conditioning) system. These systems always seem to break down just when you need them most: air conditioning systems in late spring, and heating systems in autumn. If you look at failure statistics, this is actually the case, as problems occur when the stress on a system increases.
Keeping the HVAC systems in buildings and offices running is the job of companies contracted to service and repair the systems. Imagine being the CEO of such a company. You would want to minimize the seasonal breakdowns to reduce the number of service teams required. From a business perspective, it would be better to perform servicing throughout the year and schedule visits based on when a service is required. This requires insights. Let's walk through the insight development that an HVAC company might need.
Before working out how to generate the insight, you need to understand the goals.
In this example, the first goal is to keep the number of service teams at the optimal level for the number of buildings. The second goal is to drive additional revenue through enhanced services. These goals define the required insights. Understanding where to start for the first insight requires analyzing existing data using advanced analytics from reported faults. Let’s say 40% of issues are found to be mechanical (e.g., loss of refrigerant, failure of refrigerant pump and blockage of condenser) and 60% electrical (e.g., lightning strikes and power surges).
Analyze the data to deliver meaningful insights.
Analysis is then needed to understand the key reasons for the mechanical faults in terms of serviceable items, age of items before failure, location and time of year. Simple parameters can be measured (e.g., refrigerant pressure, pump vibration, oil condition, power consumption and airflow) and used to feed health-check data into the system, with a risk/cost weighting to show which factor has the greatest cost to the service provider. In this case, for certain types of units, the refrigerant pump is NOT field replaceable and requires an entire unit replacement (often lifted by crane), so this is the highest risk item.
For electrical faults, the analytics are more complex and require the use of machine learning algorithms using information on power quality in an area and the incidence of lightning strikes, as well as type of equipment, electrical test results and any protection equipment installed. From these, it is possible to generate a risk profile for any site that would indicate susceptibility to power-related faults and the risk of such an event in a 12 month period.
Get the basics right, and business benefits will follow.
As this example demonstrates, without adding any “things,” there can be rich insights into costs and risks from combining internal data with external sources. Most importantly, you can get a clear indication of the critical parameters to monitor to accurately calculate when a service is due and the potential failures if service does not happen.
Additionally, monitoring systems can be fitted to sites where there are significant risks. Thresholds can be set to visit sites for a service, and new data can be gathered to create new insights on the effectiveness of the service offerings. Once sufficient field data is gathered, new insights can be used to accurately calculate the projected cost of service for units, and calculate on-the-spot quotations for new service contracts. The insights on projected service requirements also allow discussions on the projected lifetime of a unit, and the opportunity to have robust data to show when buying a new unit is cheaper than continuing to service an old one.
It’s like new for old.
From a simple beginning, the development of such insights can mean that HVAC systems are like having a secondhand car, but with the fixed cost servicing and variable service intervals that a new car would provide. The insights allow the HVAC company to offer new products, and give building owners more accurate data for answering their service, fix and replacement questions.
Insight creates IoT value.