Rethinking Big Data and Analytics Within Financial Services: Using Actionable Intelligence for Results

Rethinking Big Data and Analytics Within Financial Services: Using Actionable Intelligence for Results

Banks and other financial institutions across the world have long identified data as being the key to unlock their hidden potential, which can lead to improvements in revenue, capabilities, and performance. However, a significant number of organizations still struggle with many facets of data analysis, including what data to collect, how to approach analysis, and what pitfalls to avoid. In this special joint paper with the National University of Singapore (NUS) fintech lab, IDC examines how to refocus data analysis efforts using the concept of actionable intelligence, which provides technology with a whole new way of thinking and approaching how to solve the conundrums of extracting the most value out of data.Michael Yeo, senior research manager, IDC Asia/Pacific Financial Insights, says, "Too many banks still place their focus on collecting more data or more in-depth data when in reality looking at external data sources or adding much needed time-related context to the data they already have would deliver the best value. Actionable intelligence, as proposed by Professor Keith Carter from the NUS fintech lab, provides a pair of fresh eyes on a topic at the forefront of many CIOs' minds — how do I get the best value from my data?"

Please Note: Extended description available upon request.


Executive SnapshoT
Situation Overview
Five Steps to Enable Actionable Intelligence
Step One: The Approach Toward Business
Strategic Business Questions, Wrangle Data, Answer with Visualization, Taking Action or "SWAT"
Purpose of Obtaining Data
Step Two: Data Quality and Data Granularity
Why Is Recent Data Important? How Do We Qualify Recent Data?
A Forecast Is Only as Valuable as the Initial Input Data
Example in Action: Supply Chain Management
Lack of Information Structures in Visualizing Various Data Granularities
Example: Supply Chain Logistics Flow
Step Three: Data Completeness
Powering Competitive Intelligence
Making Obtaining External Data Sets a Corporatewide Priority
End Goal of Competitive Intelligence
Competitive Intelligence in Action
Step Four: Designing Systems to Account for Human Behavior
Gaming the System for Profit
Understanding the Capabilities of Different Data Analysis Standards
Advice for the Technology Buyer
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Synopsis

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