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Business Analytics and Data Warehousing Business Analytics as a concept has come a long way, from being a novelty to a norm today Suresh Kumar Financial Services Asia Pacific Oracle Corporation Agenda Market Assessment Business Analytics in Banking Trends In Banking
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Business Analytics as a concept has come a long way, from being a novelty to a norm today
Financial Services Asia Pacific
Fragmented data – difficult to understand profitability of customers, products and channels
Lack of automation to support key Banking processes, eg: loan origination, transparency and accountability enabling regulatory reporting/tracking
Consistency of service across channels and improve cross selling
To service current business needs through an adaptive IT architecture
To manage human capital and internal knowledgeMarket Assessment: BankingKey Business Drivers & Challenges facing the Banking Industry:
Example – Retail Banking
Future Trends in BankingBeyond Traditional BI-DWH: More Customer Centric Model
Enterprise Wide Information
& Customer Data Integration
Concise metrics on KPIs at corporate level
Highly correlated with overall strategic plans
Focused on Key Performance Indicators for a business unit
Report employee performance versus goals
Broader scope of business
Targeted analytics to improve job performance
Broadest scope for ad-hoc analytics
Targeted analytics to improve multi-organization initiatives
Limited ad-hoc capability
Highly focused analytics to monitor customer
activity and guide behavior
DetailedFuture Trends in BankingEmpower All Users - Not Just the Few
Cross Sell / Up Sell
Channel Usage / Efficiency
Profit & Loss Analysis
Data Warehouse Enabling Analytics to Provide Business Insight
“A Data Warehouse is a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management’s decision-making process.”
The data warehouse is intended for knowledge workers, that is, the people who need to analyze information provided by the warehouse and make business decisions.
ReportingTraditional Business Analytics EnvironmentMulti-Vendor, Un-integrated
Enable consolidation to fewer, more cost effective systems
Eliminate the need for special purpose engines“Bring the algorithms to the data, not the data to the algorithms”
Simplify the end-user experience
Simplify, costly ETL procedures
Scalable, integrated, cost-effective data warehousingAnalysis capabilities embedded in the databaseSecure, personalized, easy to use, with analytical sophistication
Complete ETL, metadata services, based on open standards
unify the “data silos” and provide a single version of the truth throughout the enterprise reducing redundancy of marketing interactions and providing one integrated view of customer data.
optimize the risk/value ratio for customer portfolio in order increase their overall profitability. Align organization around profitability driven vision at each levels, develop a cohesive interaction strategy based on that metric.
Understand Attrition drivers, identify risky behaviors and profile, target proactive service and marketing actions to prevent and reduce attrition
optimize each interaction to increase perception of value added to customer. Banks need to truly understand what is being done with their products and services and how they impact a customer’s life and lifestyle.
make analytic-based decisions in seconds and pervasive rather than days or weeks long and exclusive. Deliver granular insight to all touch-points in order to respond rapidly and intelligently to customer requests, and increase the value of each interaction.
StudyData Warehouse in Banking Implementation Methodology
Proof of Concept
Winter 2005 “Top Ten” Results Summary
Source: Winter Corporation, September 2005
917 Production Customers / 156 of these are in Financial Services!
Oracle Business Intelligence
& Data Warehousing