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©2014 Talend Inc.

MDM: Why, When, How

Presented by Didier Joséphine and

Jean-Michel Franco

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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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MDM 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 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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Typical challenges during MDM planning cycle

Lack of a solid

Business Case

Lack of readiness

Unclear

Roadmap

Misalignment

between

stakeholders

Unclear

requirements Undefined

Roadmap

Many MDM initiatives

get stuck in their

planning phase

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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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Some misconceptions on MDM

Misconception Key success factor

Massive IT Project (Think Big, Start Big)

Incremental program with engagement from Lines of Business

MDM & integration as separate disciplines

(Start Small, Stay Small)

Total data integration capability for current and future needs

A standalone application (Siloed Approach)

A real time platform to operationalize the master data

Golden record is only based on systems of record like CRM

(Soon to be Outdated)

There will always be new sources of data to give you a better 360 view of

customer--- social, mobile, clickstreams….

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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

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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

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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Taking care of the most precious “resource”

in a citizen community: the children

Challenge:

Need a single view of a child to provide top quality services and value for money on a one to one basis for the local government’s 210 000+ children and their family

Why Talend:

• MDM masters the cross references between public services (education, social care…) and orchestrates data governance to effectively match, merge and un-merge incoming records.

• Complex Data Integration and Data Quality load routines provide sophisticated fuzzy matching.

Value:

Improved public service provided for child protection, through a shared knowledge of each child situation and context

* For Internal Use Only

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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

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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 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

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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

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

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Monetizing content and increasing ARPU in the media industry

Challenge:

Deliver 28,000 hours of multimedia content monthly from 340 content providers targeting 75 million households

Why Talend:

• Flexibility and rapid implementation time • Unified integration platform with

embedded data quality, ESB and Business Process Management

Value:

Decreased costs and time for adding new content to the movie catalog

Re-engineer the billing process to meet compliance mandates and drastically reduce cost and time of operations

* For Internal Use Only

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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

Make sure you don’t transform your MDM into a packaged app : separate data and processes in your design

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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Use case: getting a single view of employee in a highly distributed organization

Challenge:

• 190000+ employees across 100 countries and 400 subsidiaries)

• No global and up to date view of the employees at a global level in a highly decentralized organization

Value:

• shared knowledge of employees at group level and ability to reach them immediately, e.g. communication in crisis situations

Key capabilities needed :

• Strong security, lineage and audit capabilities • Integration to a disparate environment, including

employee directories) • Workflow based authoring (e.g. : professional

transfer)

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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

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Use Case Bring Actionable Customer Data

across touch points

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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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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©2014 Talend Inc.

MDM: why, When, How

Presented by Jean-Michel Franco