With ever-increasing IoT data, using ETL to provide real-time analysis won’t cut it. A mainframe, though, can process your data without a hiccup.
Companies today face incredible challenges around compliance, security and analytics, as their data lakes fill with invaluable information from ever more sensors. And tomorrow’s challenges will be no easier. As the digital age expands to cover all facets of our lives, more and more computing power will be necessary to process all of the data created.
Take the explosion of Internet of Things (IoT) as an example. We have only sampled the benefits that the IoT can provide. In the words of Dan Mitchell, a retail analytics industry expert with SAS, IoT can be fundamentally described as “a network of connected physical objects embedded with sensors. IoT allows these devices to communicate, analyze, and share data about the physical world around us via networks and cloud-based software platforms.”
The concept of devices collecting data and sending it somewhere to be analyzed is not new. Heart monitors have been used for decades to collect and send data to cardiologists about the health of their patients’ hearts. But in a data-driven world, collecting data is only one part of the equation; the capability of analyzing this data in real time takes IoT to an exciting level. But the amount of data the IoT creates already, and will create in the future, is truly staggering.
Luckily, the speed and capacity of the mainframe, analyzing data across your entire enterprise in real time, means the mainframe can process your IoT data without a hiccup. I’ll explain what I mean.
The internet of where we buy our things
In the brick-and-mortar retail industry, tools such as mobile apps, Wi-Fi tracking, RFID inventory tracking, and traditional in-store infrared foot-traffic counters all contribute to a retailer’s data lake for the purpose of understanding their consumers and providing a customized shopping experience. But providing the connected consumer with a frictionless shopping experience means analyzing the data in your lake in real time to target your consumers with features such as in-store sales ads.
Some of the data in your lake is perishable and should be leveraged immediately while the consumer is still in the store, while other data is timeless and will provide insight for future use. But the question becomes this: How do you make sense of the data being collected by so many connected devices?
All your water in different lakes
Specific devices often store their data in specific databases. With so many devices collecting data, it’s not unusual for a retailer to end up with multiple databases, each warehousing different types of data, stored both on the mainframe and on a myriad of distributed platforms. The challenge then becomes how to design a process that enables data scientists to access IoT data in a manner that facilitates real-time analytics.