This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM journey -> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain

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Workshop: Overcoming the Five Challenges of your MDM journey
Presented by: Didier Josephine & Jean-Michel Franco

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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

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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
Evolution path
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

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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

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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
Modeling
Managing the
data quality
Enabling stewardship
Integrating & propagating the data
Operationalizing
the master 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- Commerce
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

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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
Managing the data quality
Enable stewardship
Integrating & propagating the data
Operationalizing
the master 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
Managing the Data Quality
Enable stewardship
Integrating & propagating the data
Operationalizing
the master 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

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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 ServicesData StagingMetaDataRepositoryWeb LayerHybrisTCP/IP - KereberosLegendCustomer Data Management – Static ArchitectureIntegration ServicesBatchAdaptorsReal-timeAdaptorsReal time data servicesFile basedMasterRepository@ComResACDSPegaTracsVisionData Quality ServicesTalend Integration PlatformParsing& enrichment(Experian) MatchingServicesBatch data servicesData LayerMaster Data GovernanceTalendAdministrationData QualityDashboardMigrationAdaptorsStandardisation Services Integration Layer ActiveDirectorySOAP over JMSGetCustomerDetailsCoreGeCustomerinteractionsCreateCustomerUpdateCustomerPublishCustomerGetCustomerEngagementsGetCustomerProfileSearchCustomerMatchCustomerPublishCustomerMerge Integration Layer MatchCustomerBulkSOAP over HttpTalend ESB

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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
Managing the Data Quality
Enable
stewardship
Operationalize
the master data
Operationalize
the master data

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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

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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)

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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
Managing the Data Quality
Enable stewardship
Integrating & propagating the data
Operationalizing
the master 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

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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

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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…

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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

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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

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Thank your for your attention
Overcoming the Five Challenges of your MDM journey
Contact us: jfranco@talend.com
djosephine@talend. com
Learn more: www.talend.com.product/mdm