BUSINESS VISUALIZATION THROUGH ANALYTICS. Copyright 2013, SAS Institute Inc. All rights reserved.

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1 BUSINESS VISUALIZATION THROUGH ANALYTICS.

2 BEFORE AND AFTER SAS ANALYTICS

3 BusinessDecision Analytics 35 % LEADE R 38 25% FOCUSED COHERENCE Data Management

4 RETAIL

5 MANUFACTURING

6 HEALTH CARE

7 BANKING

8 INSURANCE

9 FINANCIAL SERVICES

10

11 PREDICTIVE FORWARD THINKING ANALYTICS Monitor & Detect Current State Predict & Act Future/New Opportunities Higher Decision Impact

12 SAS ANALYTICS SAS LIFECYCLE FOR ANALYTICS 13

13 TOP PERFORMING COMPANIES ARE USING MORE PREDICTIVE ANALYTICS Q&A: How To Energize Your Enterprise BI Platform With Integrated Predictive R Models, Forrester Research, Inc., May Forrester Research, Inc. Reproduction Prohibited

14 ANALYTICS USAGE Varying Levels of Analytics Use and Expertise ANALYTICALLY DRIVEN ANALYTICALLY INFORMED ANALYTICALLY AWARE ANALYTICALLY NEW LEVEL 1 Isolated analytics use. Basic tools and limited or no best practices LEVEL 2 Predictive analytics usage is part of mission critical applications only. Full benefits are not understood by a majority in the organization. LEVEL 3 Analytics usage consists primarily of tactical and ad hoc approaches. Analytics dev. and deployment is constrained, yet departments have their own experts and/or initiatives. LEVEL 4 Analytics talent is centralized into larger groups. Management understands and supports analytics for strategic value, thus bringing business units into alignment ANALYTICALLY INNOVATIVE LEVEL 5 Company is committed to analytics as part of its future growth plan. Business units embrace their own transformational analytical plans.

15 SAS VISUAL ANALYTICS FUELING THE ANALYTICS LIFECYCLE & STATISTICS VISUALILSATION & REPORTING EXPLORE DATA DEPLOY & MONITOR MANAGE DATA TEXT SAS Visual Statistics EXPLORE & DEVELOP MODELS SAS Visual Analytics

16 SAS LASR ANALYTIC SERVER SAS VISUAL ANALYTICS & STATISTICS It is an in-memory engine specifically engineered for the demands of interactive and iterative analytics In-memory = Fast, sub-second responses Multi-User = Hundreds of concurrent users Stateless = Don t pre-compute things Interactive = Instantly visualize the impact from changing model parameters Deployment = MPP (distributed) or SMP (single machine)

17 SAS VISUAL ANALYTICS & STATISTICS DATA CONNECTIONS Oracle Teradata MS SQL Server XML CSV Text EBCDIC VSAM COBOL HADOOP Greenplum SAP BW SAP ERP SAP HANA Delimited CADatacom Oracle Apps MySQL Informix ODBC EXCEL ADABAS IBM DB2 Siebel SFDC PostgreSQ L

18 SAS VISUAL ANALYTICS & STATISTICS MANIPULATE DATA Access structured and unstructured data Data filtering, including outliers Join/promote tables, compute columns Dynamic Group-By operations

19 SAS VISUAL BUSINESS VISUALIZATION DRIVEN BY ANALYTICS ANALYTICS ENTERPRISE/DEPARTMENTA L AND SELF SERVICE VISUALIZATION POWERED BY SAS ANALYTICS REPORTING, MOBILE COLLABORATION

20 BUSINESS THE DIFFERENCE BETWEEN RAPID INSIGHT AND FAST VISUALIZATION INFORMATION DATA VISUALIZATION ANALYTIC VISUALIZATION EXPLORATION DISCOVERY

21 SAS VISUAL ANALYTICS VISUALIZATION POWERED BY SAS ANALYTICS ANALYTIC VISUALIZATIONS AND REPORTING WILL ALLOW YOU TO: Quickly and easily identify the TOP 10 influencers of customer retention and loyalty Examine customer call comments and Twitter streams for topics and sentiment Predict churn for the next two quarters Conduct what-if scenarios for churn forecast Visualize the impact of marketing campaigns and the lift generated Easily share insights with key stakeholders very quickly

22 SAS VISUAL ANALYTICS VISUALIZATION POWERED BY SAS ANALYTICS EXAMPLE: TEXT ANALYSIS GIVES YOU INSIGHT TO CUSTOMER EXPERIENCE AND OPINION Analytics applied to text provides real MEANING

23 SAS VISUAL EXAMPLES : REPORTING VISUALIZATION ANALYTICS

24 SAS VISUAL INTERACTIVE EXPLORATION AND PREDICTIVE MODELING STATISTICS EXPLORE AND DISCOVER PREDICT AND REFINE COMPARE AND ASSESS

25 SAS VISUAL STATISTICS ANALYTIC CAPABILITIES Classification Predict outcomes using causal factors -, churn, campaign response, part failure Regression Estimate interval prediction using causal factors - spend, policy premium, credit limit, etc. Segment data based on self-similarity risk, profitability, demographic, etc. Clustering Group-By HOW IT CAN BE USED? Models by segments/groups (e.g. location, store, owner, device, etc.).

26 SAS VISUAL RAPID MODEL BUILDING AND REFINEMENT STATISTICS PROTOTYPE REVIEW REFINE

27 SAS VISUAL STATISTICS ASSESS AND SCORE Model comparison using lift charts, ROC charts, misclassification tables etc. Interactively evaluate lift at different depths of file Interactively define event probability cut-off Generate SAS code for scoring purposes

28 SAS VISUAL KEY BENEFITS STATISTICS PRECISION Best-in-class data discovery and analytics to derive precise insights and make targeted decisions AGILITY Spend more time perfecting your models to reflect changing conditions and less time waiting for answers SPEED In-memory Analytics provides speed, scale and concurrency for timely insights

29 SAS ANALYTICS GIVES YOU THE ABILITY TO:

30 WHY ANALYTICS IS IMPORTANT Lost revenue, missed opportunities. SAS ANALYTICS Without the ability to quickly access and analyze all relevant organizational data, problems and issues aren t noticed until it s too late, so opportunities are missed.

31 WHY ANALYTICS IS IMPORTANT Delayed, ineffective decisions. SAS ANALYTICS Inability to analyze data and present information in an easy-to-understand format delays and impacts quality of decisions; business leaders often rely on gut feeling or past experiences for guidance.

32 WHY ANALYTICS IS IMPORTANT Limited IT resources. SAS ANALYTICS Dependence on costly but scarce IT resources raises operating costs and prevents users from being able to fully leverage information assets. Outdated, manual methods offer simplistic analytical capabilities and limited self-sufficiency, leading to suboptimal results.

33 DANIEL PANA COUNTRY LEADER ROMANIA THANK YOU! sas.com

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