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Five Types of Analytics of Things – Deloitte

Article Synopsis :

The data captured through Internet of Things devices will fundamentally change the way insurers protect customers.

 The Digital Insurer reviews Deloitte’s Report on Five Types of Analytics of Things

Automated analysis of IoT data is the next frontier in analytics  

“Five Types of Analytics of Things” by Tom Davenport of Babson College (via Deloitte) highlights new approaches to interpreting IoT data to help insurers move beyond descriptive to predictive & prescriptive forms of analytics.

The five types of analytics as described in the article are:

  1. Descriptive analytics: The most commonly used form of analytics, comprised of collecting data and visualizing it in the form of mainly bars and charts. Requires human expertise and is, most of the time, only so meaningful.
  2. Diagnostic analytics: Based on regression models and data validations, this form of analytics requires a strong base to support growing sources of data. Building the right data model is important before moving on to predictive, prescriptive and automated analytics.
  3. Predictive analytics: Predictive analytics are increasingly well known primarily for one application: predictive maintenance. Companies that install sensors in equipment, and then use diagnostic models to learn what sensor data are associated with product problems or failures, can then create predictive models that suggest when failure is likely and what should be done to prevent it. These applications are typically found in industrial applications like gas turbines, windmills and locomotives, but firms are also using them in building elevators and point-of-sale devices. The health care equivalent of predictive maintenance uses medical device data to help predict the onset of serious health problems in humans or animals
  4. Prescription analytics: Think of prescriptive analytics as recommendations—analytical models that decide the best course of action and then inform a human about it. They may involve optimization models (e.g., the best price to maximize profit on a product), scoring models or predictive models. The human recipient of the recommendation normally has the ability to accept or reject it.
  5. Automating analytics: Given the need for human attention in other types of analytics, and the vast amounts of data that will be generated by IoT, automation of decisions and actions is an obvious direction for the field. There will be way too few humans to make decisions on all the data and analyses coming from IoT, so we’re going to have to automate many processes involving its use. Analysis of medical device data will ultimately lead to automated injections of certain drugs. Analysis of server farm data will generate automated reboots. Analysis of traffic data will change streetlight patterns automatically. We’re not there yet with any of these IoT domains but we’ll get there eventually. Automated and highly networked systems already in place include financial markets and the energy grid.

A multi-step analytics approach helps organizations understand how to progress through the five phases of maturity. Key is not to despair about how far you have to go, but simply to try to advance from where you are. If you’re stuck on line and bar charts, try to use that data to run a diagnostic regression equation. If you’ve done that already, try predicting something—and so forth. The sooner you start employing more sophisticated analytics the closer you are to deriving real value from the Internet of Things.

Link to Full Article:: click here

Digital Insurer's Comments

With the proliferation of wearable technology and connected devices the volume of information is increasing at a staggering pace. Digital insurers are aggregating this data building strong analytical models to proactively shield customers from risk. Leveraging IoT in business processes and decision making can forever change the relationship between insurers and insureds by:

  • Improving customer satisfaction
  • Proactive risk management
  • Saving on claims cost
  • Real time risk assessment and dynamic risk pricing

This article serves as an excellent primer for executives in search of a framework to guide analytics investments, all the way from present-state customer analysis to IoT.

Link to Source:: click here


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