SAS Institute: Big data and smarter analytics

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David Wallace, Global Financial Services Marketing Manager for SAS Institute, presents at the 2013 NAFCU Annual Conference.

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SAS Institute: Big data and smarter analytics

  1. 1. Big Data and Smarter Analytics: What Does it Mean to Credit Union Executives? Presented by David M. Wallace SAS
  2. 2. Agenda • Big Data • Business Analytics • Visualization: Where Big Data and Business Analytics Meet • Where and How: The ‘Sweet Spot’ Areas for Credit Unions • Can We Do This? • Questions
  3. 3. Big Data: What Is It?
  4. 4. VOLUME VARIETY VELOCITY VALUE TODAY THE FUTURE DATASIZE THRIVING IN THE BIG DATA ERA
  5. 5. Big Data: Growth in Volume and Variety Source: CEB TowerGroup, Gain New Insight from Unstructured Data, 2013.
  6. 6. “…the broad use of data and quantitative analysis for decision-making within organizations." Business Analytics: What is It? Source: Thomas H. Davenport, The New World of Business Analytics, March, 2010 “Business analytics empowers people in the organization to make better decisions, improve processes and achieve desired outcomes." “It brings together the best of data management, analytic methods, and the presentation of results—all in a closed-loop cycle for continuous learning and improvement.”
  7. 7. 5 Stages of Analytical Maturity Source: Thomas H. Davenport, Competing on Analytics Don’t Be Here Aim for Here
  8. 8. Evolution of Analytics Source: Jack Phillips and Tom Davenport, Analytics 3.0: The Era of Impact, International Institute for Analytics, April 2013
  9. 9. Evolution of Analytics Source: Jack Phillips and Tom Davenport, Analytics 3.0: The Era of Impact, International Institute for Analytics, April 2013
  10. 10. Evolution of Analytics Source: Jack Phillips and Tom Davenport, Analytics 3.0: The Era of Impact, International Institute for Analytics, April 2013
  11. 11. What Are Advanced Analytics?
  12. 12. Visualization
  13. 13. Visualization: 3 Capabilities EXPLORATION AND VISUALIZATION POWER OF ANALYTICS RAPID DELIVERY OF MOBILE INSIGHTS
  14. 14. Visualization: Value of Predictive Analytics DATA VISUALIZATION ANALYTIC VISUALIZATION EXPLORATION DISCOVERY
  15. 15. Visualization: Increase Use of Analytics and BI • Self-service • Easy to use Analytics • Work with more data • Reporting and Dashboards • Mobile BI • Collaboration
  16. 16. FORECAST Visualization: What Can You Do?
  17. 17. Visualization: For Business and IT FOR BUSINESS • Easy-to-use analytics create a starting point for building and analytics driven culture • Visual exploration of data, combined with analytics, fuel insight discovery for competitive advantage • Rapid delivery of insights to colleagues via the Web and mobile promote collaboration and fact based decisions FOR IT • Control governance and empower colleagues with easy access to all data • Leverage a highly scalable environment for cost effective growth when the business needs it Why Visualization with Analytics?
  18. 18. Visualization: Summary
  19. 19. Applying Analytics to Credit Unions Member Experience & Marketing Risk & Regulatory Compliance Operations Fraud & Financial Crimes
  20. 20. Example: Analytics for Financial Crimes
  21. 21. Getting Started
  22. 22. 10 Practical Steps for Making Analytics Work 1. Expand the use of business analytics where feasible. 2. Integrate analytics across the organization. 3. Deploy analytics on specific business tasks. 4. Use a variety of analytics tools, including more sophisticated ones. 5. Create a data-management strategy that includes ready access to data. 6. Deploy the necessary analytical technology. 7. Develop formal data- management processes. 8. Secure executive support. 9. Deliver and communicate value. 10. Hire and develop the right analytical talent. Source: Bloomberg Business Week, Making Business Analytics Work: Lessons from Effective Analytics Users, May 2012. n = 930.
  23. 23. 4 Keys to Analytical Talent 1. Put talent before technology – Maximize analytics tool investments by getting talent in place first 2. Emphasize “soft” skills in addition to technical skills – Critical thinking and problem solving capabilities – “ability to deal with the world through an analytical lens” 3. Invest in ongoing staff development – Training existing employees  creating a fact-based decision culture 4. Be creative when looking outside for new talent – Go beyond traditional areas (examples: engineering, math) – Partner with local universities; tap provider resources Source: Bank Systems & Technology, 4 Keys to Building an Analytical Workforce, February 2012
  24. 24. What to Look For in a Business Analytics Solution • Robust visualization • Support for advanced analytics • Prebuilt analytical models and task support • Suited for range of users • Ease of use • Balance of user autonomy and IT control • Modular • Fully integrated • Availability of training and technical support • Low total cost of ownership Source: IT Business Edge, You’re Never Too Small for Business Analytics, September 2011
  25. 25. For More Information • Kristin Locklear – Kristin.Locklear@sas.com • Mary Jones – Mary.Jones@sas.com SAS is the NAFCU Services Preferred Partner for Business Intelligence, Predictive Analytics Software & Risk Assessment. Learn more at www.nafcu.org/SAS.
  26. 26. Thank You! David.M.Wallace@sas.com
  27. 27. Presentation Summary • Big Data and Smarter Analytics: What Does it Mean to Credit Union Executives? • In this session, we will discuss how investment in business analytics and Big Data solutions can help credit unions achieve data-driven insight. Learn how technologies like predictive analytics, use of Big Data, and data visualization can help your CU enhance member relationships and restore member confidence; improve risk management while rebuilding liquidity and your overall capital positions; and engage and manage regulators and other stakeholders in a new era of intense oversight and scrutiny. Also discover how business analytics can help you reduce operational losses and combat financial crime while allowing you to identify new ways to drive income and enhance member value

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