The Internet of Things (IoT) is a mediocre phrase. It’s not creative or clever, and it doesn’t exactly roll off the tongue. It is only because the phenomenon’s potential is so great that it can get away with such a lackluster name. Every day, more machines, sensors, and devices are talking to data systems, and eventually, to analytics users. This data is complex, vast, and fast-moving. The IoT represents a new approach to thinking about devices and data generation, and so a new analytical approach is required as well.
Aberdeen examined companies with the ability to collect, integrate, and visualize data generated by the IoT. These “IoT organizations” leverage the glut of information generated by diverse sources to better understand operations and overall performance.
A new analytical approach requires the right tools to get the job done (Figure 1). IoT organizations are 47% more likely than All Others to have interactive data visualization tools. As data pours in from connected sources, analysts can fully engage with the information and drill down from intriguing findings to develop deeper understanding. For example, a plant manager can engage a visualization of machine performance to determine if output is maximized, or compare data on different machines side by side. An IT analyst can investigate infrastructure performance data to find the root of problems, or identify areas that desperately need additional investment. Still, more than half of IoT organizations only work with static visuals. Their analysts and decision makers must make do with only the top layer of information. Further data discovery requires IT requests or inefficient workarounds. Aberdeen’s report, Interactive Data Visualization: The Age of “Look but Don’t Touch” is Over, explores the advantages of enabling interactivity for data analysis and exploration.
Analytical Tools for the IoT
Seventy-nine percent (79%) of companies that analyze data from the IoT also have the ability to incorporate location / geo-spatial data. Only a quarter of all other organizations consistently work with location data. Marrying IoT data to location data enables contextual analysis to paint a more complete picture of operations and customer interactions. Analysts can pin IoT sources to a map and identify proximity correlations or geographical outliers. By knowing what is happening and where at all times, decision makers can closely monitor and improve their spheres of responsibility. Aberdeen’s Location Analytics: Putting the Evolution of BI on the Map, examines the numerous data sources top performers enrich with geo-spatial information.
Finally, IoT organizations are 83% more likely than All Others to have predictive analytics. Making sense of the endless flood of IoT data is not intuitive and calls for substantial analytical firepower. Analysts can extrapolate what IoT sources will be telling them tomorrow, next week, and next year based on today’s data. Visualizing these predictions enables decision makers to compare multiple what-if scenarios and make the best possible choice for the future, right now. Going back to the plant floor manager example, predictive analytics can be applied to machine data to set maintenance schedules and fix small problems before they become big ones. Predictive analytics can also aid in the implementation of an IoT environment, as analysts run simulations to determine where to place sensors for data collection. Past Aberdeen research has addressed the surprisingly slow pace of predictive analytics adoption in the market, even in the face of a clear return on investment (ROI).
If the Internet of Things could apologize for how clunky its moniker is, it would. It would also tell organizations that the same old analytical practices simply won’t cut it anymore. The growing IoT will deliver an unceasing crush of data to organizations every single day. Collecting and storing data from the IoT should be the first steps on the road to impactful presentation and robust analytics. IoT organizations offer a blueprint for success with this swelling technological ethos.
Learn more about data visualization in Interactive Data Visualization: The Age of “Look but Don’t Touch” is Over