Aligning big data use cases with business objectives is the key to big data success. Every big data project revolves around a well-defined use case that organizes the data and defines the criteria. Most organizations are drowning in data and the only way to organize that data is to take a step back, assess the value of various pieces of information, and then apply those useful bits in a way that drives business. So the only effective way to apply big data is by starting with a big data use case.
The size of the digital universe is expanding at a rate of 40 percent annually, including the number of new mobile and Web users and the Internet of Things. The amount of available data is doubling every two years and by 2020 it will reach 44 zettabytes or 44 trillion gigabytes of data. And all that data can be tapped for better business decision-making, if you can design the right big data use cases.
Defining Big Data Use Cases
A big data use case provides a framework to test hypotheses and model data to deliver insights. To make data-driven decisions, you first need to apply big data use cases to organize the data.
Use cases are a means to create a context for data in order to organize it. You start with the raw data, and then aggregate it to deliver intelligence or reveal patterns from which you gain insight. Once you have the results of the analysis you apply the insight to make better business decisions in a way that has a positive operational impact. The result is value creation and better financial outcomes.
However, the whole process has to start with the right big data use case. Here are five common use cases where big data is having a significant impact:
1. Risk management and fraud detection
More big data use cases are being created to address risk and fraud. Big data breaks down data siloes within the organization to provide a comprehensive picture of real-time activity. Analytics can aggregate and parse data from multiple sources, such as credit cards, payments, deposits, and fiscal transactions, or tracking online activity, such as user logins and file access.
All activity records are aggregated in a single, big data repository for analysis, making it easy to identify fraudulent activity. Big data also is capable of automating responses, such as isolating ports or servers or redirecting suspicious traffic. And, big data can be applied to monitor data security and handle eDiscovery for regulatory compliance.
2. Brand and sentiment analysis
With online marketing, web chatter has a greater impact than ever on brand sentiment and sales. Big data makes it possible to aggregate asynchronous data (online conversations, social media, blog content) with synchronous data (sales figures, product stocking data, pricing information) to identify consumer sentiment.
Hadoop can handle real-time analytics, and break down data insights by geography, age group, gender or any other demographic criteria. Analytics can also measure changes in sentiment over time with new marketing campaigns.
3. Customer insight
Big data use cases also are ideal for determining customer behavior and preferences. Hadoop lets you build behavioral models that combine sales and social data to predict shopping behavior. Big data use cases also are valuable for assessing comparison shopping, managing the customer lifecycle, and assessing online engagement.
4. Target marketing and personalization
In addition to assessing customer behavior, big data uses cases can be used to segment target markets. Analytics can map user behavior, location, time of day, and other parameters to determine performance for a particular store or product. It also can be used to predict performance among specific customer groups or market areas.
Personalization is another big data use case application. Online marketers are continually refining their marketing programs based on online activity. Big data use cases allow them to personalize sales and marketing messages using a variety of matching identifiers, such as mapping online store behavior to in-store coupons or offers.
5. Business operations intelligence
Big data also can identify problems in business processes. Tapping machine data, for example, can generate substantial savings by identifying production problems and automating responses. For example, Intel was able to save $9 million by adding CPU tester modules and big data analytics to reduce component failures. Similarly, UPS was able to save $30 million by using big data to analyze daily truck travel routes and cut 85 million miles off delivery mileage.
These are just five types of big data use cases that can be applied to almost any operation. If you can assimilate different data types to get better answers to business problems, then you may have a perfect big data use case. What’s your biggest business challenge? Is it a big data candidate?