When you think about big data, knowing that its common characteristics are volume, variety, and velocity probably doesn't do you a whole lot of good in understanding whether what you're contending with falls into this new category. Thinking about it in terms of a tipping point is better, Fern Halper, a partner at the IT consulting and research firm Hurwitz & Associates, suggests in our newly posted video, Big Data, Fast Infrastructure.
"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."
System performance, availability, security, and manageability matter greatly. Big Data technologies as a new generation of technologies and architectures designed to extract value economically from very large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis via IDC
Although we all "hem" and "haw" when in the store and asked for all the data that is used for these analytical programming it is seriously needed.
Stores not only need to know what "dress' to sell but also just "where" to place it--so to speak. What you or I may seem as obvious, may not be as clear to them. Software that produces concise analytical data may help them notice that which seems so obvious to us.
Just today I witnessed a need for this analytical data, while in a large pharmaceutical store with my elderly parent. We currently have one of the largest number of elderly people in history--a segment of people who great purchasing power, yet their interests are not considered.
With this data in place, stores might consider putting pharmaceutical products that meet their needs closer to the entrance of the store where they would not have to walk so far. They might consider placing items within arms reach instead of on the highest or lowest shelf--places that are unreachable to those who move around with pain.
Because of my alternate role as a transportation planning consultant, I keep returning to the issue of applications for the public transportation industry ... of which I've seen very little. (In travel demand forecasting, yes, and a bit in ridership counting, but not in terms of truly Big Data collection and analytics, usable for things like behavior pattern assessment and marketing/outreach.)
I'm wondering if there's much of any effort yet to identify the benefits of BI, Big Data, and analytics in this industry.
The issue of big data reminds me of days back in high school math. Sometimes the problem was that you had so much information and weren't sure if all of that really was required. Sometimes it would seem that you can solve this problem without using some of the information given and so the fact that this seemingly unnecessary info was given, is what would make the problem impossible to solve.
If only there had been a software to help sieve the info so that one could know what can be safely dismissed...
Thanks Beth, I also love this video. It really shows why HPC is needed in businesses and the benefits it provides.
For example this type of analtyics can not only help a store decide which dress to sell, but where to place it to ensure maximum sales. And these types of decisions can be made on real time data, vs what happened last month.
I love the video and think Fern explains very well where the real challenges with big data occur. It's easy to visualize the problem with this presentation and to understand why solutions are currently so much in demand.
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