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

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Presentation Transcript
  • 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.
sap visual intelligence
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
sap visual intelligence hana support
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
sap visual intelligence roadmap for 2012
SAP Visual Intelligence Roadmap for 2012



  • 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


  • 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

sap visual intelligence fast facts
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:
extend your analytics capabilities
Extend Your Analytics Capabilities

Sense & Respond

Predict & Act


What is the best that could happen?

Predictive Modeling


Generic Predictive Analytics

What will happen?

Ad Hoc Reports & OLAP

Standard Reports

Why did it happen?

Cleaned Data

Raw Data

What happened?


The key is unlocking data to move decision making from sense & respond to predict & act

sap predictive assets
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…

pal algorithm roadmap
PAL Algorithm Roadmap

beyond SP5 (2013)


Neural networks



Kohonen SOM

Hierarchical Agglomeration


Polynomial regression

Model Management

Bagging, boosting, ensemble modeling

Cross validation

Time Series



Monte Carlo method

SP4(Summer 2012)

Extend algorithms in each category. Cover time series and preprocessing.




Exponential/ Logarithmic Regression

Logistic/ Geometric Regression


Inter-Quartile Range test

Time Series

Single/ Double/ Triple Exponential Smoothing

SP5 (Dec 2012)


Anomaly Detection


Summary statistics



Time Series

Time series decomposition methods


  • 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
an aside why r
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
sap businessobjects predictive analysis1
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

sap businessobjects predictive analysis2
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
thank you

Thank You

Contact information:

Bob Ferris Executive Solution Engineer