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Five Big Data Use Cases That 2017 Will Bring

November 30, 2017

Five Big Data Use Cases That 2017 Will Bring

Big data technology is maturing, and more organizations are dipping their toes into the big data pool and coming away with new insights about operations, customers and markets. Big data analytics is proving its worth with businesses of all sizes in all industries. The value of big data is that it can integrate almost any form of data set—structured SQL, unstructured data sources such as email or social media content and more—and it can reveal patterns and behaviors that were undetectable using data warehouse technology. However, as with any analytics, the answers are only as good as the questions. For big data to be valuable, you have to start with the right use case to define the parameters of the question to make sure the findings are valid. It’s often the reseller’s job to help customers create big data use cases that deliver ROI.

Big data adoption is accelerating as more organizations adopt NoSQL platforms (the foundation for big data analytics). NoSQL vendors such as MongoDB, DataStax, Redis Labs and Amazon Web Services are becoming part of the adoption mix for enterprise infrastructures, alongside established database management systems from Oracle, IBM, Microsoft and SAP. In a survey of 2,200 Hadoop customers, only three percent said they would be doing less with Hadoop in the next 12 months. Seventy-six percent have big data projects slotted for the next three months. Almost half of the companies that haven’t deployed Hadoop yet said they would in the next 12 months.

Clearly, big data is moving from the experimental or “hype” phase to mainstream enterprise computing as it continues to demonstrate substantial ROI. Those businesses realizing the greatest ROI are the ones with well-defined use cases. Here are some of the use cases that are gaining momentum for 2017:

1. The Internet of Things

The Internet of Things (IoT) promises to change computing forever, not only by adding a flood of new digital information but by enabling new machine-to-machine communications that will automate innumerable processes. Self-driven automobiles are one example of the IoT; feeds from sensors, cameras and GPS systems correlate to direct cars safely to their target destinations. That same IoT data can be harnessed using big data for analytics, improving performance for any automated process. Xcel Energy, for example, uses IoT data to manage smart-grid power distribution. IoT data also can be harnessed for inventory management, manufacturing, logistics or any application where machine data can be gathered and analyzed to assess and automate processes.

2. Customer personalization

Businesses today know more about their customers than ever before. They track every transaction, every e-commerce mouse click and even shopper behavior in the store. Warranties, service calls and rewards programs provide valuable customer data, along with social media chatter, Yelp reviews and online surveys. Analyzing that information reveals a lot about customer trends in general and individual shopping habits in particular. Not only does customer data provide insight about product demand, packaging, pricing, retail layout and related sales strategies, but it enables companies to tailor products and offers to each customer’s unique needs and demands. Consider the power of big data for e-commerce. Analytics allow online sales engines to enrich product data for search on desktops and mobile devices, as well as apply predictive analytics and machine learning to anticipate customer preferences.

3. Analyzing outside patterns

More publicly available information is going to be harnessed to benefit business and the public at large. For example, a Nucleus Research report shows that a resort received a 1,822 percent reduction in labor costs by synching its shift schedule with the National Weather Service. In Durham, North Carolina, big data has helped the police department reduce violent crime by 39 percent over a seven-year period using predictive analytics for better officer deployment and to improve public safety. Business will increasingly use these same data sources and techniques for detecting patterns that affect operations and for predictive analytics.

4. Internal patterns and business processes

Most businesses have only just begun to tap their operational data. Log data, for example, is being analyzed using relational databases, but the amount of log data is growing at an incredible rate. With big data techniques, massive quantities of log data can be stored and analyzed without programming SQL queries. The results reveal where operations can be optimized or where there are failures in business processes. When you add external data sources, such as customer and market data, the value of the trend data increases.

5. Security and fraud

Big data is increasingly being used to detect cyberattacks by analyzing enterprise data and traffic patterns, often triggering automated responses to head off an attack. Big data analytics are also used to protect privacy and identify fraudulent activity. Credit card companies, for example, routinely use big data to detect fraud. Medical insurance underwriters can lose up to 
$5 billion annually to fraud, but by using big data rather than complex SQL queries, underwriters get faster, more accurate detection of potentially fraudulent claims.

These are just a few of the use cases that are shaping the big data market. The value of the returns from these projects hinges on the scope of discovery and the nature of the data (i.e., garbage in, garbage out). Creative resellers are building on successful use case examples to design custom big data projects for customers that more than pay for themselves and that open the door to selling future big data initiatives.