Driving Business Intelligence with Better Analytics

Agenda

• Key Analytics Trends

• What drives Analytics?

• Business Scenarios / Framework

• Leveraging existing Investments & Data Assets

• Key considerations

Key Analytics Trends

> Analytics And Business Intelligence – CXO’s #1 technology priority

> Increasing Analytics Maturity

> Pervasive Analytics – Analytics embedded within Business Processes and applications

> In-Memory Analytics – faster, better, cheaper

> Analytics leveraging Big Data Platforms

> Optimizing User Experience – Data Visualization & Exploration

> Social Media – a mainstream input for Analytics

Key Drivers for Analytics

> Underpin strategic adjustments in real-time

> Compete - Secure the most powerful and unique competitive stronghold

> Increase customer profitability

> Improve operational efficiencies

> Optimize return on equity

> Identify and mitigate risks and threats

Big Data Analysis Framework

Key Analytics Capabilities

> High level of maturity in basic Business Intelligence, predictive modeling

> lagging in core capabilities of text analytics and data visualization

> Need for more advanced data visualization and analytics capabilities increases with the introduction of big data

Recommendations:

> Build analytics capabilities based on business priorities

> Take Data visualization solution to mainstream

> Provide Social analytics capabilities to better understand customers to align products and services Banking & Financial Markets

Global

Source: IBM Institute for Business Value & University of Oxford

Relevance of Big Data to Financial Services

> Credit Risk Scoring & Analysis

> Trade Surveillance (AML)

> Abnormal Trading Pattern Analysis

> Fraud Detection

– Credit Fraud

– Deposit Account Fraud

– Bad Debt Fraud

> Customer Segmentation

> Customer Loyalty Analysis

Customer Analytics, driving Big Data Initiatives

Solutions areas:

> Customer 360⁰

> Customer & Financial Advisor Attrition

> Segmentation & Targets (Cross- sell / Up-Sell)

> Usage based Auto Insurance premium

> Fraud Detection & Prevention

Source: IBM Institute for Business Value & University of Oxford

More than half of big data efforts underway by financial service companies are focused on achieving customer-centric outcomes.

Leveraging Existing Investments

> Significant source of Insights – Transactions, Log data, Events, Emails, Social Media

> Leverage more internal data (events, customer touch points, etc.)

> Platforms & Products – Enterprise Data warehouse, Data Marts, Business Intelligence and Visualization tools, Predictive Analytics Platforms

> Active big data analytics efforts by banking and financial institutions are on analyzing transactions and log data

– Customer Sentiment Analysis

– Product – Portfolio mix

– Fraud detection

– Attrition prediction

Potential New Investments:

–Data Visualization Tools

–Big Data Framework / Platforms

• Focus on Data driven Analytics based on Business Objectives & Priorities

• Identify/build a data scientist skillset for your business

• Ensure strong collaboration between end-users and data scientists

• Emphasize data visualization, data exploration and usability

• Include touch points with Mobile, Social Media and Cloud initiatives in

your Big Data Roadmap

Key Takeaways

Jim Milde President - Financial Services & Insurance NTT DATA, Inc., Jim.Milde@nttdata.com http://americas.nttdata.com/