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The Next Frontier in Data Discovery SAP Visual Intelligence

The Next Frontier in Data Discovery SAP Visual Intelligence. Bob Ferris Executive Solution Engineer . Disclaimer.

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The Next Frontier in Data Discovery SAP Visual Intelligence

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  1. The Next Frontier in Data Discovery SAP Visual Intelligence Bob Ferris Executive Solution Engineer

  2. Disclaimer • This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.

  3. SAP Visual Intelligence User Powered and IT Approved • Self-Service • No need for IT to create predefined query, report, or dashboard • Little user training required • Connected to enterprise BI • Leverage existing data, security, and admin services • Single metadata umbrella for trusted information • Secure • One IT-sanctioned security model and single sign-on • Content management – version control, promotion and rollback • Simple to manage and scale • 1 unified platform to deploy and administer • Proven scalability without operational disruptions

  4. SAP Visual Intelligence – HANA Support • We will consume Hana analytic and calculated view with and without variables. • We will have the ability to enrich Hana analytic view With Geographical information. • We will consume time hierarchies created in Hana (hierarchical navigation enabled in viz) • We will have the ability to categorize dimensions as measures

  5. SAP Visual Intelligence Roadmap for 2012 2.0 1.0 • Add Enterprise data acquisition: UNX • Continue investment in data manipulation & visualizations • Complete integration with Explorer & BI Platform • Schedule datasets, automate creation of Information Space, leveraging of desktop defined semantic enrichment. • Sharing: upload/download dataset and workspace to Streamwork and BIOD 1.1 • Easy to use and quick to install. • Answers on massive data volumes at high speed • Directly connect and semantically enrich online HANA data • Create interactive visualizations on top of the data set. • Share created visualizations using email or collaborate through SAP Streamwork. • Visualize the same HANA data via Explorer server and Mobile* • Acquire data from corporate and personal data sources (CSV, Excel, HANA, SQL data sources) • Merge Data from heterogeneous data sources • Do advanced data manipulations without scripting or code. • Intuitive visualization and analysis experience • Visualize the same HANA data via Explorer server and Mobile* Dec 2012 June 2012 May 2012 * Manual recreation of Explorer Information Spaces

  6. Demo

  7. SAP Visual Intelligence – Fast Facts • 64 bit ONLY • English Only - More languages later this year • HANA Version: SAP HANA 1.0 SP3 Rev 26 • External Experience Site: • https://www.experiencesaphana.com/community/solutions/explorer

  8. Predictive Analytics at SAP

  9. Extend Your Analytics Capabilities Sense & Respond Predict & Act Optimization What is the best that could happen? Predictive Modeling COMPETIVE ADVANTAGE Generic Predictive Analytics What will happen? Ad Hoc Reports & OLAP Standard Reports Why did it happen? Cleaned Data Raw Data What happened? ANALYTICS MATURITY The key is unlocking data to move decision making from sense & respond to predict & act

  10. SAP Predictive Assets BI clients Visual Int / Analysis • Industry / LOB Applications HPA Customer Analytics, HPA Instant Compliance, Unified Demand Forecast for Retail, Smart Meter Analytics, Operational Risk Management, Energy & Environmental Resource Management, Life Sciences, Manufacturing, Project Bingo, Project AHEAD… SBOP Predictive Analysis, HANA Studio HANA Predictive Analysis Library R Integration Visual Numerics IMSL Algorithms for BI clients SAF / Khimetrics HANA, BW, Universes, RDBMS, CSV…

  11. PAL Algorithm Roadmap beyond SP5 (2013) Classification Neural networks SVM Clustering Kohonen SOM Hierarchical Agglomeration Regression Polynomial regression Model Management Bagging, boosting, ensemble modeling Cross validation Time Series ARIMA Simulation Monte Carlo method SP4(Summer 2012) Extend algorithms in each category. Cover time series and preprocessing. Classification CHAID Regression Exponential/ Logarithmic Regression Logistic/ Geometric Regression Preprocessing Inter-Quartile Range test Time Series Single/ Double/ Triple Exponential Smoothing SP5 (Dec 2012) Preprocessing Anomaly Detection Correlation Summary statistics Binning Normalization Time Series Time series decomposition methods PMML • SP3(Nov 2011) • Cover classical predictive analysis algorithms in each category. • Clustering • K-means • ABC Classification • Classification • C4.5 decision tree • KNN • Regression • Linear Regression • Association • A-priori

  12. An Aside - Why R • Open Source statistical programming language • Over 3,500 add-on packages; ability to write your own functions • Widely used for a variety of statistical methods • More algorithms and packages than SAS + SPSS + Statistica • Who is using it? • Growing number of data analysts in industry, government, consulting, and academia • Cross-industry use: high-tech, retail, manufacturing, CPG, financial services , banking, telecom, etc. • Why are they using it? • Free, comprehensive, and many learn it at college/university • Offers rich library of statistical and graphical packages • R is a software environment for statistical computing and graphics

  13. SAP BusinessObjects Predictive Analysis • Start screen…

  14. SAP BusinessObjects Predictive Analysis Data Loading Understand the business and identify issues Load the SAP and non-SAP data into HANA or other source Data Visualization and Sharing Visualize the model for better understanding Store the model and result back to HANA Share results via PMML and with other BI client tools Data Preperation Visualize and examine the data Sample, filter, merge, append, apply formulas Data Processing Define the model via clustering , classification, association, time series, etc. Run the model

  15. SAP BusinessObjects Predictive Analysis • Intuitively design complex predictive models • Read and write from data stored in HANA, Universes, IQ, and other sources • Drag-and-drop visual interface for data selection, preparation, and processing

  16. Predictive Analysis Demo

  17. Thank You Contact information: Bob Ferris Executive Solution Engineer Bob.ferris@sap.com

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