"The key about big data is that it exists at the tipping point of the workarounds that organizations have historically put in place to manage large volumes of complex data," she says. "Big data technologies allow people to actually analyze and utilize this data in an effective way."
Here, Halper shares what she considers "really interesting" examples of big data analytics use cases across three verticals: financial services, healthcare, and retail.
In financial services, scoring transactional data in real-time for credit card fraud prevention and detection is one example of a big data use, she says. "Imagine millions of credit card transactions being analyzed to determine if they might be fraudulent using predictive analytics. Or, on the unstructured side, picture the text in insurance claims being analyzed to determine what might constitute fraud."
In this latter case, consider the worker who has filed a worker’s compensation claim but whose file is full of reprimands from his boss. A company could train an analytical system to use this unstructured file data in combination with data from structured sources to find patterns indicating fraud, Halper says. "As new claims come in, the system can automatically kick out the ones that might need to be investigated."
In healthcare, picture a stream of data from equipment in a neonatal unit that might monitor the infants' temperature, blood pressure, and heart rate. "The amount of data coming from this equipment is enormous, and it would be hard for a person to process it all," she says. "However, big data solutions can capture the data and analyze it in order to determine, for example, if an infection might be cropping up in an infant -- so big data and big data analytics can be used to help care for premature infants, or anyone in a hospital."
In retail, you need only think of the recommendation engines from Amazon and eBay. They are getting more sophisticated, too, Halper says. For example, eBay uses advanced technologies that look at what you're purchasing and then make recommendations based on its models of the vast amount of other purchases people have made.
Another example is the use of advanced analytics over massive amounts of data in real-time at big box stores. "Using your loyalty card, based on what you’re buying, what you have bought in the past, and what others with similar profiles like you have bought, the store will provide you with coupons for different products you might like."
Success with big data use cases like these won’t come easily. Be prepared to contend with technical challenges revolving around how to gather, store, manage, and analyze big data -- in all its different forms. Also, understand that finding people with the right skills will be a challenge, "as you'll need people capable of architecting a system to deal with this data from ingestion to actively doing something with it."
Lastly, you'll face analytics challenges. "You’re accumulating huge data from Websites, CRM, call centers, social media, sensors -- you want to know what this all means, and that requires picking the right tools, having the right skillsets in the people doing the analysis, and getting the results of the analysis to the people who need it."
Whether you've identified your ideal big data use case or not, now is the time to get your organization to start thinking differently about what is possible with orders of magnitude more data, Halper says. "Get buy-in to this, and get them to act on it."