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Big Data Innovation in Banking

Banks are still struggling with understanding the concept of big data and how it can be applied in their organizations.

As financial institutions grapple with evolving business landscapes and increased information demand, finding optimal ways to store, organize and monetize the ever-increasing crush of data they possess is of crucial import. How effectively banks can make better business decisions based on the so-called "big data" they process on a daily basis will be crucial for the industry going forward, said Andy Hirst, senior director of Industry Marketing for SAP.

Hirst, speaking on big data innovation at the International SAP Conference for Financial Services this week, noted that banks are inundated with new sources of information constantly.

"The industry has been been analyzing structured information for many years, but the new growth now is in unstructured data," he said.

Additionally, the way businesses want to look at and analyze data has been greatly influenced by consumer use of mobile devices, Hirst said. "We're used to seeing information presented beautifully in these great graphic ways," he added.

Further, he noted that internet and retail companies who are able to use data to engage in highly targeted marketing efforts, such as Amazon or Google, have raised customer expectations. This confluence of the new consumer experience and the desire for seeing real-time results on mobile devices have introduced new technologies to try and help financial institutions take advantage of their data better, like in-memory computing. Hirst believes in-memory solutions will help banks not only process data faster, but more importantly aid in turning that new capability into tangible value, or as Hirst puts it, "What is the business value of being able to do something faster today than yesterday?"

To that end, Hirst offered several key takeaways for banks to consider to get the most out of the big data they have access to. Firstly, he advised them to take advantage of predictive analytics, "not looking at how the world was, but how it will be." The faster a bank can analyze data, the better the predictive value of it, Hirst noted, and as a result the industry must move from batch to real-time processing.

Hirst also advised financial institutions to "maintain one copy, not dozens" of their data. The more data is copied and moved, the less reliable it becomes, he added.

Banks also should focus on not simply using more data, but more diverse data. This includes not only internal data, but external information, such as social data, he said. Also of note is the need to realize that this process is not just a science, but an art as well. "It's about putting humans and data together to get the most insight," he said.

Ultimately, said Hirst, faster data processing and sophisticated analytics are crucial for banks to achieve a 360-degree view of the customer, developing true relationship-based pricing, and answering the question, "How do banks make money off the data being held in their back systems right now?"

[See Also: 7 Big Data Players To Watch]

Bryan Yurcan is associate editor for Bank Systems and Technology. He has worked in various editorial capacities for newspapers and magazines for the past 8 years. After beginning his career as a municipal and courts reporter for daily newspapers in upstate New York, Bryan has ... View Full Bio

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User Rank: Apprentice
7/13/2013 | 1:55:06 AM
re: Big Data Innovation in Banking
Everyone always sees the root cause of problems as what they are selling - the shoemaker sees leg pain as bad shoes, the shirt maker sees headaches as neckline to tight, the chiropractor sees indigestion as subluxation, and so on.

The capability to do something should not dictate strategy.

In a Toyota plant there is no data. There is only movement. The customer wants new truck beds to bolt on trucks tomorrow morning - enough for one day. The steel supplier must deliver steel to make that many trucks beds every day - twice per day - morning and night. Every truck bed gets made and delivered - by the end of the day there is zero inventory left in the plant - except perhaps for a few nuts and bolts. Why count what you don't want? Why not ask the customer what he wants and build a business system responsive enough to delivery. And yet there are plenty of people trying to sell the manufacturing plants a new system to count and analyze things they shouldn't even have.

Is Amazon successful because of their systems and data strategy or in spite of it? Have they traveled from 240 to 300 in the past 12 months because their data is telling them more about their customers? Do customers buy more because they see things they've searched for recently on the side of their page?

Systems folks would like you to think that but if you look at your own behavior I'll bet you find it annoying to continually see something you either decided not to purchase or already purchased hounding you everywhere you go - plastered across every page.

Meanwhile the Amazon that used to deliver to me in 2 days flat no excuses can no longer do that. What's their data telling them about how I feel about that?

Do you really think better data can make the one second attention span you are given on mobile into a transaction? You do?

When did we begin letting capabilities drive strategy? Didn't understanding the customer and the market used to be the basis for strategy? Strategy is positioning the pieces on the field of play before the battle. When did a strategic planning committee ever say "We need to use BIG DATA to predict what our customers will want so we can use fewer people and less time to do more."

Everyone wants to sell you stuff by making their ideas the latest buzz word. If they can get you to refer to what they do as the "Xerox" solution then you're bound to repeat it in order to look good to your friends or even look smarter than everyone else "We should be using BIG DATA to analyze our bank customers and understand what they'll want next." "Gee, that guy is really smart!"
User Rank: Author
7/11/2013 | 8:46:02 PM
re: Big Data Innovation in Banking
I think that fraud and the potential for using predictive analytics to prevent fraud are going to drive more banks to follow these recommendations. With the threats banks are facing now they are going to have to invest in fighting fraud with an analytics approach. That will get them more comfortable in using their data, and other sources of data, in different ways and will help them develop the talent to make the most of big data.
User Rank: Author
7/11/2013 | 7:29:12 PM
re: Big Data Innovation in Banking
Do you think he told banks anything they don't already know? Did he discuss obstacles to making the most of big data? It seems so obvious -- why is there so far to go before realizing the benefits?
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