Data analytics, the brain of IoT
Mike Gualtieri, principal analyst at Forrester, hosted a panel titled Five Essential Analytics for IoT Applications, at Teradata’s PARTNERS 2016 conference in Atlanta, Georgia. He listed five essential types of data analytics that must be performed on IoT applications on both a macro and micro scale.
Teradata PARTNERS 2016
Before giving his list, Gualtieri explained that IoT apps have existed before the existence of the term “internet of things,” and have just been reclassified as IoT by their vendors. He provided some background on what defines an IoT app: something that has sensors and may or may not need actuators or controller. And something that is not truly smart without the brain that comes from the analytics you preform on data.
According to Gualtieri, IoT apps must:
- Learn individual device and systems of device’s characteristics and behaviors
- Detect context in real time
- Decide how to act in real time
- Adapt logic over time to improve application value
The principle analyst said that the problem with analyzing data from an IoT system is that most analytics are done much later than when data is created, and that there is a need for real-time analysis as well as batch analytics.
Enterprises must act on a range of perishable insights to get value from big data, according to Gualtieri. That is because that while data doesn’t get lost with time, but insights do. And some need to be acted on immediately.
“Most companies are wasting a lot of money on analytics that don’t get looked at or are looked at and used improperly or not at all,” Gualtieri said.
Essential analytics for IoT
Guarltieri gave his five types of analytics that are essential for IoT insight:
- Streaming analytics: detect and act on real-time perishable insights. Filter, aggregate, enrich and analyze high throughout of data from disparate live data sources to identify patters, detect urgent situations and automate immediate actions in real-time (In-memory (RAM)). Adoption is growing from 24% to 42% in 2014 to 2015, respectively.
- Example: Warn other drivers that the road is slippery to avoid a crash right now. Sensors detects wheel slippage and sends warning to car a mile behind that there is slippage up ahead. IoT can learn about that part of road so next time it warns the driver.
- Machine learning: Builds learned logic (predictive) models from historical data. Mostly done in batch mode with few exceptions. Expressed in different ways: AI, predictive analytics, data-mining, etc. Analyzes data from IoT system and produces models that predict outcomes or understand context with significant accuracy and improve as more data is available.
- Types of models: classifiers (predict characteristic), recommenders (recommend next best action), cluster (identify patterns).
- Spatial analytics: sensors detect what is happening at certain location. Tools, techniques and technology to understand the fixed and changing spatial relationship among physical objects. Location is critical. Historical sources: GPS, IP address, RFID, cellular.
- Time series analytics: All data has a timestamp. Batch processing of time series, looking for trend. Tools, techniques and tech that analyze time ordered data to find statistical patterns and/or forecast future values in time.
- Prescriptive analytics: improve your decision to act (or not). Success depends on the collective efficacy of decisions about customers, operations and strategy. Gives you the ability to rapidly model, improve and change decision logic.
To round things out, the speaker said that analytics for IoT are comprised of practices, processes and tech that use data, analytics, math, experiments and human insights to improve efficacy of decisions made by humans and/or by decision logic embedded in apps.
