IoT Developers May Need Time Series Data Analysis Skills

Today, IoT devices gather data at an ever-faster clip. But understanding the data generated and finding meaningful patterns amid the torrent remains an obstacle to successful Internet of Things implementations. 

The fast-arriving data streams can take the form of time series data of varied types. It must be handled in sequence, and it is updated in small but continual amounts. 

Time series data types are familiar in geological research, processing, manufacturing and other industrial settings, but  developers, now charged with digital transformation, often lack time series know-how.

IoT device data could soon engulf organizations, and software developers who are expected to create useful reports, and real-time visualizations and automated feedback loops with IoT sensor data. They must also often do so with limited tooling or experience. 

In the future, developers may need to learn elements of data science, and even AI deep learning – while still maintaining expertise in advanced programming – in order to succeed in time series data analytics, according to industry viewers. Yet, some say, AI is not always required, and could be overkill, in some instances. 

Driving activity is the data flood that is growing as devices multiply. Market researcher Statista estimates IoT connected devices worldwide will reach 75.44 billion by 2025, five-times the number of such devices in 2015. 

Understanding IoT Sensor Traits

Industry participants must acquire new skills to meet the data surge, according to Dan Lluch, principal marketing engineer at mathematical computing software maker MathWorks.

“In recent years, it has been much easier to add sensors to operational systems and have data streamed to remote locations,” Lluch said. “The cost of adding sensors is low, and the required infrastructure is now ubiquitous. The major challenge is the availability of expertise to realize value from the data streams.”

Lluch said developers need to gain an understanding of basic characteristics that different sensors display, to build useful analytics on top of applications.

“Data sampling rates are generally tied to the action needed on the system, which in turn impacts the analytics,” Lluch said. So, he continued, while it may make sense to take multiple samples per second for something such as heart rate measurements, it does not make sense to take multiple samples per second for ocean tidal measurements.

The sensor data type influences the implementation of analytics, according to Lluch, depending on whether, for example, the data of interest is a single data point, such as a temperature value, or is vector data, as may emanate from a chemical analyzer. Also calling for different approaches to analytics is RGB pixel data, which is often the key identifier in computer vision applications.

Manufacturing plants, supply chains and tiered service and maintenance offerings have become active users of time series technology that creates a “fusion of data” from multiple sensor types, Lluch said. So, developers should expect to mix and match different sensor data in time series dashboards.