What is the Relationship Between IoT and Big Data?

 

The Internet of Things (IoT) and big data are massive, complex ideas. While interrelated, they’re also distinct. The IoT consists of millions of devices that collect and communicate information, but big data encompasses a much wider landscape. To understand the relationship between IoT connectivity and big data, let’s first take a look at the role of big data and its key attributes. 

What is Big Data?

True to its title, big data means tremendous amounts of information. It comes from a variety of sources, from connected devices to clicks from online consumers. The units used to measure big data—petabytes, terabytes, and exabytes—reflect its overwhelming nature. While advances in computing technology have enabled organizations to collect big data sets, computers lacked the power to process such amounts of information until recently. Today, businesses and other organizations are starting to sift through their data in search of actionable insights that can aid decision-making. Professionals able to work with data sets are in high demand. They use modeling software and statistical analysis to extract patterns, performance information, and potential problems. These analysts are the translators who turn big data into useful reports.

Increasingly, artificial intelligence (AI) and machine learning/deep learning technologies are aiding the process of big data analysis. They can compile data from multiple sources and use it to predict outcomes and make recommendations. For example, video streaming services such as Netflix and Amazon remember the movies you watch and recommend similar titles for future viewing.

The Four “V”s

To aid understanding of such an enormous concept, data scientists at IBM popularized the four “V”s of big data: volume, variety, velocity, and veracity. 

Volume

The incredible amount of data collected today through sensors, online transactions, social media, and other mediums cannot be processed or even stored using traditional methods. According to some estimates, the accumulated volume of big data will be close to 44 zettabytes or 44 trillion gigabytes by 2020. Data sets are often so large that they cannot fit on a single server, and must instead be distributed between several storage locations. Data analytics software such as Hadoop is built to accommodate the need for distributed storage and aggregation.

Variety

Today’s data comes in a wide range of types, from social media posts to video clips. In past decades, data was more clearly defined—for example, phone numbers, addresses, or ledger amounts—and could be collected easily into spreadsheets or tables. Today’s digital data often cannot be corralled into traditional structures. Powerful analytics software seeks to harness unstructured data, such as images and videos, and combine it with more straightforward data streams to provide additional insights.

Velocity

Currently, data is collected at a mind-boggling rate of 2.5 quintillion bytes per day. From hundreds of thousands of social media posts to more than 5 billion Google searches per day, accumulated data is streaming into servers at a previously unprecedented speed. 

Veracity

Veracity refers to the truthfulness or accuracy of a particular set of data. That includes evaluation of the data source—is it trustworthy, or would it lead analysts astray? Poor data quality costs the U.S. around $3.1 trillion per year, so pursuing veracity is important. It includes seeking to eliminate duplication, limit bias, and process data in ways that make sense for the particular application or vertical. This is an area where human analysts and traditional statistical methodologies are still of great value. While AI is becoming more sophisticated, it cannot yet match the discernment of a trained human brain.

Big Data and IoT

In one sense, the IoT is a series of creeks and rivers that feed into an ocean of big data. The enormous collection of connected sensors, devices, and other “things” that represent the IoT—7 billion worldwide—is making a significant contribution to the volume of data collected. IoT use cases span a wide swath of uses and sectors, from agriculture to smart devices to machinery. Sensors are used for asset management, fleet tracking, remote health monitoring, and more. 

The tools created for big data and analytics are useful for corralling the influx of data streaming in from IoT devices. IoT-focused developers are creating platforms, software and applications that enterprises and organizations can use to manage their IoT devices and the data generated. 

Distinct but Complementary

While both big data and the IoT refer to collecting large sets of data, only the IoT seeks to run analytics simultaneously to support real-time decisions. For example, an e-commerce company might track consumer habits over time and use that data to create tailored content and advertising for the customer. But in the case of an autonomous car, data cannot be put aside for later analysis. If it shows an impending accident, the machine needs to know those results without delay so it can make a split-second decision. 

Many IoT devices rely on cloud computing, or communicating with a remote server, but in some verticals designers are applying the idea of edge data processing. In this model, the device retains power to process some data locally, ensuring minimal latency for time-sensitive operations.  

While the focus of IoT is more on the immediate analysis and use of incoming data, big data tools can still aid some functions. Predictive analytics, for example, considers a machine’s performance and service alerts over time, building the library of data needed to anticipate upcoming problems. That means companies can be proactive about servicing their equipment. For example, they can ensure that spare parts or service personnel are on hand before a machine breaks down. 

Types of data sources are another major distinction between the two. Big data analytics typically looks at human choices, especially in the online realm, in an effort to predict behavior and uncover patterns or trends. On the other hand, IoT is centered on machine-generated data, and its primary goals are machine-oriented—optimal equipment performance, predictive maintenance, and asset tracking, to name a few.

Common Goals

Big data and IoT are distinctive ideas, but they depend on each other for ultimate success. Both emphasize the need for converting data into tangible insights that can be acted upon. 

One example of IoT working together with big data analytics comes from the shipping industry. Shipping companies are attaching IoT sensors to trucks, airplanes, boats, and trains to keep track of speed, stops, engine status, and other information. They can use that data to make immediate decisions and to anticipate forthcoming maintenance, but they also store accumulated information to get a big-picture view of the company’s performance over time. Ultimately, this combination of immediate IoT insights and long-term big data analytics results in cost savings, improved efficiency, and better use of environmental resources. 

IoT and big data have an important relationship that will continue to develop as technology advances. Companies wishing to harness the power of data should carefully consider the devices they choose to deploy and the types of information they collect. Making an effort at the front end to gather only useful, applicable data—and designing internal systems to process it in sector-specific ways—will make the process of analytics that much easier.