Every day, humanity generates 2.5 quintillion bytes of data. Every minute, users are sharing nearly 500,000 tweets, watching more than 4 million YouTube videos, and conducting more than 3.6 million Google searches. The incredible amount of data that we generate every day — whether that’s data generated by consumers on the web or within businesses — is what makes up big data.

When it comes to decision-making, human beings’ intuition often isn’t enough. The appeal of big data is that it can be used to make data-driven decisions based on the unprecedented, vast amounts of granular detail available to businesses and professionals. Understanding how to manage this tidal wave of information is what Jeanette Horan does for IBM as their Chief Information Officer. “We have all of these vast amounts of information,” says Horan in her Big Think Edge lesson, “and what we’re really trying to do is to figure out how can we make that information available in a way that’s consumable to our business such that it can be actionable?”

How IBM handles big data

As one of the world’s largest companies focusing on products in cloud computing, artificial intelligence, and analytics, capitalizing on big data is an integral part to how IBM runs their business. In her Big Think Edge video, Horan gives a primer on the most important big data practices that she regularly implements at IBM.

She discusses, for example, how to optimally deploy salespeople across an opportunity with big data; the difference between internal, external, structured, and unstructured data and what business problems each data type can be used to solve; and how marrying those types of data can offer richer insights into how businesses can overcome their challenges.

What big data can do for you

Established companies looking to gain insights into their customers and processes, as well as professionals hoping to gain relevant digital skills stand to benefit from learning how to leverage big data to gain an edge in the marketplace.

Consider the case of FleetPride, a company that delivers spare parts for heavy machinery. FleetPride’s supply chain is at once one of the most crucial and most complicated part of their operations. “To improve the efficiency of the entire supply chain,” said FleetPride’s director of advanced analytics, “we wanted to take the emotion out of strategic decision-making and let data do the talking. However, until recently, we lacked the in-house skills and the proper tools to access our operational data and turn it into insight.”

By centralizing their data, they were able to make use of big data solutions to improve their business. As an example, they were able to predict the likelihood of warehouse staff making picking mistakes — as a result of this, FleetPride’s managers simplified packaging labeling, and now 99.5 percent of their packages are error-free. But without first consolidating their data and analyzing it, the decision to simplify their labeling would not have been made.

Another example is UPS, which also had many disparate data sources they could stitch together. By combining data from corporate repositories, local repositories, spreadsheets, or even people’s heads, UPS trucks were able to drive 85 million fewer miles per year. They improved their efficiency even more by developing an algorithm to ingest data based on GPS, maps, the location of loading docks and package receiving areas, and other time-saving data to produce optimal routes between stops, saving the company billions of dollars.

The stratospheric improvements in efficiency that data-driven decision-making can bring have completely changed the business world. More and more companies are looking to incorporate a big data approach in their business model. That’s why in 2019, big data solutions brought in $189.1 billion in revenue — and they’re forecasted to bring in $274.3 billion by 2022.

As the world becomes more connected, big data is only going to get bigger. This is at once both a challenge and an opportunity — learning how to successfully manage and analyze these increasingly large and complex datasets will be difficult, but the rewards can be great.