The explosive growth of digital data has opened up new avenues for growth – actionable insights from varied information sources can be analyzed using sophisticated algorithms & analytics to create a clear, sustained competitive advantage. The key lies in processing data better, faster and cheaper.
Banks today have amassed a wealth of data from which they can create personalized services and construct contextualized experiences for their customers. However, combining internal information sources with external data flows remains a challenge for most banks.
Big Data – Value Creation Pipeline
Business Challenges
Data and specialized Data Sciences can enable hyper-targeted marketing, optimized transaction processing and personalized wealth management advice – improving every aspect of banking. However, to fully unleash the power of Big Data across the enterprise, banks will first need to overcome internal challenges of disparate legacy systems.
- Inability to leverage volumes of data, quickly and with more certainty
- Absence of consistent master data, enterprise information and systems
- Need for greater precision in the prediction process
- Lack of enterprise view
- Low data quality
- Usage of disparate technologies for implementing identical processes in different departments
- Lack of centralized metadata management
- Data security issues
- Lack of governance and ownership of data assets
- Privacy, legal and regulatory compliance
Solution Overview
Accelerate your bank’s growth with our big data offering – designed to collate, process and deliver insights and measurable benefits that add to bottom line.
- Advanced analytics, data mining, big data analytics, social analytics and predictive modelling
- Canned reports, OLAP cubes, dashboards, scorecard and metrics, and mobile analytics
Our Big Data services for banks undergo a comprehensive series of data journeys:
- Infrastructure set-up: Infrastructure choice, sizing, set-up and administration
- Platform and technology set-up: Cloudera, Hortonworks, Microsoft, Google, Pivotal, IBM and SAP
- Data integration: Data ingestion, storage, quality check, standardization, changing data capture, real-time data, complex event processing and data virtualization
- Domain and analytics: Batch analysis (behavior and predictive), interactive analytics, stream analytics and machine learning
- Visualization: Data discovery, self-service BI, BI reports and dashboard followed by health check scorecards
Results
- Integrated view of customer – provides banks with powerful insights of their customers by combining internal transactional data with external social, web and location data
- Customer Value Management – a closed loop analytical process to understand customer segments with a focus on enhancing customer knowledge – driving value creation opportunities
- Social media analytics solution – integrated into an existing BI tool or used on a subscription model
- Multichannel usage analytics dashboards – insights on channel usage trends and channel usage mix
- Multiple big data use cases drive business value – including customer journey analytics, digital acquisition, transaction data mining and contact optimization
