Big Data is a sand trap! Everyone talks about it, assumes everyone else is using it, so everyone claims they’re using it… but honestly they’re not. Check out our awesome introduction on SAP’s Predictive Analytics 3.1. Learn how to turn your Big Data sand trap into an instant money pit.

1. SAP BusinessObjects
Predictive Analytics 3.1
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Lesson One: Introduction

2. Course Overview
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Coming Soon
SAP BusinessObjects – Predictive Analytics 3.1
Lesson One: Introduction
Lesson One
SAP
BusinessObjects
Predictive
Analytics -
Introduction
Lesson Two
Adding a Dataset
and Visualization
Lesson Three
Predictive
Analytics –
Predictive
Features
Coming Soon…
Coming Soon…

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Agenda
What Will Be Covered:
1. SAP BusinessObjects Predictive Analytics 3.1 Introduction
2. Automated Analytics/Expert Analytics/Predictive Factory/Time Series Forecasting
3. Big Data – Spark & SparkSQL
4. SAP HANA VORA
a) SAP HANA Support
5. Additional New Features
6. Summary & Next Lessons
7. Contact Us

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▪ In lesson one of our three part SAP BusinessObjects Predictive Analytics 3.1
course, you will learn about the added business value of the latest updates of
SAP’s Predictive Analytics 3.0 to version 3.1.
▪ Lesson two introduces an in-depth look at the various features of SAP’s
BusinessObjects, using our real world business use case of a web application
crawling actual data from crime reports & arrests in a specific region for 2009.
▪ Lesson three investigates the possible real world crime prediction features
available for this data for 2010 via SAP BusinessObjects Predictive Analytics.
SAP BusinessObjects Predictive Analytics

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▪ SAP BusinessObjects Predictive Analytics 3.1 is a statistical analysis &
data mining solution (end-to-end machine learning platform).
▪ Build predictive models to discover hidden insights regarding data source information.
▪ Anticipate future business events & harvest profitable forward-thinking decision making.
▪ Aids in administration, maintenance, & operationalization of predictive models.
▪ Includes thousands of customer-driven robust predictive models.
▪ Increases automation capabilities, overall accuracy & business value of analysis results.
SAP BusinessObjects Predictive Analytics

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https://uacp2.hana.ondemand.com/viewer/c36bc49d7cce4d35a2099aecdea81e2c/3.1/en-US/41a645fbe691441b8884222092cc90e8.html
▪ BusinessObjects Predictive Analytics now includes Automated Analytics.
▪ Automated Analytics features the below modules:
▪ Data Manager: Semantic layer tool used in data preparation.
▪ Modeler: Creates models (e.g., clustering, classification, regression, etc.).
▪ Export models in formats that can be easily applied to the production environment.
▪ Social Extracts: Uses structural relational data stored in datasets.
▪ Improves model prediction & decision capabilities.
▪ Represents data in graphical form, illustrates how data is linked to other data.
▪ Dedicated workflow creates colocation & frequent path analyses.
▪ Based on geo-referenced data.
▪ Recommendation: Product recommendations.
▪ Based on customer input & social network link analysis.
Automated Analytics Modules

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https://uacp2.hana.ondemand.com/viewer/#/94dbf2ba9d4047618880187451c3b253/3.1/en-US
▪ BusinessObjects Predictive Analytics now includes Expert Analytics.
▪ Different visualization techniques (e.g., scatter matrix charts, cluster
charts, & decision trees) used to dissect relevant signal data.
Expert Analytics
▪Divulges new business opportunities, &
incorporates predictive analysis results w/ future
applications & business processes.
▪Used for time series forecasting, trend analysis,
outlier detection, classification analysis, affinity
analysis, & segmentation analysis.

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https://uacp2.hana.ondemand.com/viewer/#/41d1a6d4e7574e32b815f1cc87c00f42/3.1/en-US
▪ One of the most unique updates included is the new Predictive Factory.
▪ User-friendly interface, which leverages predictive models to increase business value.
▪ Ensures operational systems are fed by production databases.
▪ Benefits of the Predictive Factory are:
▪ All-inclusive business context predictive model management.
▪ Existing Automated & Expert Analytics model importation, easy time series forecasting model
authoring, model versioning management, & model lifecycle management & monitoring.
▪ Meticulous main industrial task scheduling, new data model applications, & model retention to
ensure performance accuracy & deviations.
Predictive Factory

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https://uacp2.hana.ondemand.com/viewer/41d1a6d4e7574e32b815f1cc87c00f42/3.1/en-US/c2f0326ebc89450f83f106529733d60e.html#loioc2f0326ebc89450f83f106529733d60e
▪ Predictive Factory allows for automated management of predictive models.
▪ Organizing modeling activities for a project’s business case allows for:
▪ Classification of imports, regression, time series model creation, clustering via the Modeler.
▪ Segment time series models: applying forecasting mechanisms to various time series at the
same time for different dataset segments.
▪ Importing, applying & training predictive pipelines.
▪ Automating tasks, applying models, testing deviations & running external commands.
▪ Monitoring model performance over periods of time.
Predictive Factory

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(1) https://uacp2.hana.ondemand.com/viewer/41d1a6d4e7574e32b815f1cc87c00f42/3.1/en-US/164760e27e1544ee9dc55b3e25c3da5e.html
(2) https://uacp2.hana.ondemand.com/viewer/41d1a6d4e7574e32b815f1cc87c00f42/3.1/en-US/06c2e140952846219f4e539bbe4affd4.html
(3) https://uacp2.hana.ondemand.com/viewer/41d1a6d4e7574e32b815f1cc87c00f42/3.1/en-US/2654afee72ab45cc9b3345e35b91e406.html
▪ One great feature in Predictive Factory is Time Series Forecasting.
▪ Used as pre-prepared data sources which contain time stamped signals.
▪ These data sources can contain training series precision, complete optimized
forecasting models, & lifecycle model management in areas such as task applying,
task deviation, model management & performance.
▪ After creating a Time Series Forecasting model the below information is
available to help interpret your new model:
▪ Settings: Information regarding your model properties.
▪ Training datasets, variable roles, model training options.
▪ Reports: Overview reports & Forecast reports.
▪ Status: Information about model anomalies.
▪ Errors & warning messages encountered during model training.
Time Series Forecasting

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▪ Apache Spark is a lightning-fast cluster computing technology.
▪ Designed for quick computation; based on Hadoop MapReduce.
▪ Extends the MapReduce model for additional computation types.
▪ Includes stream processing & interactive queries.
▪ In-memory cluster computing increases the application’s processing speed.
▪ Designed for wide range of workloads (batch applications, streaming, iterative algorithms, etc.).
▪ Reduced burden of maintaining separate analysis tools.
▪ Features include: Speed, multiple languages, & advanced analytics.
▪ Supports ‘Map’ & ‘reduce’, SQL queries, Streaming data, Machine learning, & Graph algorithms.
▪ SparkSQL is a Spark Core component that includes a data abstraction called
SchemaRDD; provides support for semi-structured data & structured data.
Big Data – Improved Spark & SparkSQL
https://www.tutorialspoint.com/apache_spark/apache_spark_introduction.htm
Spark SQL
Spark
Streaming
Mlib
(Machine
learning)
GraphX
(graph)
Apache Spark Core

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▪ Big Data1 and Hadoop2 currently support SparkSQL version 1.63.
▪ Access SparkSQL data source for utilizing Native Spark Modeling 4 on Hive 5 data source.
▪ The Native Spark Modeling (NSM) feature allows for large data-intensive
automated algorithm processing on Spark.
▪ Spark is used as the distributed processing engine for NSM.
▪ Allows for better performance & training benefits scalability.
▪ NSM allows for delegation of predictive calculations across Spark clusters.
▪ Use NSM or create automated models & generate Vora-compatible SQL.
Big Data – Improved Spark & SparkSQL
(1) https://en.wikipedia.org/wiki/Big_data
(2) http://hadoop.apache.org/
(3) http://spark.apache.org/releases/spark-release-1-6-0.html
(4) https://blogs.sap.com/2016/07/02/configure-native-spark-modeling-in-sap-businessobjects-predictive-analytics-30/
(5) https://hive.apache.org

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http://www.sap.com/product/data-mgmt/hana-vora-hadoop.html
▪ SAP HANA Vora is a new Spark execution framework, designed to
enrich & accelerate the utilization of Hadoop data & interactive analytics.
▪ Use SAP HANA Vora w/ Automated Analytics to perform predictive modeling.
▪ NOTE: In this current version release of SAP HANA Vora, data manipulation and in-
database modeling scenarios are not supported.
Predictive Modeling for SAP HANA Vora
▪ The Simba SparkSQL ODBC driver in
Automated Analytics is required to access
SAP HANA Vora via a Hadoop environment.
▪ Perform Native Spark Modeling on SAP HANA Vora
Spark instance.
▪ Spark automated algorithms are available via
distributed processing.

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▪ Generate Scoring Formula as SAP HANA user defined function (UDF):
▪ The BusinessObjects Predictive Analytics modeler allows exportation of
user-defined scoring equations.
▪ Invoked via a calculation view, ABAP code, or an SQL view.
▪ Auto-generate any score in real-time based on updated data or new data.
▪ Utilized in business applications & reports.
▪ Can be surfaced back into Business Warehouse cubes for use in BW queries.
▪ Provides all-inclusive predictive workflow w/ BW & SAP HANA, including the upcoming
SAP BW/4HANA data warehouse.
▪ Advanced users creating predictive models w/ any combo of HANA PAL, HANA APL &
open-source R algorithms can automate repetitive workflows without compromising
flexibility of using predictive technology of their choosing.
New For SAP HANA Support

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http://www.sap.com/developer/topics/sap-hana-express.html
▪ Automated Analytics can be used together w/ SAP HANA Express Edition,
a streamlined downloadable Express Edition of SAP’s in-memory platform.
▪ Now faster & used for data-driven application development.
▪ Expert Analytics can export a chain of trained models to any local file for use
in the new Predictive Factory.
▪ Used to apply tasks, schedule, or retrain; enabling the evolution of models to be monitored.
▪ Kxen_learnAndApply mode now performs learn and apply steps:
▪ Input application dataset specification is now identical to the training dataset one.
▪ Makes model configuration easier.
▪ Data cache enabled for increased performance.
▪ Only applies to the sendMode () API call.
Additional New Basic Features in 3.1

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▪ Unique guided tour features can set up assist displays, & guided connection
tours to Automated Analytics.
▪ Predictive process is now entirely automated (e.g., data preparation, business
intelligence systems, & business application predictions).
▪ High level predictive models ensure accuracy on business decision/turnout predictions.
▪ Complex models can now be uploaded/imported easily into Predictive Factory.
▪ Models can now be assessed, applied, tasked, monitored & scheduled easily.
▪ Deviations can be auto-detected by models created in Automated Analytics.
▪ Better analyzation & model advancement; higher levels of security.
Additional New Basic Features in 3.1

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Summary
▪ In this lesson, you learned about the added business value of the latest updates
of SAP’s Predictive Analytics 3.0 to version 3.1.
▪ The next lesson will introduce an in-depth look at the various features of
SAP’s BusinessObjects, using our real world business use case of a web
application crawling actual data from crime reports & arrests in a specific region
for 2009.
▪ Lesson three will investigate the possible real world crime prediction features
available for this data for 2010 via SAP BusinessObjects Predictive Analytics.

18. Success – You Completed The Lesson!
10/10Copyright © Blackvard Management Consulting – All rights reserved www.blackvard.com
Congratulations, you completed the lesson!
In this lesson you successfully learned:
1. SAP BusinessObjects Predictive Analytics 3.1 Introduction
2. Automated Analytics/Expert Analytics/Predictive Factory/Time
Series Forecasting
3. Big Data – Spark & SparkSQL
4. SAP HANA VORA
a) SAP HANA Support
5. Additional New Features
6. Summary & Next Lessons
7. Contact Us

19. Copyright © Blackvard Management Consulting – All rights reserved www.blackvard.com
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