Data is one of the most valuable resources that any organization has at any time, but many organizations are still failing to monetize it properly. This is often due to reliance on legacy systems that simply can’t handle the requirements for big data today. Big data is a popular—and sometimes intimidating—term, but you don’t really need to be a data scientist to understand it. Big data is basically just a way to describe large and constantly growing volumes of data, from both structured data and unstructured data sources, that can’t be analyzed through traditional data methods.

Thanks to digital transformations, organizations are dealing with more data than ever and trying to rely on manual analysis, or even the manual transfer of data between disparate data systems simply doesn’t cut it anymore.

Utilizing big data properly can help you gain insights into customer behavior, improve your business processes, help you make better decisions, and improve the customer experience, just to name a few applications. Before you really get into breaking up data sets and using them in data analytics, however, you’ll need to understand the five basic characteristics of big data and why they matter.

Volume: This is the sheer amount of data that an organization receives. Data volume began to explode in 2012, thanks to a widespread adoption of digital transformations, and these days, it’s estimated that volumes of data double roughly every 40 months.

Velocity: This is the speed at which data is generated, and this is just as important for big data technology as volume. If you can’t collect and analyze data at an appropriate speed, you won’t be able to keep it accurate and current.

Variety: This is the full spectrum of data sources that organizations collect big data from. Data can come from financial transactions, social media interactions, customer behavior, smartphone use, and any number of other sources.

Veracity: This refers to the accuracy and quality of data sets. In order to monetize data effectively, you have to know that the information from all your systems is accurate. That’s why it’s important to integrate your systems and use master data management techniques in big data analytics.

Value: Ultimately, the value of data is attributed to how many insights you can gain from it and apply it toward decision-making. This is the best use of big data, and all your efforts should be concentrated on increasing value. Here are just three real-life applications of big data.

1. The Financial Industry

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Big banks often use big data sets for risk analytics to determine if a loan is a good idea and to set terms for it. The Securities Exchange Commission (SEC) also uses big data to monitor and analyze activity in various financial markets. Thanks to network analytics and natural language processing back by machine learning, it’s possible to identify illegal trading activity.

2. Supply Chain Efficiency

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One of the most common big data applications for virtually any organization is the ability to locate inefficiencies in the supply chain and improve on them. This is made possible largely thanks to the Internet of Things (IoT) and connected devices. For example, GPS trackers used in freight trucks can help track the speed of deliveries and plan the most efficient routes.

IoT sensors on the factory floor can also identify hazards, such as unusual temperatures. Camera systems backed by artificial intelligence can even detect unusual activity in warehouses or be used in preventive maintenance on assembly floors.

3. Predictive Analytics

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Customer data is extremely important for any business, and big data analytics can help you make more use of it than ever. An integrated customer relationship management (CRM) system, for example, can help you instantly collect and share customer data with the sales team to help make better decisions when it comes to customer service. You’ll be able to pinpoint your best customers and make special offers to them based on their purchasing history.

Even better, however, is how you can use a combination of historical and current customer data to predict future buying trends. With the predictive analytics provided by big data, you’ll be able to stay ahead of the competition by offering customers what they need exactly as they need it.

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