Marcello Vinci, Center of Excellence, Database & Data Management, SAP EMEA SouthSAP Leonardo Digital Data Framework

CHALLENGES OF BECOMING A DATA DRIVEN ORGANISATION

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Multiple businessapplicationlandscapes

Multiplebusiness

transactionalsystems

Multiple businessdata

warehousessystems

Industry relevantdata models andapplications not

present

Location drivenbusiness context

is missingInability to createagile analytical

models from multiDB / app

environments

Inability tounderstand

transactionaldata complexity

Keyunstructureddata still not

digitised

Uncontrolledcustomer dataauditing andtransparency

Heteregenousdata sources

preventing 360view

Legacysystems that

are difficult toaccess

Inability tomerge &

consolidatecore systems

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BIG DATAWAREHOUSE

S/4FRAMEWORK

LEONARDO(Custom Applications/ Machine Learning /

IoT)

SPATIAL

ITMODERNIZATION

(Legacy Systems)

ANALYTICSDATA MART

INFO &PROCESS

EXCELLENCE

DIGITAL CONTENTTRANSFORMATION

(Opentext)

DATAPROTECTION &COMPLIANCE

(GDPR)

DATA HUB

INDUSTRYINSIGHTS

AGILE ITOPERATIONS

(LandscapeManagement project)

SOLUTION FOR BECOMING A DATA DRIVEN ORGANISATION

PARADIGM OFINNOVATION

DATA SCIENCE

DATAHUB

CLOUD & IOT

GEOSPATIAL

“TRUST SPHERE”BLOCKCHAIN +

GDPR

DATA SOURCES, ACCESS METHODS, AND INFORMATION NEED

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Real time Visibility Consolidated Ad hoc Archive andanalysis

Predictive Sentiment

Real-time datamart

Operationaldata mart

Datawarehouse

Agile data mart Near-line datamart

Predictive datamart

Unstructureddata mart

Streams Direct, databasereplication

ETL and ELTreplication

Federation andETL

ELT, ETL and federation of data andqueries

Sensors, IoT andevents

Transactionalsystems

Transactional and analytical systems SocialSourceAny

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SAP MODERNDATA

FRAMEWORK

SIMPLIFIEDARCHITECTURELOGICAL MODELING

ANALYTICALENGINES

INTUITIVE USEREXPERIENCE

SIMPLIFIEDDATA ACCESS

BI-MODAL LOGICAL DATA MODEL ANALYTICAL ENGINES

THE PROBLEM

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Supply Chain Finance HR Manufacturing Sales ConnectedAssets

BW

Tactical Reports Functional Reports Strategic Reports Innovation Apps

COMPLEXITY INHIBITSDATA-DRIVEN

DECISION MAKING

Today – People manually extract, combine and process data

Open APIs

Custom/ 3rd Party/Packaged Apps

Data Processing& Management

Integration &Virtualization

Multi-layerDistributed Storage

Data Pipelines &Orchestration

DeploymentAnywhere

SAP’S MODERN DATA FRAMEWORK

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CustomApplications R SAP

Business-Objects SAP S/4 HANA SAP AnalyticsCloud 3rd-Party SAP BW Apps

C++

Java

Ruby

SQL

ABA

P

SEM

AN

TIC&

MA

NA

GEM

ENT

Series PredictiveSpatialText GraphVirtual DataMarts

ColumnarXSA Server

SAP

HAN

AST

REA

MIN

GA

NA

LYTIC

S

SAP HANA Data Warehousing Foundation

Data Pipelines | MetadataManagement | Orchestration

Dyn

am

icTi

erin

g

SAP

HA

NA

Sma

rtD

ata

Acc

ess

Rep

lica

tion

EIM

/ET

L

SAP

HA

NA

Sma

rtD

ata

Inte

gra

tion

LOGICALDATA

WAREHOUSE

SAP

HAN

AEx

t.N

odes

Columnar Storage / SAP IQMulti-PB DW

SAP HANA ManagedExtended Storage

3rd-Party External DataSAP Databases

Big Data / Data Lake / HDFS SAP Vora Series,DocStore

Oracle / IBM /Teradata/ Microsoft / Others

ON-PREMISES / CLOUD / HYBRID

Sensor / Device /POS / Transactions / Kafka

Streams / MQ

REAL-TIME SOURCES

Middle Eastern Bank Real-time geo-location specific offers

Business Challenges & Objectives

Engagement Results:

ü Replacement of outsourced monthlyrecommendation reports by in-house real-timeanalysis allows immediate action

ü Instant results pushed via SMS at the right time &location to increase response rates

ü Integration of machine learning insights to Hybrismarketing enables customer 360 view

ü Customer value management calculations andrevenue uplift potential are now performed atcustomer/ vendor/ spending category level acrossseveral TB of transaction data

ü 100+ additional use cases enabled via theplatform => Use Case Roadmap (e.g. Home,Office Identification, Bank productrecommendation, Salary changes identification)

The bank knows a lot about their customers. From transactions, they know what customers buy and like. They know wherecustomers live and work. From ATM withdrawals, they understand a customer’s current location. Without a platform that canleverage geo-spatial information, real-time event data and predictive modeling, they can’t make the best of this knowledge.

�Combining all of this information will allow them calculate the best next offer based on buying habits and deliver these in real-timethrough Hybris when an ATM withdrawal provides location data, improving customer satisfaction and commercial and partneringopportunities with retailers as well as achieve a better understanding of their behavior based on trend and patterns (past behavior,historical effect on the same customer)

Identified Home Location: Al Barsha

Identified Work Location: Deira

Thank you.Marcello Vinci,Center of Excellence Data Base & Data ManagementSAP EMEA South

Thank you!

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