SAS CLOUD ANALYTICS MAY 2015
SAS SOLUTIONS ONDEMAND HISTORY Established in 2000 Formed as the Application Service Provider Group HP ES40 6/833: Tru64 Unix V5.1 TruCluster. 4 CPU s, 8 GB Memory 2
SAS CLOUD ANALYTICS - PRESENT
SAS SOLUTIONS ONDEMAND 700+ employees worldwide Multiple lines of business representing 250+ SAS solutions 450 customer sites with users from over 70 countries 4
GLOBAL ADVANCED ANALYTICS LAB Formed by Dr. Goodnight in 2007 150+ dedicated Data Scientists 97% of analysts hold advanced degrees in statistics, mathematics, and operations research, with 40% of these being PhDs 30 approved and 15 pending patents 5
SAS DATA CENTERS Multiple Global & Regional Data Centers Customer and Third-party IT options 2500+ managed servers, of which 1700+ are virtual (67%) 8 PBs managed storage Scalable infrastructure with physical and logical security Applications monitoring and support 24/7 High availability / disaster recovery 6
SAS SOLUTIONS ONDEMAND Security is Top Priority SECURITY AND CERTIFICATIONS Hosting Environment SOC 2 / SOC 3 Safe Harbor Physical and logical security Scalable infrastructure Standard SLA of 99% uptime Multiple data centers ISO 27001 international data centers 7
SAS CLOUD Live ANALYTICS 1. Cary, NC, USA 2. Charlotte, NC, USA 3. Toronto, Canada 4. Wynyard, UK 5. Dublin, Ireland 6. Johannesburg, South Africa 7. Sydney, Australia (2) In Progress 1. Frankfurt, Germany Oregon California Charlotte Toronto Virginia Cary Dublin L Isle d Abeau Barcelona London Frankfurt Cernusco Beijing Seoul Tokyo Available Dubai 1. Alphaville + Sao Paulo, Brazil 2. Barcelona, Spain 3. L Isle d Abeau, France Bangalore Singapore 4. Cernusco, Italy 5. Dubai, UAE 6. Bangalore, India 7. Seoul, Korea 8. Beijing, China 9. Tokyo, Japan 10. Singapore 11. Oregon, California, Virginia, Live In Progress Available August 2014 Sao Paulo Alphaville Johannesburg Sydney USA 8
SAS CLOUD ANALYTICS OFFERINGS DELIVERED BY SAS SOLUTIONS ONDEMAND
DEPLOYMENT OPTIONS WHAT MEETS THE BEST NEEDS OF THE CUSTOMER? Private Cloud RaaS SaaS IaaS Enterprise Hosting SAS Cloud Azure Google AWS Public Cloud Platform and Infrastructure By cloud provider *Software by SAS* Platform and Infrastructure by Customer *Software by SAS* Remote Managed Services 10
CURRENT SOLUTION OFFERINGS DELIVERED BY SAS SOLUTIONS ONDEMAND
SAS CLOUD ANALYTICS SOLUTION OFFERINGS Life Sciences Government Health Plans (Public/Private) Organizations that discover and develop new products, as well as the companies that service those firms State and federal government organizations Organizations that manage insurance and payments for consumers and employers SDD, CTDT, Key Opinion Leader Tax Fraud, Workers Compensation Fraud, Unemployment Fraud (Tax and Insurance) Child Welfare, Eligibility Fraud, Claims Fraud Fraud, Health Outcomes Analysis, Episode Analytics 12
SAS CLOUD ANALYTICS SOLUTION OFFERINGS Banking Retail and Investment banking and Financial Services Insurance Property and Casualty Insurance with Personal and/or Commercial lines Cyber Insider Threat programs for cross industry verticals Asset Performance Management and Basel 3 Compliance and Fraud Management solutions for all fraud types Fraud Detection services for Claims and organized activities around providers Subrogation Comprehensive Insider Threat monitoring solution with real time monitoring 13
SAS SECURITY INTELLIGENCE TRUSTED ACCESS A malicious insider threat to an organization is a current or former employee, contractor, or other business partner who has or had authorized access to an organization's network, system, or data and intentionally exceeded or misused that access in a manner that negatively affected the confidentiality, integrity, or availability of the organization's information or information systems. Carnegie Mellon University CERT Division of the Software Engineering Institute (SEI) 14
SAS SECURITY INTELLIGENCE DEMONSTRATION Automated Business Rules weblog/syslog security/dlp organization Levels e-comms of Detection Threat Score Anomaly Detection Link and Predictive analytics Text Analytics Insider Threat Score Combining analytical methods and domain expertise White box Ability to evolve 15
16
17
PIONEERING DATA SCIENTISTS R.A. Fisher In God we trust; all others must bring data. Edwards Deming Trevor Hastie, Robert Tibshirani, and Jerome Friedman, co-authors of The Elements of Statistical Learning in their Preface essentially, all models are wrong, but some are useful. Box, George E. P.; Norman R. Draper (1987). Empirical Model-Building and Response Surfaces, p. 424. 18
SAS SOLUTIONS ONDEMAND For additional information, contact: John.Brocklebank@sas.com, Jinwhan.Jung@sas.com www.sas.com