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The 3 Differences Between Big Data and IoT

“Big data” and “IoT” are hot terms, and in Information Technology circles, it’s hard to talk about one without the other. Yet, despite their intimate connection, they are, in fact, two different technology trends. Here we breakdown how big data and IoT are different.

Two very different concepts

Big data, as its name indicates, represents massive amounts of data. But, that’s not all. In addition to volume, IBM data scientists have recognized big data to show variety, velocity and veracity.

Big data is a result of a variety of sources – social media, transactions, enterprise content, sensors and mobile devices, among many others. Velocity refers to the speed at which big data is collected. Every 60 seconds, there are 72 hours of footage uploaded to YouTube, 216,000 Instagram posts and 204 million emails sent. In regards to veracity, the data collected needs to be of good quality that is continuously updated in real-time. Analyzing big data can offer superior value to the companies and individuals who use it.

The Internet of Things (IoT), on the other hand, turns everyday “things” into smart objects. Fridges, watches, thermostats, cars, shipping containers, are outfitted with sensors that connect to the Internet and each other to collect and transmit data. This information can become big data when it is combined with information from other sources and meets the other dimensions defined above.

Different time sequencing

Big data is focused on the long-game. Big data collects massive amounts of data, but it doesn’t leverage the information to make real-time decisions. Instead, there is normally a lag between when the data is collected and when the data is analyzed.

For IoT, time is of the essence. It collects and uses data in real-time to optimize operations, detect security breaches, correct malfunctions and more. IoT analytics must include managing real-time streaming data, and making real-time analytics and real-time decisions “at the edge” of the network, says Bill Schmarzo, CTO for Dell EMC Services’ Big Data Practice.

Streaming data management must have the ability to ingest, aggregate (mean, median, mode) and compress real-time data from sensor devices at the edge. Edge analytics would automatically analyze real-time sensor data and render real-time decisions (actions) that optimize operational performance (blade angle or yaw) or would flag unusual performance or behaviors for immediate investigation (security breaches, fraud detection)

Diverging analytical goals

Big data analyzes mostly human-generated data in the pursuit of finding patterns in human behaviors and activity. To ensure certainty in any human-related patterns, an incredible amount of data from multiple sources over longer periods of time is required. This explains the longer lead time required for big data. It is for this reason that big data is used for long-term projects like predictive maintenance, capacity planning, customer 360 and revenue protection.

On the opposite spectrum, IoT aggregates and compresses machine-generated data from a variety of sensors that include RFIDs, fitness trackers, virtual reality devices, smart air purifiers and every other smart device. The goal in collecting this data through effective IoT device management is to track and monitor assets and be able to correct problems in real-time. For example, the sensors in a smart garbage receptacle will indicate when it is near capacity. This knowledge is then used to schedule a garbage collector to empty the bin.

Big data and IoT are different, but they are intricately linked. Used in tandem, IoT delivers the information from which big data analytics can draw the information to create the required insights – helping businesses not only react to problems as they occur, but predict them and fix them beforehand.

Case Study: IoT is redefining the customer experience. Nokia case study.

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