Applications of Big Data

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. 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 pattern 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 quality of logs and trace data. In the absence of Big Data solution, much of this data must go unexamined. Organizations simply don’t have the manpower or resource to charm 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 is the most broadly applicable.

3. Call Center Analytics: Now, we turn to the customer-facing Big Data application examples of which call center analytics 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. It much of 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 patterns on the fly not only by making sense of time/quality resolution metrics, but also by capturing and processing call center itself.

4. Social Media Analytics: Of the customer facing Big Data application examples we could discuss, analytics of social 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 Cog-nos Consumer Insights, a point solution running on IBM’s Big-insights Big Data platform, can make sense of the chatter. Social media can provide real-time insight into how the market is responding to products and campaigns.

5. Improving Health and Public Health: The computing power of big data analytics enables us to decode entire DNA strings in minutes and will allows us to find new cures and better understand and predict disease patterns. Just think of what happens when all the individual data from smart watches and wearable devices can be used to apply it to millions of people and their various diseases.

The clinical trials of the future won’t be limited by small sample sizes but could potentially include everyone! Big data techniques are already being used to monitor babies in a specialist premature and sick baby unit. By recording and analyzing every heart beat and breathing pattern of every baby, the unit was able to develop algorithms that can now predict infections 24 hours before any physical symptoms appear. That’s way team can intervene early and save fragile babies in an environment where every hour counts.

6. Role of Big Data in Medicine: The data generated by medical care and medically relevant research are rapidly becoming bigger and more complex, particularly with the advent of new technologies. Our ability to advance medical care and efficiently translate science into modern medicine is bounded by our capacity to access and process these big data. From human genetics and pathogen genomics to routine clinical documentation from internal imaging to motion capture.

7. Science and Technology: The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.99995% of these streams, there are collisions of interest per second.

As a result, only working with less than 0.001% of the sensor stream data. The data flow flow from all four LHC experiments represents 25 petabytes annual rate before replication. This becomes nearly 200 petabytes after replication.