Driving Business Intelligence with Better Analytics
Agenda
• Key Analytics Trends
• What drives Analytics?
• Business Scen...
Key Analytics Trends
> Analytics And Business Intelligence – CXO’s #1 technology priority
> Increasing Analytics Maturity
...
Key Drivers for Analytics
> Underpin strategic adjustments in real-time
> Compete - Secure the most powerful and unique co...
Big Data Analysis Framework
Key Analytics Capabilities
> High level of maturity in basic Business Intelligence, predictive
modeling
> lagging in core ...
Relevance of Big Data to Financial Services
> Credit Risk Scoring & Analysis
> Trade Surveillance (AML)
> Abnormal Trading...
Customer Analytics, driving Big Data Initiatives
Solutions areas:
> Customer 360⁰
> Customer & Financial Advisor Attrition...
Leveraging Existing Investments
> Significant source of Insights – Transactions, Log data, Events, Emails, Social Media
> ...
• Focus on Data driven Analytics based on Business Objectives & Priorities
• Identify/build a data scientist skillset for ...
Driving Business Intelligence With Better Analytics
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Driving Business Intelligence With Better Analytics

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Driving business intelligence with better analytics
Blobal Directions 2013

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Driving Business Intelligence With Better Analytics

  1. 1. Driving Business Intelligence with Better Analytics Agenda • Key Analytics Trends • What drives Analytics? • Business Scenarios / Framework • Leveraging existing Investments & Data Assets • Key considerations
  2. 2. 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
  3. 3. 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
  4. 4. Big Data Analysis Framework
  5. 5. 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
  6. 6. 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
  7. 7. 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.
  8. 8. 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
  9. 9. • 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/

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