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

The Next Frontier in Data Discovery SAP Visual IntelligenceBob FerrisExecutive Solution Engineer

© 2011 SAP AG. All rights reserved. 2Confidential

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.


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


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

• 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


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


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


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


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


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

http://www.r-project.org/index.html


SAP BusinessObjects Predictive Analysis

Start screen…


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



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


Predictive Analysis Demo

Thank You

Contact information:

Bob Ferris Executive Solution Engineer

[email protected]

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

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