Solution Expert Analytics & Data Platform - SAPSA-SAPSA-Impuls... · Solution Expert Analytics &...

Finding the signals in the noise

Niklas Packendorff @packendorffSolution Expert Analytics & Data Platform

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materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements,

which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

Legal disclaimer

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© 2015 SAP SE. All rights reserved.

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4

SAP Big Data PortfolioArchitecture that is logical and mapped to needs Do you have good answers to these questions?

Can we respond flexibly to changing business situations?

Do our business users always get timely, up-to-date and

accurate information?

Which transactions/batch processes run multiple

times a day or are simply too long?

Can we perform scenario simulations or predictive

scenarios?Can we manage the cost and complexity to combine Big Data with ERP/CRM data?

Do the users have access to up-to-date KPIs/reports that they can change quickly, as needed?

Do we have processes that need massive re-work due to data

latency?


5

SAP Data Platform Portfolio

Use whatever tool or UI to view your data

(SAP has a few ready for you)BusinessObjects, Lumira, Custom Application, Predictive Analysis

Use data from any system,

SAP or non-SAP, business or

sensor, POS ...Data Services/SLT

Listen to millions of records

per second and only keep

what is interestingEvent Stream Processor

Connect to many terabytes

and petabytes of backend

dataHadoop integration/Data staging

Query billions of records

combined from many sources

ad-hoc in secondsQuery Engine, Rules Engine

Enjoy fast programs

(using many queries)XS Engine, SQL, SQLScript, JavaScript

Make use of built-in

sophisticated statistical

algorithmsPredictive Analytics Library

Explore statistics ... or let

the system make a best

proposalKXEN InfiniteInsight

Treat text as just another

query aspect. Search and

relate.Text analysis engine

Treat geographic location

as just another query

aspectGeospatial data types

Reuse your exising tools/

models with high-speed

executionSAS integration

Reuse your R models (your

local statistician will know)

with high-speed integrationR integration


© 2015 SAP SE. All rights reserved. 6

Please find more details here: https://blogs.saphana.com/2015/06/16/new-sap-hana-sps10/

The modern, fast & agile data platform

https://blogs.saphana.com/2015/06/16/new-sap-hana-sps10/


Rethinking the Information Supply Chain


Comprehensive Data Provisioning for SAP HANA Conceptual view of real-time high volume data movement from any source

SAP HANA Smart Data Access

SAP HANA Smart Data Streaming

SAP HANA Real-time replication

Any Source SAP HANA Data

ProvisioningTransform and Persist

Data

Other Data Sources (HANA, IQ, ASE, Hadoop,

Teradata, Oracle, MSSQL)

Streaming Event

Sources

Databases and

data marts

Cloud Deployments

SAP and other

Transactional systems

Each of these product provides unique data movement capability for SAP HANA, so

that customers can integrate any data sources into SAP HANA in real time.

SAP HANA Cloud Integration

SAP HANA Smart Data Integration

SAP HANA Smart Data Quality SAP HANA

Virtual TablesHANA Tables


Analytical Views Attribute Views Tables

Calculation Views

HANA Agile data modelingStore data once, re-use in multiple models, avoid duplicate data and aggregates



SAP HANA Smart Data AccessData virtualization for on-premise and hybrid cloud environments

Benefits

Enables access to remote data access just like “local” table

Smart query processing including query decomposition with

predicate push-down, functional compensation

Supports data location agnostic development

No special syntax to access heterogeneous data sources


A platform for Predictive Analytics & Machine Learning

Accelerate predictive analysis and scoring with in-database algorithms delivered out-of-the-

box. Adapt the models frequently

Execute R commands as part of overall query plan by transferring intermediate DB tables

directly to R as vector-oriented data structures

Predictive analytics across multiple data types and sources.

(e.g.: Unstructured Text, Geospatial, Hadoop)


Data visualizationAnalyzing all data and sharing insights to make it actionable

14

eBay Early Signal Detection System Powered by Predictive

Analytics on SAP® HANA

CompanyeBay

HeadquartersSan Jose, California

IndustryProfessional Service (Internet)

ServicesOnline Marketplace

Employees

31,500 (2012)

Revenue or BudgetUS$ 14.1 billion (2012)

Web Site

eBay.com

Business Challenges

Increase ability to separate signal from noise to identify key changes to the

health of eBay’s marketplace

Improve predictability and forecast confidence of eBay’s virtual economy

Increase insights into deviations and their causes

Technical Challenges

Detect critical signals from 100 PBs of data in eBay EDW

Highly manual process because one model does not fit all the metrics hence

requires analyst intervention

Key benefits

Automated signal detection system powered by predictive analytics on SAP HANA

selects best model for metrics automatically; increases accuracy of forecasts

Reliable and scalable system provides real-time insights allowing data analysts to

focus on strategic tasks

Decision tree logic and flexibility to adjust scenarios allows eBay to adapt best

model for their data

“HANA is valuable in the sense that it accelerates that speed to insight.

HANA, with in-memory capability, with multicore, fast, lots of data, all of that coming

together is how I think analytics is going to work broadly in the future.”

David Schwarzbach, VP&CFO eBay North America at eBay Inc.

Determine with

100% Accuracythat a signal is positive at 97% confidence

Automated

Early Signal

Detectionsystem powered by SAP HANA


15

eBay Early Signal Detection System Powered by Predictive

Analytics on SAP® HANA

Architecture DiagramKey SAP Solutions/ Features

SAP HANA Enterprise + R

SAP Business Objects dashboards

Technical KPIs

500 metrics analyzed to identify outliers

Determines 100% accuracy at 97% confidence if signal is

true positive

Implementation

Project Duration: 6 weeks (4 POC, 2 Productize)

Data Go-Live: 05/09/2013

Next Step

Implementation Phase II, exploration of Signal

Causation

EDW

Network

Storage

R Server [100 Petabytes]

Automated Early Signal

Selection System

SAP HANA


Tack!!Niklas Packendorff @packendorffSolution Expert Analytics & Data Platform

17© 2015 SAP SE or an SAP affiliate company. All rights reserved.

© 2015 SAP SE or an SAP affiliate company. All rights reserved.

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company.

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National product specifications may vary.

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affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and

services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as

constituting an additional warranty.

In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop

or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future

developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time

for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-

looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place

undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

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