As IoT multiplies data volumes, how should your approach to data management and analytics change?
Overview
The most advanced corporations are building a central nervous system for digital information, including IoT. This allows them to listen to data to sense micro-trends, conduct analysis and make real-time autonomous decisions with little or no human intervention at massive scale.
Modern automobiles carry multiple computers emitting dozens of kinds of data every day. Smart meters are providing electricity data which turns into flexible rate plans so consumers can manage usage costs on hot summer days. By using in-store sensing beacons, retailers are learning how to follow customers through the store aisles in order to provide a better shopping experience. Transportation companies are using sensors to conserve fuel, track assets, and optimize routes. If the rise in connected devices – the Internet of Things (IoT) – tells us anything, it’s that Big Data is here to stay and is only going to get bigger.
Sensor data is the next new rich data source. Imagine engineers analyzing sensor data to discover root causes for hard disk drive failures. Great! But imagine if that sensor data is analyzed alongside other corporate data assets. Now, the company has the ability to predict which customer shipments contain those defective parts.
The big return-on-investment (ROI) happens when sensor data is viewed in context with the rest of the business. But line-of-business managers and technologists must start by asking the right questions:
Trillions of bytes of sensor data can - if used correctly - dramatically increase profits and enable new business models. Success with IoT starts with the ability to capture, sort and analyze the information at unprecedented scale. Consider how these Teradata leaders and early adopters are using IoT today:
Heavy goods trains burn a lot of diesel fuel. With sensors on the tracks and locomotives, the rail company is able to match locomotive speed to location. Throw in some analysis, and the top speed for fuel efficiency can be provided to locomotive engineers based on the engine type and cargo weight. Just one percent savings in fuel can be worth $46 million.
For a roadside rescue service, sensors in service vans mean they know exactly where their repairmen and spare parts are at any given time. So, instead of routing a repairman 20 miles to headquarters to get a spare part to finish a job, the service dispatcher can do an inquiry that shows the part needed is only five miles away in another service van. A short drive later, the repair is finished, and the technician is on schedule for his next appointment.
One major healthcare provider streams patient behavior and sensor data from its magnetic resonance imaging, radiography and ultrasound imaging equipment to enable its field service personnel to predict device failures on expensive machines weeks in advance. This meant repairs could be scheduled in off-hours, minimizing any impact to patients.
Data and analytics may be the most critical toolsets for Volvo’s “Designed Around You” strategy. Nearly 90 percent of all Volvo cars are connected, with customer permission to give Volvo high-value information so they can provide drivers with safer cars and tailored services.
The most advanced corporations are building a central nervous system for digital information, including IoT. This allows them to listen to data to sense micro-trends, conduct analysis and make real-time autonomous decisions with little or no human intervention at massive scale.
Getting the most out of IoT requires a multi-dimensional approach: