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.
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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.
What is Big Data?
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”:
- Volume: The term big data refers to very large quantities of data. While there isn’t an exact size that qualifies a dataset for the big data label, most big data repositories are measured in terabytes or petabytes.
- Velocity: Within most big data stores, new data is being created at a very rapid pace and needs to be processed very quickly. For example, the stream of data coming from social media feeds represents big data with a high velocity.
- Variety: Big data comes from a wide variety of sources and resides in many different formats. A big data repository might include text files, images, video, audio files, presentations, spreadsheets, email messages and databases.
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.Â
10 Top Big Data Use Cases
So how are enterprises using big data today? Here are ten of the most popular big data use cases.
1. 360° View of the Customer
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.
2. Fraud Prevention
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. So, for example, if a credit card were used to rent a car in Hawaii, but the customer lived in Omaha, a customer service agent might call to confirm that the cardholder was on vacation and that someone hadn’t stolen the card.
Thanks to big data analytics and machine learning, today’s fraud prevention systems are orders of magnitude better at detecting criminal activity and preventing false positives. In the example already mentioned, for instance, a sophisticated fraud prevention system might be able to see that the customer had recently purchased airline tickets, sunscreen and a new swimsuit before the rental car purchase. Based on historical patterns, a predictive analytics or machine learning system would be able to tell that the rental car was thus less likely to be a fraudulent purchase.
But fraud prevention systems can get even more sophisticated than that. According to Experian, fraud tends to be concentrated in certain geographic regions—often near airports, which make it easy for criminals to move stolen goods. However, which zip codes are riskiest tends to change over time. Big data analytics can look at past records of fraudulent transaction and quickly identify changing trends. Credit card companies and retailers can then pay more attention to transactions in zip codes that are emerging as hotbeds for criminal activity.
Credit card issuers are understandably hesitant about disclosing all the advanced analytic techniques that they use to detect and prevent fraud. However, many credit card firms and other consultants offer technology, advice and services to other firms to help them set up systems to stop criminal transactions.
3. Security Intelligence
On the theme of criminal activity, organizations are also using big data analytics to help them thwart hackers and cyberattackers. Operating an enterprise IT department generates an enormous amount of log data. In addition, cyber threat intelligence data is available from external sources, such as law enforcement or security providers. Many organizations or now using big data solutions to help them aggregate and analyze all of this internal and external information to help them prevent, detect and mitigate attacks.
Big data security solutions vary in sophistication and they are sold under a wide variety of names. For example, vendors sell log analytics tools that can detect anomalies in network data, security information and event management (SIEM) tools that offer real-time analysis of security alerts generated by other security software, and user and entity behavior analytics (UEBA) solutions that use analytics and machine learning to detect unusual patterns in device or user activity. Other big data security solutions are labelled as security intelligence offerings or network intelligence offerings.
4. Data Warehouse Offload
One of the easiest — and potentially most cost-effective — ways for organizations to begin using big data tools is to remove some of the burden from their data warehouses. Even among the few organizations that haven’t yet started experimenting with big data analytics, it is common to have a data warehouse that facilitates their business intelligence (BI) efforts.
Unfortunately, data warehouse technology tends to be very costly to purchase and run. And as business leaders have begun demanding more reports and insights from their BI teams, the data warehouse solutions haven’t always been able to provide the desired performance.
To solve this problem, many enterprises use an open source big data solution like Hadoop to replace or compliment their data warehouses. Hadoop-based solutions often provide much faster performance while reducing licensing fees and other costs.
5. Price Optimization
Both business-to-consumer (B2C) and business-to-business (B2B) enterprises are also using big data analytics to optimize the prices that they charge their customers. For any company, the goal is to set prices so that they maximize their income. If the price is too high, they will sell fewer products, decreasing their net returns. But if the price is too low, they may leave money on the table.
Big data analytics allows companies to see which price points have yielded the best overall results under various historic market conditions. Businesses that are more sophisticated with their pricing analytics may also employ variable or dynamic pricing strategies. They use their big data solutions to segment their customer base and build models that show how much different types of customers will be willing to pay under different circumstances. B2C companies that have attempted this approach have met with mixed results, but it is fairly standard among B2B companies.
6. Operational Efficiency
In addition to helping organizations optimize their pricing, big data analytics can also help companies identify other potential opportunities to streamline operations or maximize their profits. Often, this particular big data use case is the purview of BI or financial analysts.
These staffers have long been running the weekly, monthly and quarterly reports that help executives track the bottom line. But as big data tools have become available and have improved in their sophistication, analysts are able to incorporate data from more sources and to update those reports much more frequently.
For example, a nationwide retailer might want to track the hourly sales of a new product in all of its physical stores. Big data analytics could easily highlight potential problems, say, for instance, a particular store that hadn’t sold any of the new product during the first few hours of the rollout. A quick phone call might then reveal that the store manager had forgotten to put the new product on display, and staff could remedy the situation before it became more costly for the company or led them to inaccurate conclusions about the popularity of the product.
7. Recommendation Engines
Speaking of popularity, one of the most familiar use cases for big data is the recommendation engine. When you are watching a movie at Netflix or shopping for products from Amazon, you probably now take it for granted that the website will suggest similar items that you might enjoy. Of course, the ability to offer those recommendations arises from the use of big data analytics to analyze historical data.
These recommendation engines have become so commonplace on the Web that many customers now expect them when they are shopping online. And organizations that haven’t taken advantage of their big data in this way may lose customers to competitors or may lose out on upsell or cross-sell opportunities.
8. Social Media Analysis and Response
The flood of posts that flow through social media outlets like Facebook, Twitter, Instagram and others is one of the most obvious examples of big data. Today, companies are expected to monitor what people are saying about them in social media and respond appropriately — and if they do not, they quickly lose customers.
As a result, many enterprises are investing in tools to help them monitor and analyze social platforms in real-time. Sometimes these are standalone social media products, while at other times, they are part of a larger marketing intelligence or big data analytics solution.
9. Preventive Maintenance and Support
Many of the big data use cases mentioned so far relate to retail or financial companies, but businesses in manufacturing, energy, construction, agriculture, transportation and similar sectors of the economy can also benefit from big data. In these examples, some of the biggest benefit might come from using big data to improve equipment maintenance.
As the Industrial Internet of Things (IIoT) begins to become a reality, factories and other facilities that use expensive equipment are deploying sensors that can monitor that equipment and transmit relevant data over the Internet. They then use big data solutions to analyze that information — often in real time — to detect when a problem is about to occur. They can then perform preventive maintenance that may help prevent accidents or costly line shutdowns.
10. Internet of Things
And enterprises in every industry are beginning to see the possibilities of the Internet of Things (IoT). As in the preventive maintenance example, they are using sensors to collect data that they can then analyze to achieve actionable insights. They might track customer or product movement, monitor the weather or keep an eye on security camera footage.
As with big data itself, the number of ways in which analytics can be applied to IoT solutions seems to be endless.
Other Common Big Data Use Cases
While these are ten of the most common and well-known big data use cases, there are literally hundreds of other types of big data solutions currently in use today. Companies routinely use big data analytics for marketing, advertising, human resource manage and for a host of other needs.
Many organizations find that they can apply big data solutions to their unique, industry-specific needs. For example, healthcare organizations look for patterns in treatment that lead to the best outcomes for patients. Farmers use big data to find the best time to plant or harvest. Professional sports teams use analytics to decide who should be on the roster and to help improve player performance. The energy industry uses big data from smart meters to improve efficiency, and financial traders use big data to determine when to buy or sell.
Perhaps it shouldn’t be surprising then that once organizations begin to experiment with big data technology, they often find dozens of new uses for it that they hadn’t originally considered. As time goes on and the big data tools become even more sophisticated, organizations and vendors will almost certainly discover new ways to use big data solutions that no one has even considered today.
