The Next Frontier in Data Discovery SAP Visual IntelligenceBob FerrisExecutive Solution Engineer
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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.
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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
User Powered and IT Approved
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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
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SAP Visual Intelligence Roadmap for 2012
• 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*
1.1
May 2012
June 2012
1.0
Dec 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
2.0
* Manual recreation of Explorer Information Spaces
Demo
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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
CONFIDENTIAL
Predictive Analytics at SAP
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Extend Your Analytics Capabilities
ANALYTICS MATURITY
CO
MPETIV
E A
DVA
NTA
GE
Sense & Respond Predict & Act
Raw Data
Cleaned Data
Standard Reports
Ad Hoc Reports & OLAP
Generic Predictive Analytics
Predictive Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best that could happen?
The key is unlocking data to move decision making from sense & respond to predict & act
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SAP Predictive Assets
HANA, BW, Universes, RDBMS, CSV…
HANA Predictive Analysis Library
R IntegrationVisual
Numerics IMSL
Algorithms for BI clients
SAF / Khimetrics
SBOP Predictive Analysis, HANA Studio
Industry / LOB ApplicationsHPA 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…
BI clients Visual Int / Analysis
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PAL Algorithm Roadmap
SP3 (Nov 2011)Cover classical predictive analysis algorithms in each category.
Clustering• K-means• ABC ClassificationClassification• C4.5 decision tree• KNNRegression• Linear RegressionAssociation• A-priori
SP4 (Summer 2012)Extend algorithms in each category. Cover time series and preprocessing.
Classification• CHAIDRegression• Exponential/
Logarithmic Regression
• Logistic/ Geometric Regression
Preprocessing• Inter-Quartile Range
testTime Series• Single/ Double/ Triple
Exponential Smoothing
SP5 (Dec 2012)
Preprocessing• Anomaly Detection• Correlation• Summary statistics• Binning• NormalizationTime Series• Time series
decomposition methods
PMML
beyond SP5 (2013)
Classification• Neural networks• SVMClustering• Kohonen SOM• Hierarchical
AgglomerationRegression• Polynomial regressionModel Management• Bagging, boosting,
ensemble modeling• Cross validationTime Series• ARIMASimulation• Monte Carlo method
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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
An Aside - Why R
R is a software environment for statistical computing and graphics
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SAP BusinessObjects Predictive Analysis
Start screen…
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Step1
Data Loading
Step 2
Data Preparation
Step 3
Data Processing
Step 4
Data Visualization and
Sharing
Data Loading1. Understand the business
and identify issues2. Load the SAP and non-SAP
data into HANA or other source
Data Preperation1. Visualize and examine
the data2. Sample, filter, merge,
append, apply formulas
Data Processing1. Define the model via
clustering , classification, association, time series, etc.
2. Run the model
Data Visualization and Sharing
1. Visualize the model for better understanding
2. Store the model and result back to HANA
3. Share results via PMML and with other BI client tools
SAP BusinessObjects Predictive Analysis
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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
SAP BusinessObjects Predictive Analysis
Predictive Analysis Demo
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