Big data technology continues to be big business, with Fortune Business Insights forecasting that the big data technology market will grow to $116 billion by 2027, up from $41 billion in 2019, at a compound annual growth rate (CAGR) of 14%. But when it comes to big data, while your customers may have heard the term, they may not understand how the technology applies to them and their data centers. As big data adoption continues to grow, it will become increasingly important to competitiveness for enterprises large and small, across all verticals. Use cases will help you make big data sales. Here are four powerful big data technology application examples that can help you on your way.
1. Fraud detection
For businesses whose operations involve any type of claims or transaction processing, fraud detection is one of the most compelling big data application examples. Historically, fraud detection on the fly has proven an elusive goal. In most cases, fraud is discovered long after the fact, at which point the damage has been done and all that's left is to minimize the harm and adjust policies to prevent it from happening again. Big data platforms that can analyze claims and transactions in real time, identifying large-scale patterns across many transactions or detecting anomalous behavior from an individual user, can change the fraud detection game.
2. IT log analytics
IT solutions and IT departments generate an enormous quantity of logs and trace data. In the absence of a big data solution, much of this data must go unexamined: organizations simply don't have the manpower or resource to churn through all that information by hand, let alone in real time. With a big data solution in place, however, those logs and trace data can be put to good use. Within this list of big data application examples, IT log analytics (a type of descriptive analytics) is the most broadly applicable. Any organization with a large IT department will benefit from the ability to quickly identify large-scale patterns to help in diagnosing and preventing problems. Similarly, any organization with a large IT department will appreciate the ability to identify incremental performance optimization opportunities.
3. Call center analytics
Now we turn to the customer-facing big data application examples, including call center analytics (another type of descriptive analytics), which are particularly powerful. What's going on in a customer's call center is often a great barometer and influencer of market sentiment, but without a big data solution, much of the insight that a call center can provide will be overlooked or discovered too late. Big data solutions can help identify recurring problems or customer and staff behavior patterns on the fly not only by making sense of time/quality resolution metrics, but also by capturing and processing call content itself.
4. Social media analysis
Of the customer-facing big data application examples we could discuss, analysis of social media activity is one of the most important. Everyone and their mothers are on social media these days, whether they're "liking" company pages on Facebook or tweeting complaints about products on Twitter. A big data solution built to harvest and analyze social media activity, like IBM's Cognos Consumer Insights, a point solution running on IBM's BigInsights big data platform, can make sense of the chatter. Social media can provide real-time insights into how the market is responding to products and campaigns. With those insights, companies can adjust their pricing, promotion and campaign placement on the fly for optimal results.