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 Ferguson
Managing Director
Intelligent Business Strategies
Denodo Webinar
June 2016

2
Copyright © Intelligent Business Strategies 1992-2016!
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.
www.intelligentbusiness.biz
mferguson@intelligentbusiness.biz
Twitter: @mikeferguson1
Tel/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
Profit
Margin

5
Where In The Value Chain Can You Make Improvements To Improve
Profitability?
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 profitability
The increasingly complex data landscape
virtualisation
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
Marketing
System
Customer
Service
System
HR Gen.
Ledger
Procurement
system
Billing
system
Fulfilment
System
Sales
System
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 cloud
Private or public cloud
Enterprise Service Bus
Enterprise PortalMashups Office Applications
SaaS BI
Off-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
social graph
data
RDBMSFiles
clickstream
Web logs social data
Graph
analytics tools
Enterprise Information Management
event streams
Stream
processing
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
Packaged
applications
Office
documents
Legacy
applications
BI
systems
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?
The impact on the business of distributed data
virtualisation
data warehouse
 Conclusions

16
Core Business Processes Often Now Execute Across A Hybrid Computing
Environment
order
credit
check fulfil ship invoice paymentpackage
Process Example - Manufacturing Order to cash
schedule
Order
entry
system
Finance
credit
control
system
Production
planning &
scheduling
system
CAM
system
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 3
Channels/
Outlets
order credit
check
order credit
check
Order
(product line 1)
Order
(product line 2)
Order
(product line 3)
Enterprise

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 3
Product 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
Accounts
Assets
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,
Materials
Supplier
Master data
Planning
ERP ERP CAD
Manufacturing
execution system
Shipping
systemSCADA
systems
SCM
CRM
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
Product,
Materials
Supplier
Master data
Planning
ERP ERP CAD
Manufacturing
execution system
Shipping
systemSCADA
systems
Manufacturing
volumes &
inventory DW
Finance DW
Sales & mktng
DW
SCM
CRM
system
Management reporting?
KPIs?

23
Data Inconsistency Across DW Systems Is Common
BI tool BI tool
DW
mart
BI tool BI tool
DW
mart
BI tool BI tool
DW
mart
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 Impact
Very high total cost of ownership
No Agility – Takes time and is costly to change
Risk of inconsistency across DWs and marts
No drill down across marts
No 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
Streaming
data
Hadoop
data store
Data Warehouse
RDBMS
NoSQL
DBMS
EDW
DW & marts
NoSQL Graph
DB
Advanced Analytic
(multi-structured data)
mart
DW
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
development
Investigative
analysis,
Data refinery
Data mining,
model
development
Graph
analysis
Graph
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
What is data virtualisation?
virtualisation
data warehouse
 Conclusions

29
How Does Data Virtualization Work?
Example – Integrated Claims Information
Data sources
Oracle
Customer
Data
DB2
Claims
SQL Server
Payment
JSON
Incidents
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
Virtualization
Server
Results can come back
as a SQL result set,
XML, HTML, CSV etc.
web contentRDBMS
XML
File
2. 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 contentRDBMS
XML
File
SQL, X/Query, Web services
Application
or BI tool
Virtual table
Each federated
query dynamically
creates the virtual
table at run time
Portal
Data
Virtualization
Server
Virtual table
WWW
Spread
sheets
mapping mappingmappingmapping mapping

32
Improving business performance and agility using data
virtualisation
data warehouse
 Conclusions

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
Benefits
Simplified access
Access to real-time data across the process
Agile and responsive
Avoid unplanned operational costs
See across multiple instances of apps
See 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 3
Channels/
Outlets
order credit
check
order credit
check
Order
(product line 1)
Order
(product line 2)
Order
(product line 3)
Enterprise
Datavirtualization
Datavirtualization
Datavirtualization

35
Data Virtualization Helps You Quickly See Across Your Value Chain
Making Planning Much Easier
Fore-
casting
Product,
Materials
Supplier
Master data
Manufacturing
volumes &
inventory DW
Planning
ERP ERP
Finance DW
Shipping
system
CRM
system
Sales & mktng
DW
SCADA
systems
Data virtualization
SCM
Manufacturing
execution system
CAD
Benefits
Management Reports easy to produce
See real-time data across value chain
Easier to do planning
Dynamic planning on real-time datacost
Agility

36
Data Virtualization Allow Simplification Of Data Warehouse Architecture
And Reduced Total Cost Of Ownership
DW
ETL
Operational
data
Agile BI = Agile Architecture
Data virtualization can easily
accommodate change and
speed up time to value
Data visualisation
BI tools
Virtual ODS
virtual mart
personalised
virtual views
virtual mart
personalised
virtual views
Data Virtualization
data
mart cube
ODS
ETL
ETL
ETL
personal
data 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 model
Easy to change
Quickly produce insight
100% agile development
Bi Tool
Build prototype
Data virtualisation
OLTP
OLTP
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 deployment
Re-use BI tool reports and dashboards
Reuse Data Virtualization metadata in ETL
Re-use data
DW
Virtual data model
Easy to change
Quickly 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 access
Quick to implement
Non-disruptive
Agile

41
virtualisation
Reducing time to value and increasing revenue in the logical
data warehouse
 Conclusions

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
Streaming
data
Hadoop
data store
Data Warehouse
RDBMS
NoSQL
DBMS
EDW
DW & marts
NoSQL Graph
DB
Advanced Analytic
(multi-structured data)
mart
DW
Appliance
Advanced Analytics
(structured data)
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
Advanced Analytics
(structured data)
Self-Service
BI tool
Advanced
Analytics
Streaming
data
RT Analytics
C
R
U
prod 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
DW
D
I
e.g. Deriving insight from social web sites like for sentiment analytics
new
insights
OLTP systems
sandbox
Data Scientists
social
Web
logs
web cloud
DataVitualisation(LogicalDW)
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
DW
D
I
OLTP systems
SQL on
Hadoop
Archived data
Archiveunused
ordata>nyears
Virtual marts
Virtual views of
DW & Big Data

47
Logical DW - Real-time Data From NoSQL DBMSs Can Also Be
Joined To DW And Big Data Using Data Virtualization
DW
D
I
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
Virtual marts
Virtual views of
DW & Big Data

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
DataVirtualisation

49
LDW, Governance And Self-Service DI – Data Virtualisation Reduces Copying,
Enforces Security And Increases Agility
C
R
U
prod client
asset
D
MDM Systems
C
R
Urisk
rates
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 Cloud
XML,
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
www.intelligentbusiness.biz
mferguson@intelligentbusiness.biz
Twitter: @mikeferguson1
Intelligent Business Strategies Limited
Wilmslow, Cheshire, England
Tel/Fax (+44)1625 520700
Thank You!

Data Virtualization Use Cases
Mark Pritchard, UK Director Sales Engineering
June 2016

Data Virtualization
Introduction 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 Challenge
Data Is Siloed Across Disparate Systems
MarketingSales ExecutiveSupport
Database
Apps
Warehouse 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 Solution
Data 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 Virtualization
Real-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 Integration
Lower integration costs by 80%.
Flexibility to change.
Real-time (on-demand) data services.
Complete Information
Focus on business information needs.
Include web / cloud, big data,
unstructured, streaming.
Bigger volumes, richer/easier access to
data.
Better Business Outcome
Projects 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 Cases
Data 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 Studies
Data 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 Study
UK General Insurance Company

62
• Opportunities in New Markets
• Changing Customers
• New Competition
• Proliferation of Channels
• Regulatory Change
• New Technology
Market Trends Changing Industry Approach
Traditional
Evolving
Exposure
Scale
Underwriting
expertise
Competitive
advantage
Creative
data
sourcing
Distinctive
analytical
method
Competitive
advantage

63
• 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
Target State Agile BI Architecture
DataSources
Virtual
Staging
Virtual
Semantic
AtomicWarehouse
VisualisationAnalytics

Example Use Case

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
Use Case: Agile Operational Reporting

67
XSLT
Original Approach
Data Virtualization Solution

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 Study

70
Issues
Data 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 Services

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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