Decision Support Optimization through Predictive Analytics - Leuven Statistical Day 2010

Transcription

1 Decision Support Optimization through Predictive Analytics - Leuven Statistical Day 2010 Ernst van Waning Senior Sales Engineer May 28, 2010

2 Agenda SPSS, an IBM Company SPSS Statistics User-driven product requirements and Market trends From Statistics to Data Mining From hypothesis testing to relevance research and segmentation for better business decisions Discussion 2

3 SPSS, an IBM Company

4 Introducing SPSS A leading provider of predictive analytic software, services and solutions Software data collection, text and data mining, advanced statistical analysis and deployment technologies Services implementation, training, consulting, and customization Solutions combine software and services to deliver highvalue line-of-business solutions; used for optimizing marketing campaigns, call center effectiveness, identification of fraudulent activity and more Enables decision makers to predict future events and proactively act upon that insight to drive better business outcomes 4

5 IBM Acquires SPSS

6 SPSS Statistics

7 SPSS Statistics today SPSS Statistics has the most customers 250,000 customers Over 95% of the Fortune 1000 In Every Industry 10 of the top 10 global commercial banks 8 of the top 10 telecommunication services companies 21 of the top 25 retailers worldwide 24 of the top 25 market research firms And Throughout the Public Sector All major national governments Every branch of the U.S. Military and all U.S. State Governments #1 statistical software on universities 7

8 SPSS Statistics: Key Benefits Increase analyst productivity The most comprehensive workbench Presentation ready output Grows with your needs Increase functionality with add on modules, scripting and programmability Scales to your requirements Flexible deployment options maximize resource utilization 8

9 Customer Requirements Functional Refinements: Ability to Handle More Data, Faster Open, Extendable and Customized Integration (Business Processes): Managing, Securing and Deploying Statistical Assets Simple to Use: Get the Power of Statistical Analysis Into the Hands of Non-statisticians More Tools for New Business Problems Recent Enhancements: Improved performance and scalability Bootstrapping Automated Data Prep Custom Dialogue Builder Standard ODBC, R and Python interfaces Office Integration & Output Visualization easy RFM/Direct Marketing Advanced Model Viewers Algorithmic enhancements

10 Improved Statistics Reporting 2008 SPSS Inc.

11 Enhanced Model Viewers Interactive visualizations for Two-Step Cluster Viewers for Automated Data Preparation results Viewers for Nonparametric Statistics 11

12 12

13 From Statistics to Data Mining

14 New ways of working to optimize business decisions and actions New Approach Traditional Approach Predict and act Volume Lack of Insight Velocity Inefficient Access Sense and respond Instinct and intuition Skilled analytics experts Real-time, fact-driven Everyone Inability to Predict Variety Back office Automated Point of impact Optimized 14

15 Enabling the Predictive Analytics Process Capture & Connect Analyse & Predict Deliver & Act Data Collection delivers an accurate view of customer attitudes and opinions Predictive capabilities bring repeatability to ongoing decision making, and drive confidence in your results and decisions Unique deployment technologies and methodologies maximize the impact of analytics in your operation 15

16 From Statistics to Data Mining SPSS Statistics allows you to verify hypotheses With scientific rigor For scientific purposes Data Mining focuses on investigation and relevance analysis Exploratory purpose Get insight in what your data tell you! SPSS Modeler (formerly called Clementine) 16

17 IBM SPSS Modeler 17

18 Modeler Differentiating Capabilities Easy to Learn / Intuitive Visual Interface Visual approach - no programming Comprehensive range of data mining functions Flexible deployment options Powerful Automated modeling Automated data preparation Multi model creation & evaluation Integrated analysis of text, web, & survey data Open & Scalable architecture No need for specialized databases Data mining within standard databases with SQL pushback (for highest performance) and support for in-database mining Maximized use of infrastructure with multithreading, clustering and use of embedded algorithms 18

19 Modeler Operation Operations on data - nodes Connections carry data between nodes Stream diagrams Executable Visual programming 19

20 Visualisation Initial exploration of data (Methodological point) Discovery of partial patterns for insight to guide pre-processing Exploring the results of modelling Graphs are interactive 20

21 Predictive Models: Families of Algorithms Classification Segmentation Association Data Reduction Predict or Forecast Find Groups Find Links Less Attributes Classification, Regression, Time Series Clusters Association Discovery, Sequence Factors Target (what to predict) No Target Many Targets 21

22 The Datamining Process (CRISP-DM) 22

23 IBM SPSS Text Analytics Uses natural language processing heuristic rules and statistical techniques to reveal conceptual meaning in text Extracts concepts from text and categorizes them Makes unstructured qualitative data more quantifiable, enabling the discovery of key insights from sources such as survey responses, documents, s, call center notes, web pages, blogs, forums and more Brings repeatability to ongoing decision making 23

24 Questions?