© Talend 2014 1 Workshop: Overcoming the Five Challenges of your MDM journey Presented by: Didier Josephine & Jean-Michel Franco
Jun 10, 2015
© Talend 2014 1
Workshop: Overcoming the Five
Challenges of your MDM journey Presented by: Didier Josephine & Jean-Michel Franco
© Talend 2014 2
Your Interlocutors
Jean-Michel Franco Director, Product Marketing
Didier Joséphine Sales Engineer, MDM expert
Key Facts about Talend
• Founded in 2006
• 400 employees in 7 countries
• Highly scalable integration solutions addressing Big Data, Application Integration, Data Integration, Data Quality, MDM, BPM
• Dual HQ in Los Altos, CA and Paris, France
• Open Core business model
• Subscription license
• Services & training
2007 2008 2009 2010 2011 2012 2013
© Talend 2014 3
OVERCOMING THE FIVE CHALLENGES
OF YOUR MDM JOURNEY
Master Data Management 101 The five challenges to deliver on the promises of MDM - Modeling Agility: creating the single version of the truth - Data Accuracy: managing the data quality - Lines of Business Accountability: establishing data stewardship - Data Accessibility: Connecting enterprise sources and beyond - Master Data Actionability: connecting to processes, real time Outlook and future trends Wrap-up
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Master Data Management is a
cornerstone for data-driven processes
Know Your Customer
Know Your Products
Know Your Suppliers
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Talend
MDM
Customers
Suppliers
Products
Assets Agencies Stores
Organiza-tions and Reference
Data
Employees
MDM is about creating and managing the
golden records of your business
What ?
(44%)
Who ?
(33%) How ?
(21%)
Where ?
(3%)
Number sources : Gartner
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Definition
Master data management (MDM) is the process of creating a single point of
reference for highly shared types of data, including customer, products,
suppliers, sites, organizations and employees.
Master data management requires companies to create a single view of their
shared master data asset. It then links together multiple data sources, and
ensures the enforcement of policies for accessing and updating the master
data, handling data quality and the routing of exceptions to people.
This “Data Stewardship” capability allows the lines of businesses to take
ownership of the content they need for their data centric processes. Once a
single view is created, that data can be operationally applied, and eventually
in real-time, to business problems and opportunities.
MDM is a strategic initiative for data-driven organization seeking to improve
business results such as better customer experience and service, increasing
cross-sell and up-sell revenue, and streamlining supply chains.
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The journey from Data Integration to Information
Governance
From a fully IT driven model… …to a federated and collaborative
responsibility model
IT Lines of Business
Evo
lutio
n p
ath
From Data Management… …to Information Governance
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The Business cases for MDM
M&A and restructuring
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360° Views
Managed Data Accuracy
Collaborative Data
Governance
Information Accessibility
Information Accountability
MDM Platform
Governance, Risk Compliance and fraud mgmt.
Just-in-time and lean operations
Customer centric
processes
Customer Experience
Management
Time to market
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MDM : why change? why now? And how ?
Source : Gartner 2014 survey Enterprise Information and MDM
MDM is a hot topic
• in top 3 initiative for 50% of IT execs
There is a urgent need to refresh current processes linked to master data
• Ratings of the current capability: 3,6 on 7 ; average for 79%; poor for 21%
A lot of companies have engaged, but most are at early steps
• 61% still on planning/prototyping phases
Only 49% have a clear business case
• and 31% through an ROI model
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Why MDM ?
https://info.talend.com/tdwinextgen.html
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So Where to start your journey to data
governance ?
Define your business needs and your roadmap
Set up your stewardship organization
Design the platform
Engage your MDM programs
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Turning MDM from a discipline to a program
“The biggest observed change entails a shift from organizations viewing MDM as an abstract discipline to treating it a tangible program. The successful organizations exhibit the later” Bill O Kane
Discipline Program
Vision What can be done What we will do
Goals Monolithic and long term Incremental and Time-Phased
Metrics General Specific to each project/process
Governance What is quality data How to fix it
Organization Data Stewardship Accountability and leadership
Technology Keeping the golden records
Promoting collaboration and communication
Sources : Gartner maturity model and MDM presentations
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Organizing for MDM : best practices
1. State the problem you're trying to address.
2. Determine the project's mission and business value, and link the initiative to actionable insights.
3. Devise a good IT strategy.
4. Business users must take full ownership of the master data initiative.
5. Align success criteria for MDM across the organisational chart
6. Pay attention to organizational governance and change management.
7. Develop Master Data Services for Application Integration
8. Map business needs to technology acquisition
https://info.talend.com/mdmlisttdwi.html
http://www.informationweek.com/big-
data/big-data-analytics/7-master-data-
management-project-best-practices/d/d-
id/1107222
© Talend 2014 17
OVERCOMING THE FIVE CHALLENGES
OF YOUR MDM JOURNEY
Master Data Management 101 The five challenges to deliver on the promises of MDM - Modeling Agility: creating the single version of the truth - Data Accuracy: managing the data quality - Data Accessibility: Connecting enterprise sources and beyond - Lines of Business Accountability: establishing data stewardship - Master Data Actionability: connecting to processes and application, real time Outlook and future trends Wrap-up
© Talend 2014 18
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Key objectives for successful MDM
design
Modeling Agility
Data
Accuracy
Data steward-
ship
Data
Integration
Data actionability
• Unified views
• Embedded Rules and
Controls
• Role based access
• Creating master
data services
• Connecting to
systems, real time
• Profiling for new data
sources
• Standardization & matching
• Quality analytics and control
• Authoring and user
interfaces
• Tasks management &
resolution
• Workflows and BPM
• Integrating and cross
referencing internal
systems
• Augmenting with external
data
MDM
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Modeling your data
Key steps to consider
• Creating the data model
• Defining the business rules
• Defining Data Validation controls
• Defining the roles , and the security
Mo
de
ling
Man
agin
g th
e
dat
a q
ual
ity
Enabling s
tew
ard
ship
Inte
gra
ting &
pro
pagati
ng t
he d
ata
Opera
tionalizin
g
the m
ast
er
data
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Organizing for MDM: Defining the implementation
Style
MDM
ERP CRM
COTS
DWH
Consolidation
MDM
ERP
SFA
CRM
DWH
Centralized
MDM
CRM
E-Commerc
e
Marketing
DWH
Coexistence
MDM
ERP
SFA
CRM
DWH
Registry Less Intrusive Most MDM Configuration Most ESB Configuration
Less Intrusive Standard MDM Configuration
More Intrusive Standard MDM Configuration Optional ESB Configuration
Most Intrusive Moderate MDM Configuration Required ESB Configuration
© Talend 2014 21
Modeling best practices
Functional
Engage heavily the LOBs in the designing effort Reach consensus ASAP on the data definition of golden record Start at the core and keep it simple, then expand Make the model as self explanatory as possible for the business users, and document your business glossary Create your own primary key Manage the design and validation phase carefully, as changing a data model at run time once the data is populated may be a tedious exercise Leverage views and roles for usability
Value:
➜ Establish sustainable foundations for your MDM model
➜ Establish the cornerstone for collaboration (Stewardship and IT integration)
Technical
Create an internal permanent key for Master Data records Define modeling standards and respect them Use a graphic Case tool for the design Establish naming rules Reuse definition, rules and patterns Anticipate the performance impact of controls, enrichment and propagation rules
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Managing the Data Quality
Key steps to consider
• Data Profiling
• Collect the referential to enriching the data
• Defining parsing, standardization, validation
• Defining the matching and survivorship
• Building Address validation rules
Modeling
Man
agin
g th
e d
ata
qu
alit
y
Enable
ste
ward
ship
Inte
gra
ting &
pro
pagati
ng t
he d
ata
Opera
tionalizin
g
the m
ast
er
data
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Use case: Growing the Business With an
Extended Product Portfolio
Challenge:
• Extend the direct supply catalog with a long tail through an online marketplace with millions items
• Delegate administrative task related to product introduction to supplier through a self-service portal
Key capabilities needed :
• Close the gap between the back end application (supplier self service) and the existing front end (Customer facing MDM for product data)
• Data quality and stewardship • Data and application Integration
Value:
• Increased revenue through better exposure of features, benefits and reviews
• Streamlined product on-boarding
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Data Quality best practices
Functional Know your data before starting the design: content, availability volume, typology, reliability, reference data Understand the information supply chain: who creates, imports, update, consumes (and when/where…) Establish strong collaboration with stewards in charge of manual resolution to fine tune your matching algorithms iteratively Define business and project metrics to be monitored over time, in order to size the data stewardship efforts and to show the progress
Value:
➜ Illuminate the data quality problems and its impact for lines of business
➜ Establish clear metrics for measuring the progress and success of the MDM program
Technical
Use a data profiling tool Integrate the data quality rules as gatekeepers in your data integration process Understand the constraints and objective that are behind the matching policies, including performance, impact of mismatches, cost of manual efforts… Anticipate the need for adjustments, including for undoing redoing data resolution activities
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Synchronizing with the existing systems
in batch or real time
Key steps to consider
• Batch/real time, Bulk or incremental load, propagation : defining the integration policies
• Integrating with applications: internal, cloud based, external
Modeling
Managin
g t
he D
ata
Quality
Enable
ste
ward
ship
Inte
gra
ting &
pro
pagati
ng t
he d
ata
Opera
tionalizin
g
the m
ast
er
data
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Challenge:
Support hyper growth of members in a non profit and highly regulated healthcare market
Re-engineering customer facing processes
Use case: Re-engineering member relationship
in a heavily regulated environment
Key capabilities need:
Start with strong Data quality and data reconciliation capabilities Manage external data standards and connect in real time with exchanges in the healthcare industry Implement workflow driven processes for customer facing activities (on-boarding, claims, billing…)
Value:
• Compliance (with HIPAA regulations) • Scalable processes to meet hyper growth (+250%
members acquisition rate) • Lower TCO and automated processing
© Talend 2014 27
Integration best practices
Functional
Define the integration architecture and the decision criteria to inform data integration scenarios for each source and targets
Design the integration layer as a moving object that will have to evolve on a regular basis, with its own lifecycle (new systems to connect, upgrades…)
Use design mechanisms like publish and subscribe or Master data services to avoid dependencies between system and have clear segregation of duties
Value:
➜ A shared service to bring trusted data across your IT trough a well defined and rapid to deploy process
➜ Manage change info your MDM program and take advantage into new sources of data and accelerate the roll-out of new applications
Technical
Invest on productivity and change management tools, since this makes a substantial part of your TCO
Identify the volume now…and for the future
Identify the MDM multiple environments
Define procedures for Delivery between environments
Integration Services
Data StagingMetaDataRepository
Web Layer
Hybris
TCP/IP - Kereberos
Legend
Customer Data Management – Static Architecture
Integration Services
BatchAdaptors
Real-timeAdaptors
Real time data
services
File based
MasterRepository
@ComRes
ACDS
Pega
Tracs
VisionData Quality Services
Talend Integration Platform
Parsing& enrichment
(Experian)
MatchingServices Batch data
services
Data Layer
Master Data Governance
TalendAdministration
Data QualityDashboard
MigrationAdaptors
Standardisation Services
Inte
grat
ion
Lay
erActive
Directory
SOAP over JMS
GetCustomerDetailsCore
GeCustomerinteractions
CreateCustomer
UpdateCustomer
PublishCustomer
GetCustomerEngagements
GetCustomerProfile
SearchCustomer
MatchCustomer
PublishCustomerMerge
Inte
grat
ion
Lay
er
MatchCustomerBulk
SOAP over Http
Talend ESB
© Talend 2014 28
Engage your Lines of Businesses
Key steps to consider
• Organize data stewardship tasks by roles
• Managing the day to day tasks related to master data
• Accessing and authoring the master data
• Defining the workflows for collaborative authoring
Modeling
Managin
g t
he D
ata
Quality
Enable
stew
ard
ship
Opera
tionalize
the m
ast
er
data
Opera
tionalize
the m
ast
er
data
© Talend 2014 29
Use case: Monetizing content and increasing
ARPU in the media industry
Challenge:
• Manage 28,000 hours of multimedia content delivered monthly from 340 content providers to 75 million households
Value:
• Increase ARPU (Average Revenue Per User) and improved customer experience with data to promote the movies
• Decreased costs and time for adding new content to the movie catalog
Key capabilities needed :
• Start with Data Integration and data quality to deliver quickly an improved centralized catalog
• Progressively replace a non intrusive a posteriori process to reconcile data and manage errors with a reengineered collaborative process driven by workflows
© Talend 2014 30
Best practices for Data Stewardship
Functional
Define and document the data governance policies (incl inventories roles, permissions, workflows)
Make sure that the lines of businesses are engaged and accountable
Define clear roles & tasks for data stewards and define their working environment and workflows accordingly ;
Engage the data stewards early in the project, well before the training and roll-out phase
Value:
➜ Engage the lines of business in the success of data centric initiatives
➜ Organize for a MDM roll-out and continuous improvement
Technical
Integrate the people driven tasks related to data authoring, validation and correction into the overall landscape, rather than as a separate flow
Target the right environment for the right roles (designers, data stewards, authors and contributors, end users)
© Talend 2014 31
To BPM or not to BPM ?
Functional ➜ Clearly identify the actors ➜ Nominate champions for roles and involve them in
the project to define the processes and activities ➜ Use agile methodologies to define the workflows
and interfaces ➜ Carefully design the users interface ➜ Leverage Business Activity Management for alerts
and continuous improvement
When to use BPM in MDM projects ?
MDM has the lead for data authoring Lines of businesses are highly engaged Business users are involved in the authoring process -> need for guided procedures There are clear links between MDM and business processes (e.g.: onboarding a customer/employee, referencing a product…).
Technical
Use a BPM tool that can go beyond pure MDM authoring capabilities
Keep it simple and anticipate frequent change since people centric processes are subject change and to deal with exception much more frequently that automated processes
Don’t underestimate efforts and time related to the user interface
Value: • Re-engineer your processes with a data centric
approach
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Making MDM actionable
Key Capabilities
• Integrate Master Data Services real time into processes
• Bring context into applications such as Big Data, web or Mobile Applications
Modeling
Managin
g t
he D
ata
Quality
Enable
ste
ward
ship
Inte
gra
ting &
pro
pagati
ng t
he d
ata
Opera
tionalizin
g
the m
ast
er
data
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Best practices for Operationalizing the
Master data
Functional
Identify the touch points where you need to integrate MDM data services, and prioritize the roll out interactively.
Define metrics to show the business impact, e.g. on transformation rates, click rates…
Understand the performance and availability impact of invoking MDM real time for the external applications
Define a small set of reusable, well documented master data services
Connect your master data to your Big Data via Entity Resolution to boost the relevance of your bog data analytics
Value:
➜ 360 view are populated at the right time, right place, when insights or actions are needed.
Technical
Closely integrate this capability into your existing enterprise service bus capability
Define Service level agreements for the MDM services and monitor them closely
Create sets of tests cases to industrialize and automate the testing capabilities
MDM
Business Applications
Mobile Applications
Big Data
Web applications
© Talend 2014 34
Use Case Bring Actionable Customer Data across
Touch Points in Travel & Transportation
Challenge:
Drive loyalty and customer retention in an industry disrupted by digital transformation
Key capability needed:
• Fast & easy collection, cleansing and reconciling of data for 15 million customers
• Definition of Master data services to bring customer context and progressive delivery across touch points in a real time mode
Value:
➜ Improved marketing, sales and service through knowledge and personalization
➜ Better transformation rates, cross sell/upsell ➜ Multi-Channel consistent Customer
Experience
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Example in CRM: the customer data platform Multiple customer touch point, many innovative offers, but broken
customer journeys
Customer Data
Platform
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Building the « customer data platform » to get
a true Customer 360° view…
Customer Data
Platform
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From customer 360 view to the
customer timeline Get the
loyalty card
Clicks for
The coupon
Receive a
promotion
Orders
On line
Complain
Searches
For television
Connect
to wifi
Search
In amazon
Acquires
television
© Talend 2014 38
From clickstream to customer analytics and to real time recommendations
From analytics to actionable recommendations
• Create personalized journeys - Personalization for outbound marketing (e-mails, SMS,
mobile notifications…)
- Real time recommendations for inbound marketing (mobile, web…)
- Next best actions for the field (contact center, clienteling at the point of sales…)
• Customer touch-points are integrated iteratively into real time scenarios
• Business benefits: Sales efficiency is improved, and every marketing activities (campaigns, promotions…) can be measured at a very fine grain -> click rates, transformation rates, campaign effectiveness…
© Talend 2014 39
OVERCOMING THE FIVE CHALLENGES
OF YOUR MDM JOURNEY
Master Data Management 101 The five challenges to deliver on the promises of MDM - Trends and wrap-up Wrap-up
© Talend 2014 40
Trends in MDM
Ten priorities to guide organizations into next generation MDM
1. Multi-domain MDM
2. Multi department, multi application MDM
3. Bi-directional MDM
4. Real time MDM
5. Consolidating multiple MDM Solutions
6. Coordination with other disciplines
7. Richer Modeling
8. Beyond Enterprise Data
9. Workflow and Process Management
10.MDM solutions build atop vendor tools and platforms
Source : TDWI next generation MDM
Key technologies challenges for next generation MDM
1. Complex relationships
2. Mobile
3. Social
4. Big Data
5. Time-travel
6. Cloud
7. Action enablement
8. Real time
9. Extreme scalability
10.Proactive, integrated governance
Source : The MDM Institute
© Talend 2014 41
Thank your for your attention
Overcoming the Five
Challenges of your
MDM journey
Contact us:
djosephine@talend.
com
Learn more:
www.talend.com.p
roduct/mdm