Discussions about the benefits of big data often mention the need for “business insights” in fairly generic terms. That can leave people without big data expertise wondering, what exactly are these "business insights"? And how exactly would they help my organization? Part of the problem is the huge number of potential use cases for big data solutions. Some big data platforms can be used in so many different ways that vendors hesitate to get too specific lest they turn away some potential customers. Fortunately, big data technologies have been around long enough that many organizations have already been using the tools in production for some time. These early adopters provide examples of common applications for the technology. And while every organization is different, some of the most popular big data use cases transcend industries and apply to a wide range of companies. But before we delve into big data use cases, we should begin by defining our terms. The industry-standard way to describe big data is with the “three Vs”: Big Data if often hailed as a critical tool that provides competitive advantage, but make effective use of Big Data tools is real life business scenarios offers plenty of challenges. So how are enterprises using big data today? Here are ten of the most popular big data use cases. Many enterprises use big data to build a dashboard application that provides a 360° view of the customer. These dashboards pull together data from a variety of internal and external sources, analyze it and present it to customer service, sales and/or marketing personnel in a way that helps them do their jobs. For example, imagine the sort of dashboard an insurance company might create with information about its customers. Naturally, it would include demographic data, like customers’ names, addresses, household income and family members, as well as sales information about which types of policies the customers hold. It could also pull information from the company’s customer relationship management (CRM) solution about the customers’ past interactions with the firm and even provide links to transcripts of recent calls, email messages or chat sessions. It might also show which pages of the company website a particular customer had recently visited, providing valuable clues about the reason a customer might be calling. The dashboard could also pull in external information, such as the customer’s recent social media posts. Or if an auto insurance customer had agreed to have a tracking device from the company installed, it might even provide details about the customer’s current location and recent speed. All of that information would obviously help prepare company staff to interact with the customer, but the most sophisticated dashboards don’t stop there. If it used advanced analytics or machine learning tools, the dashboard take a guess about the reason for a customer call. It could suggest opportunities for cross-selling or upselling customers on products, or if it detects that a customer might be in danger of defecting to a competitor, it might suggest potential discounts that could lower the customer’s rate. Some tools can even analyze customers’ language to detect their current emotions and suggest appropriate responses to sales or customer service agents. This might sound far-fetched and futuristic, but many companies today already have systems like this one in place, and they are using them to improve customer satisfaction and increase revenues and margins. For credit card holders, fraud prevention is one of the most familiar use cases for big data. Even before advanced big data analytics became popular, credit card issuers were using rules-based systems to help them flag potentially fraudulent transactions.
