Imagine you run an energy company with wind turbines spread out across the United States. It would be vitally important for you to have access to a variety of information, such as turbine location, maintenance schedules and requirements, and over a hundred different sensor readings every thirty seconds. All this sensor data, known as “machine data,” is driving the fast growing Machine-to-Machine (M2M) market. In fact, new technologies that make it easier to capture and process M2M data have created huge opportunities for manufacturers to streamline their overall operations, increase efficiencies and reduce costs.
The number of M2M connections will double over the next eight years to 50 billion globally, according to mobile operator association GSMA. Industry analyst firm IDC predicts that machine-generated sensor data will increase to 42 percent of all data by 2020. The increase in M2M connections and advances in technology will provide new opportunities to extend the value of machine data. As machine connectivity continues to increase, organizations will look for new ways to gather actionable insights from their data.
I predict that, over the next few years, we will see a slew of solutions and products built to solve M2M problems in the enterprise. It’s what we at JackBe call Machine-to-Enterprise (M2E). The true value of M2M is about more than just gathering the sensor data from individual machines; it’s about connecting that sensor data with other sources and empowering business users to gain visual insight into the data so they can react to changing conditions that impact the business as they happen.
We will see organizations merging machine data with other data streams, then layering powerful analytics on top of that data. In the manufacturing space specifically, M2E can be leveraged to combine real-time machine data with historical or transactional information to assess trends over time. To go back to our wind turbines example, accessing machine data in real time would provide you with a huge range of data related to the turbines’ operation, from electrical output to scheduled maintenance.
These are the kinds of actionable insights that will enable employees to quickly detect and address changes that could impact the business. We think of M2E as the “last mile” in delivering intelligence from machine data directly to the business user.
Organizations can combine all of this information into dashboards and create charts, graphs and maps to monitor machine diagnostic and performance data in real time. This allows the organization to have a complete view of all the machines in its network, but that’s only a fraction of the value. These dashboards will provide employees with the information to react to issues in real-time, or even ahead of time, delivering predictive maintenance, improved service and engineering, and ultimately lower maintenance costs.
But having insight into the machine data is only one part of the whole M2E picture. It’s also important to have insight into external data in order to predict potential issues and keep the system running efficiently. Traditionally, machine data would be able to tell you that a certain turbine was not producing as much energy as it should be. Before you can solve the problem, you have to understand the context. Perhaps the turbine needs maintenance. A real-time dashboard that visually shows you information about the machine’s maintenance schedule or highlights visual patterns would equip you with the information necessary to plan ahead. In addition, real-time awareness of weather patterns in key areas would give you valuable insight. For example, your dashboard could combine data from a real-time wind map with your machine data. What that might reveal is that the turbine’s lower electrical output might simply be due to the fact that there was less wind in the area at that time.
This is just one example, but we will see situations like this occur more and more as machines become increasingly connected to the business. Organizations will leverage real-time analytics to gain a better view of how their machines are performing and will see huge benefits in terms of operational efficiencies.
