improving agility while widening profit margins using data virtualization

Improving Agility And Profitability Using Data Virtualization- The impact of the Logical Data Warehouse on the value chain

Mike FergusonManaging DirectorIntelligent Business StrategiesDenodo WebinarJune 2016

2

About Mike Ferguson

Mike Ferguson is Managing Director of Intelligent Business Strategies Limited – a UK based boutique IT research and consultancy firm. As an independent IT industry analyst and consultant he specializes in business intelligence, data management and enterprise business integration. With over 35 years of IT experience, Mike has consulted for dozens of companies, spoken at events all over the world and written numerous articles. Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of DataBase Associates.

[email protected]

Twitter: @mikeferguson1Tel/Fax (+44)1625 520700

3

Topics

Improving profitability The increasingly complex data landscape The impact on the business of distributed data What is data virtualisation? Improving business performance and agility using data

virtualisation Reducing time to value and increasing revenue in the logical

data warehouse

Conclusions

4

Improving Profitability Is The Goal Of Every Business

Revenue

Cost

Profit Margin

5

Where In The Value Chain Can You Make Improvements To Improve Profitability?

Revenue

Cost

Profit Margin

How can I improve

profitability in the value

chain?

1. Need to improve customer insights for retention and growth

2. Need to optimise business operations

6

Increasing Revenue – How?

Customer centric organisation and operating model Customer configuration of products and service

• Build or ship to order

Complete 360o insight of a customer Targeted precision marketing for cross-sell / up-sell Predictive and prescriptive analytics Highest quality of customer service Customer self-service via digital channels Customer centric data integration Complete view across value chain

7

Reducing Cost – How?

Reduce business complexity• Reduce location complexity• Reduce business function complexity e,g. across business units• Reduce management complexity – flatten hierarchies• Reduce product complexity• Reduce process complexity• Reduce IT system complexity AND modernise IT systems• Reduce data complexity

Automation and digitalisation across channel and lines of business Improve performance management

• Map cost centres and cost types across the value chain and all entities• Track the business impact of new initiatives

Optimise operations to reduce unplanned down time and costs

8

Topics – Where Are We?

Improving profitabilityThe increasingly complex data landscape The impact on the business of distributed data What is data virtualisation? Improving business performance and agility using data


data warehouse

Conclusions

9

The Data Landscape Is Becoming Increasingly Complex And Lack of Integration Are Working Against Business

Line of business IT initiatives when there is a need for enterprise wide

common infrastructure

Multiple copies of data

Processes not integrated

Different user interfaces

Server platforms complexity

Duplicate application functionality

Point-to-Point “Spaghetti” application integration

MarketingSystem

Customer ServiceSystem

HR Gen. Ledger

Procurementsystem

Billingsystem

FulfilmentSystem

SalesSystem

Gen. Ledger

10

On-Premise Systems Within the Enterprise

Complexity Is Increasing Further As Companies Adopt and Deploy A Mix of On-Premise, SaaS and Cloud Based Systems

employeespartners customers

Private cloudPrivate or public cloud

Enterprise Service BusEnterprise PortalMashups Office Applications

SaaS BIOff-premise OLTP apps

OLTP Systems

WWW

corporate firewall

Data is now potentially fractured even more than before

BI/DW Systems

11

Data Complexity: The Internet of Things (IoT) Is Resulting In The Emergence of Hundreds of New Data Sources

High velocity, high volume data

12

Analytical Data Complexity – More And More Appliances Appearing On The Market Causing ‘Islands’ of Data

Oracle Exadata

IBM PureData System for Analytics

Pivotal Greenplum DCA

Teradata

13

Big Data Is Also Now In The Enterprise Introducing More Data Stores e.g. Hadoop, NoSQL, Analytic RDBMS

Graph DBMS MPP Analytical

RDBMS

BI tools

SQL

indexes

Search based BI tools

Custom Analytic apps

Spark & MR BI tools

OLTP data Unstructured / semi-structured content

actions

users business analysts developers

real-time

DW

SQL

social graph data

RDBMSFiles

clickstream

Web logs social data

Graph analytics tools

Enterprise Information Managementevent streams

Streamprocessing

IoT, markets

14

The Challenge Of Fractured Data In The Value Chain

Data in different locations Data in different data storage

technologies Data in different data structures Different data definitions for the

same data in different data stores Some data too big to move Different APIs and query languages

needed to access data Excessive use of ETL to copy data

• Expensive and not agile

Synchronization nightmare

<XML>Text</XML>

Digital media

RDBMSs

Web content

E-mail

Flat files

Packagedapplications

Office documents

Legacyapplications

BIsystems

Big Data applications

Cloud based applications

ECMS

“Where is all the Customer Data?”

Accessing, governing and managing data is becoming increasingly complex as it

becomes more distributed

15

Topics– Where Are We?

Improving profitability The increasingly complex data landscapeThe impact on the business of distributed data What is data virtualisation? Improving business performance and agility using data


data warehouse Conclusions

16

Core Business Processes Often Now Execute Across A Hybrid Computing Environment

ordercredit check fulfil ship invoice paymentpackage

Process Example - Manufacturing Order to cash

schedule

Order entry

system

Finance credit

control system

Production planning & scheduling

system

CAMsystem

Inventory system

Distribution system

Billing Gen Ledger

Orders data Customer data Product data

This makes data difficult to track, maintain, synchronise and manage

17

XYZ Corp.

Many Companies Have Business Units, Processes & Systems Organised Around Products and Services

Customers/Prospects

Product/service line 1

order credit check

fulfill ship invoice paymentpackage


Product/ service line 3Cha

nnel

s/O

utle

ts

order credit check


order credit check


Order(product line 1)



Enterprise

Fractured data th

at may a

lso be

duplicated and in

consis

tent

18

Business and Data Complexity Can Spiral Out Of Control if Processes And Systems Are Duplicated Across Geographies

Product line 1

Product line 2

Product line 3

Product line 1

Product line 2

Product line 3Product line 1

Product line 2

Product line 3

Product line 1

Product line 2

Product line 3

Product line 1

Product line 2

Product line 3

Suppliers

Products/Services

AccountsAssets

Employees

Customers Partners

Materials

19

Business Implications Of Product Orientation and Fractured Customer Data In A World Where Customer Is Now King

Different marketing campaigns from different divisions aimed at the same customer

Different sales teams from different divisions selling to the same customer Customer service is hard

• e.g. “What is my order status for all products ordered?”

Cost of operating is much higher due to duplicate processes across product lines

Can’t see customer / product ownership Can’t see customer risk and customer profitability Higher chance of poor data quality Difficult to maintain customer data fractured across multiple applications

20

This Makes It Difficult To Access And Report on Data Across The Process To Manage Business Operations

order credit check


Order-to-Cash Process

Orders

What order changes in the last 10 mins? What shipments are impacted by the changes

e.g. lack of inventory or shipping capacity?Which customers are affected?

Operational reporting is not timely

Inability to respond quickly to problems

Problems not seen until long after they happen

e.g. incorrect shipments

Operational oversights cause processing errors &

unplanned operational cost

Inability to see across multiple instances of a system can cause

errors & duplication of effort

Business impact

21

Planning Also Requires Data From Across A Value Chain

Fore-casting

Product, MaterialsSupplier

Master data

PlanningERP ERP CAD

Manufacturing execution system

ShippingsystemSCADA

systems

SCMCRM

system

Need to see sales, inventory, shipments, manufacturing

capacity, resources and forecasts

Plans are too resource intensive

Planning slow and is outdated by the time it

is finalised

No flexibility, not dynamic, no scenarios

or simulations

Business impact

22

Multiple Data Warehouses Are Also Common

Fore-casting


Master data

PlanningERP ERP CAD

Manufacturing execution system

ShippingsystemSCADA

systems

Manufacturing volumes &

inventory DW

Finance DW Sales & mktng DW

SCMCRM

system

Management reporting? KPIs?

23

Data Inconsistency Across DW Systems Is Common

BI tool BI tool

DWmart

BI tool BI tool

DWmart

BI tool BI tool

DWmart

Data Integration Data Integration Data Integration

Common data definitions across all tools for the same data?

Common data definitions across all DWs for the same data?

Common data transformations across all DWs for the same data?

24

Many Organisations Have Created Multiple DWs And A Lot Of Data Marts - E.g. Country Specific Data Marts

Sales DW

UK DE FR NL

ESP CH IT BEUS

ETL ETL ETL ETL

ETL ETL ETL ETL ETL

Inventory DW

UK DE FR NL

ESP CH IT BEUS

ETL ETL ETL ETL

ETL ETL ETL ETL ETL

Business ImpactVery high total cost of ownershipNo Agility – Takes time and is costly to changeRisk of inconsistency across DWs and martsNo drill down across martsNo way to compare and drill down European Sales Versus Inventory across the value chain???

25

New Data Sources Have Emerged Inside And Outside The Enterprise That Business Now Wants To Analyse

Web data• Clickstream data, e-commerce logs• Social networks data e.g., Twitter

Semi-structured data • e.g. JSON, XML, BSON

Unstructured content• How much is TEXT worth to you

Sensor data• Temperature, light, vibration, location, liquid flow, pressure, RFIDs

Vertical industries structured transaction data• E.g. Telecom call data records, retail

26

The Changing Landscape – We Now Have Different Platforms Optimised For Different Analytical Workloads

Streamingdata

Hadoopdata store

Data WarehouseRDBMS

NoSQL DBMS

EDW

DW & marts

NoSQL Graph DB

Advanced Analytic (multi-structured data)

martDW

Appliance

Advanced Analytics(structured data)

Analytical RDBMS

Big Data workloads result in multiple platforms now being needed for analytical processing

C

R

U

D

Prod

Asset

Cust

MDM

Traditional query,

reporting & analysis

Real-time stream

processing & decision m’gmt

Data mining, model

developmentInvestigative

analysis,Data refinery

Data mining, model

development

Graph analysisGraph

analysis

27

Business Users Need To Combine Data In These Systems To Get Deeper Insights

MDM System

C

R

U

D

Prod

Asset

Cust

Who are our customers?

What products do we sell?

What is the online behaviour of loyal, low risk, low fee customers so we can offer them higher fee products?

DW

Who are our most loyal, low risk customers that generate low fees?

How do I get at data in multiple analytical

data stores to answer this?

What are the most popular navigational paths through our

web site that lead to high fee products

28


Improving profitability The increasingly complex data landscape The impact on the business of distributed dataWhat is data virtualisation? Improving business performance and agility using data



29

How Does Data Virtualization Work?Example – Integrated Claims Information

Data sources

OracleCustomer

Data

DB2 Claims

SQL ServerPayment

JSONIncidents

Query

Claim status is stored in DB2 Claims payment info is stored in

SQL Server Claims form submitted was

captured and stored in JSON

Data Virtualization

Server

Source: IBM

30

How Does Data Virtualization Work?

Virtual table

1. Define common integrated data model for the virtual tables

3. Define mappings from source systems to common virtual tables

mapping mappingmappingmapping mapping

SQL, X/Query, Web services

4. Query the virtual table(s)

Application, BI tool or portal

Data VirtualizationServer

Results can come back as a SQL result set, XML, HTML, CSV etc.

web contentRDBMSXML

File2. Define the data

sources Spread sheets

WWW

Virtual table

31

Data Virtualization Servers Can Support Multiple Virtual Views AND Nested Virtual Views

Virtual table

Multiple virtual tables can be created if needed

web contentRDBMSXML

File

SQL, X/Query, Web services

Application or BI tool

Virtual tableEach federated query dynamically creates the virtual table at run time

Portal

Data VirtualizationServer

Virtual table

WWWSpread sheets

mapping mappingmappingmapping mapping

32


Improving profitability The increasingly complex data landscape The impact on the business of distributed data What is data virtualisation?Improving business performance and agility using data



33

Data Virtualization Make It Easy To Access And Report on Data Across The Process To Manage Business Operations

order credit check


Order-to-Cash Process

Orders

Data virtualization and Virtual Data Services

BenefitsSimplified access

Access to real-time data across the processAgile and responsive

Avoid unplanned operational costsSee across multiple instances of appsSee across on-premises & cloud apps

cost

Agility

34

XYZ Corp.

Data Virtualisation - See Views Of Customer Orders, Shipments And Payments Across Line Of Business Product Lines

Customers/Prospects


order credit check



Product/ service line 3Cha

nnel

s/O

utle

ts

order credit check


order credit check





Enterprise

Data virtualization

Data virtualization

Data virtualization

35

Data Virtualization Helps You Quickly See Across Your Value Chain Making Planning Much Easier

Fore-casting


Master data

Manufacturing volumes &

inventory DW

PlanningERP ERP

Finance DW

Shippingsystem

CRMsystem

Sales & mktng DW

SCADAsystems

Data virtualization

SCMManufacturing

execution system

CAD

BenefitsManagement Reports easy to produceSee real-time data across value chain

Easier to do planningDynamic planning on real-time datacost

Agility

36

Data Virtualization Allow Simplification Of Data Warehouse Architecture And Reduced Total Cost Of Ownership

DWETL

Operationaldata

Agile BI = Agile ArchitectureData virtualization can easily accommodate change and

speed up time to value

Data visualisationBI tools

Virtual ODS

virtual mart

personalised virtual views

virtual mart

personalised virtual views

Data Virtualization

datamart cube

ODS ETL

ETL

ETL

personaldata store

cost

Agility

Adapted from a slide originally by R/20 Consultancy

37

Data Virtualisation Simplifies Architecture, Reduces Total Cost Of Ownership, Improves Agility, Speeds Development

DW

UK DE FR NL

ESP CH IT BEUS

Country Specific Data Marts

ETL ETL ETL ETL

ETL ETL ETL ETL ETL

Cost

Agility

38

Agile Business Led BI/DW Development – Early Prototyping Using Data Virtualisation

Virtual data modelEasy to change

Quickly produce insight 100% agile development

Bi Tool

Build prototype

Data virtualisation

OLTPOLTP

Business Led Prototype Development

virtual data model

DW

39

Agile BI Development Process Often Starts In The Business And Is Handed Over To IT – DV Helps With Smooth Handover

Bi Tool

DW

Bi Tool

Build prototype

Data virtualisation

OLTP

Data virtualisation

OLTP

OLTP OLTP

ETL

Deploy in production

Business Led Prototype Development IT

deploy

virtual data model virtual data model

physical data model

Fast deploymentRe-use BI tool reports and dashboardsReuse Data Virtualization metadata in ETLRe-use data

DW

Virtual data modelEasy to changeQuickly produce insight 100% agile development

40

Data Virtualisation

Common Data Definitions in A Data Virtualization Server Removes Inconsistencies Across Multiple BI Tools

Common data names and definitions

(shared business vocabulary)

mapping mappingmapping mapping

web contentRDBMS XMLFile

Disparate data names and definitions(different data vocabularies)

, BI tool(vendor X)

WWW

MsgQ

BI tool(vendor Y)

Spread sheets

mapping

Simplified data accessQuick to implement

Non-disruptiveAgile

41


Improving profitability The increasingly complex data landscape The impact on the business of distributed data What is data virtualisation? Improving business performance and agility using data



42

Using Data Virtualisation To Combine Data In These Systems To Get Deeper Insights

MDM System

C

R

U

D

Prod

Asset

Cust

Who are our customers?

What products do we sell?

DW

Who are our most loyal, low risk customers that generate low fees?

What are the most popular navigational paths through our

web site that lead to high fee products

What is the online behaviour of loyal, low risk, low fee customers so we can offer them higher fee products?

How do I get at data in multiple analytical

data stores to answer this?Data Virtualisation

43

Customer 360 – Piecing Together Complete Customer Insight Needs Multiple Platforms And Analytic Workloads

Streamingdata

Hadoopdata store

Data WarehouseRDBMS

NoSQL DBMS

EDW

DW & marts

NoSQL Graph DB

Advanced Analytic (multi-structured data)

martDW

Appliance


Analytical RDBMS

C

R

U

D

Prod

Asset

Cust

MDM

Customer sentiment,

interactions,online behaviour,

& new data

Customer relationships*, social network

influencers

Customer real-time location, product

usage & on-line

behaviour

Customer master data

Customer purchase activity &

transaction history

Customer predictive analytical model development

*customer relationships to organisations, other people, locations, devices, products, phone numbers….etc.

44

Data Virtualisation – The Logical Data Warehouse

Reducing Time To Value – Integrating The Logical Data Warehouse Into The Value Chain

EDW

DW & marts

NoSQL DB e.g. graph DB

mart

DW Appliance


Self-Service BI tool

Advanced Analytics

Streaming data

RT Analytics

C

R

Uprod cust

asset

master data

Analytic Application

Operational Application

(embedded analytics)

45

Logical Data Warehouse - New Insights In Hadoop Can Integrated With A DW And Marts Using Data Virtualization

DWDI

e.g. Deriving insight from social web sites like for sentiment analytics

new insights

OLTP systems

sandbox

Data Scientists

social

Web logs

web cloud

Data V

itualisation (Logical DW

)

SQL on Hadoop

Virtual marts

Virtual views of DW & Big Data

46

Using Hadoop As A Data Archive Means Data Can Be Kept On-line, Analysed And Still Integrated With Data In The DW

DWDI

OLTP systems

SQL on Hadoop

Archived dataA

rchive unused

or data > n years

Data V


)

Virtual marts


47

Logical DW - Real-time Data From NoSQL DBMSs Can Also Be Joined To DW And Big Data Using Data Virtualization

DWDI

Nested data like JSON needs to be handled by the data virtualisation server

real-time insights

OLTP systems

social

Web logs

Column Family DB Document DB

NoSQL DB

sensors

Must flatten nested data !!

SQL on Hadoop

Data V


)

Virtual marts


48

LDW & Data Virtualisation - Sqoop Data Into Hadoop From Multiple Underlying Sources By Using Virtual Tables As A Source

• A fast parallel bulk data transfer tool • It is a data mover rather than an ETL tool• Can do very little data transformation• Can import data from any RDBMS into HDFS, Hive or Hbase• Can export data from Hadoop to RDBMS,& NoSQL systems

HDFS / Hbase/ Hive

Data V

irtualisation

49

LDW, Governance And Self-Service DI – Data Virtualisation Reduces Copying, Enforces Security And Increases Agility

C

R

Uprod client

asset

D

MDM Systems

C

R

Uriskrates pricing

Country codes

D

RDM Systems

Business User

Self-Service DI Important to make sure business users re-use rather than re-invent AND have simplified access to data

RDBMS CloudXML,JSON

web servicesNoSQL Files

Data Virtualisation

IT Data Architect

50

Conclusions

Companies are demanding more agility while the data landscape becomes increasingly more distributed

In addition, the want to reduce costs while also improving customer engagement and growth

Data virtualisation • Allows organisations to see across processes in the value chain to manage the entire

business operations• Helps avoid unplanned operational cost• Reduces complexity, improves agility and reduces total cost of ownership in data

warehousing• Removes inconsistency across multiple BI tools for better decisions • Enables the logical data warehouse • Reduces time to value in better customer engagement and growth

Reducing cost and improving growth improves profitability

51

[email protected]

Twitter: @mikeferguson1Intelligent Business Strategies Limited

Wilmslow, Cheshire, EnglandTel/Fax (+44)1625 520700

Thank You!

Data Virtualization Use CasesMark Pritchard, UK Director Sales EngineeringJune 2016

Data VirtualizationIntroduction to Data Virtualization

54

Information spread across different systems

IT responds with point-to-point data integration

Takes too long to get answers to business users

The ChallengeData Is Siloed Across Disparate Systems

MarketingSales ExecutiveSupport

Database

AppsWarehouse Cloud

Big Data

Documents AppsNo SQL“Data bottlenecks create business bottlenecks.” – Create a Road Map For A Real-time, Agile, Self-Service Data

Platform, Forrester Research, Dec 16, 2015

55

The SolutionData Abstraction Layer

Abstracts access to disparate data sources

Acts as a single repository (virtual)

Makes data available in real-time to consumers

DATA ABSTRACTION LAYER

“Enterprise architects must revise their data architecture to meet the demand for fast data.”

– Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester Research, Dec 16, 2015

56

Data VirtualizationReal-time Data Integration

“Data virtualization integrates disparate data sources in real time or near-real time to meet demands for analytics and transactional data.”

– Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester Research, Dec 16, 2015

Publishes the data to applications

Combines related data into views

Connects to disparate data sources

2

3

1

57

Benefits of Data Virtualization“Get it Real-time and Get it Fast!”

Better Data IntegrationLower integration costs by 80%.

Flexibility to change.

Real-time (on-demand) data services.

Complete InformationFocus on business information needs.

Include web / cloud, big data, unstructured, streaming.

Bigger volumes, richer/easier access to data.

Better Business OutcomeProjects in 4-6 weeks.

ROI in <6 months.

Adds new IT and business capabilities

“Benefits of Data Virtualization: get it real-time and get it fast!” – William McKnight, President, McKnight Consulting Group

Data Virtualization Customer Case Studies

59

Common Data Virtualization Use CasesData Virtualization

BIG DATA, CLOUD INTEGRATION

Advanced Analytics Data Warehouse Offloading Big Data for Enterprise Cloud / SaaS Integration

AGILE BUSINESS INTELLIGENCE

Logical Data Warehouse Virtual Data Marts Self-Service BI Operational BI / Analytics

SINGLE VIEW APPLICATIONS

Single Customer View - Call Centres, Portals Single Product View - Catalogues Single Inventory View – Reconciliation Vertical Specific - Single View of Wells

DATA SERVICES

Unified Data Services Layer Logical Data Abstraction Agile Application Development Linked Data Services

60

Customer Case StudiesData Virtualization AGILE BUSINESS INTELLIGENCE

Major UK General Insurance Company

Streamlined operational reporting on policy admin system.

Exposed all policy data for operational MI and Intra-day reports to all business stakeholders (analysts,sales, actuarial, underwriters).

DATA SERVICES

Global Chip Manufacturer

Delivered data services to streamline business processes across the entire value chain

Achieved faster time-to-market and increased revenue

BIG DATA, CLOUD INTEGRATION

Advanced Analytics Data Warehouse Offloading Big Data for Enterprise Cloud / SaaS Integration

SINGLE VIEW APPLICATIONS

Single Customer View - Call Centres, Portals Single Product View - Catalogues Single Inventory View - Reconciliation Vertical Specific - Single View of Wells

Customer Case StudyUK General Insurance Company

62

• Opportunities in New Markets• Changing Customers• New Competition• Proliferation of Channels• Regulatory Change• New Technology

UK General Insurance CompanyMarket Trends Changing Industry Approach

Traditional

Evolving

ExposureScale

Underwriting expertise

Competitive advantage

Creative data

sourcing

Distinctive analytical method

Competitive advantage

63

UK General Insurance Company

• Cost of change prohibitive to generating value from data• Slower time to market inhibiting impact of good analytics• Complexity of solution can cause delivery bottlenecks• Bespoke coded solutions require unique resource to maintain

Challenges to Agility

Reduce Cost

Flex Resource

Raise throughput

Accelerate change

64

UK General Insurance CompanyTarget State Agile BI Architecture

Dat

a So

urce

s

Virt

ual

Stag

ing

Virt

ual

Sem

anti

c

Atom

ic W

areh

ouse

Visu

alis

atio

nAn

alyt

ics

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Example Use CaseUK General Insurance Company

66

Business Requirement• Daily Operational Reporting• Multiple consumers• Brand new system

Technical Challenges• Complex XML extract • Fast pace of change• XML Schema not available• Varying data items and structure

UK General Insurance CompanyUse Case: Agile Operational Reporting

67


XSLT

Original Approach

Data Virtualization Solution


http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjtg8HG_53KAhWLXBQKHUoCA0gQjRwIBw&url=http://global.qlik.com/fi&bvm=bv.111396085,d.d24&psig=AFQjCNG0ev23EKXra8R7vRRRUSL8M5sHwA&ust=1452472410182881












68


• Cost model reduced• Significantly quicker time to market• Greater throughput• Resource pool expanded

Summary

Reduce Cost

Flex Resource

Raise throughput

Accelerate change

Customer Case StudyGlobal Chip Manufacturer

70

Issues

Global Chip ManufacturerData Service Layer for streamlining business processes in the value chain

“Lack of consistent capability to integrate data from disparate data sources and delivery using agile standardized methods.

• Data is globally distributed across heterogeneous tools & technologies• New data sources (eg: big data) & consumers (eg: emergence of SaaS)• New information exchange channels (eg: mobility) ”.

71

Data Virtualization


“An agile method that simplifies information access”.

72

Benefits


“Reduced time-to-market, increased agility, end-to-end manageability”

Example Data ServicesGlobal Chip Manufacturer

74

Use Case: Supplier Master Data


Supplier Master Data: information about companies that the company purchases from, pays, outsource manufactures with

Choosing a Supplier is the point of entry to many business process. If it fails or is slow, it impacts all 70+ downstream consumers

75

Use Case: MySamples


Need to show the latest status of samples requests. • Customer information from

MySamples app• Samples request

information (if requested) from ERP system

• Samples shipment status (if shipped) from Event Management system

Source: Intel EDW 2015

76

Summary


“An agile data integration method that simplifies information access

–Agility, time-to-market, Manageability & Reuse

Created enterprise standards to promote understandability, reduce chaos, and help drive consistency”

Thanks!

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