What Does Big Data Mean For Banks? - InformationWeek
Data Management // Big Data Analytics
01:30 PM
Michael Flynn
Michael Flynn

What Does Big Data Mean For Banks?

Traditional data management strategies and storage technologies are holding back big data projects. Try this new approach.

In a recent Said Business School study, 63% of banks recognized proficiency in big data as a competitive advantage. However, 91% indicated that they lack key skills necessary to execute more effectively, and only 3% reported that their organizations had deployed big data initiatives on a continuous basis. Many banks are trying, but few appear to be succeeding.

Why are banks struggling?
When faced with the requirements of a new big data initiative, banks too often draw only on prior experience and attempt to leverage familiar technologies and software-development-lifecycle (SDLC) methodologies for deployment.

Traditional technologies, particularly the industry’s most common data stores (e.g., relational databases), were designed to enforce structure and optimize processing performance within a constrained hardware environment. As a result, many bank technologists are used to transforming data to meet these constraints, including aggregation to satisfy scalability limitations and data-normalization to satisfy schema restrictions.

Aggregation and normalization of data in this manner can result in several weaknesses:

  • Rigid schemas do not tend to allow for flexibility in responding to upstream and downstream data changes.
  • Data lineage may be lost after aggregation and summarization.
  • Data governance is likely weakened when several constituents retain responsibility for an extended, multi-stage data flow.

Read the rest of this story on Bank Systems & Technology.

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User Rank: Ninja
8/15/2014 | 11:30:31 AM
Not surprised
Actually when you look at how capable people are overall at leveraging big data analytics, it is far from overwhelming. According to a recent IDG SAS survey, the majority of organizations are not particularly capable when it comes to amost big data tasks. However turning insights into action showed the least competence with some saying their organizations were not at all capable. 


Peter Fretty
D. Henschen
D. Henschen,
User Rank: Author
8/6/2014 | 10:29:29 PM
What this jargon means is that they're using traditional databases
Lots of jargon here to explain that banks are pretty much sticking with relational databases and aren't doing a lot of experimentationw with big data platforms like Hadoop and relational databases. In an industry that's dealing with managing money and executing transactions, I don't think that should be a surprise. In some ways it's even reassuring, but banks and other financial institutions could certainly be experiementing with analysis of social data and customer comments about their service experiences. Brand perception and what not. Banks could also look into how people are feeling about money, investments, financial choices and what not. In short, sales, service and marketing-oriented analyses.
User Rank: Author
8/6/2014 | 1:53:57 PM
Interesting perspective
Interesting perspective on the traditional relational database skills inside banking IT teams vs big data projects.
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