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.
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