Mdm dg bestpractices techgig dc final cut - copy

www.hitachiconsulting.com

Master Data Management (MDM)
Data Governance Leadership and Best Practices

Dinesh Chandrasekar
Practice Director CRM & MDM
Hitachi Consulting , GDC

© Copyright 2010 Hitachi Consulting 1

Agenda

 Impact of Poor Data & Need for DQ
 Why MDM & Customer Hub
 Customer Data Problems & Solutions
 Significance of Data Governance
 Data Governance Leadership Strategies
 Data Stewardship Best Practices
 Open Forum


Acronyms

 EIM – Enterprise Information Management
 EDM – Enterprise Data Management
 MDM – Master Data Management
 DM – Data Management
 DG – Data Governance
 DQ – Data Quality
 SOR – System of Record
 KPI – Key Performance Indicators
 UCM – Universal Customer Master
 CDH – Customer Data Hub
 PDH – Product Data Hub
 SH – Supplier Hub & Site Hub
 CH – Customer Hub

Commercial in Confidence © Copyright 2010 Hitachi Consulting 3

How clean is your Wind Shield ?

“ Ultimately, poor data is like dirt on the windshield. You may be able
to drive for a long time with slowly degrading vision, but at some
point, you either have to stop and clear the windshield or Risk
everything.” - Ken Orr Institute


Impact of Poor Data Quality
“… Fortune 1000 enterprises will lose more money in “Data integration and data quality are
operational inefficiency due to data quality issues fundamental prerequisites for the successful
than they will spend on data warehouse and CRM implementation of enterprise applications,
initiatives.” such as CRM, SCM, and ERP.”

Operational Efficiency
Customer Service

 Increased data management costs
 Ineffective Cross-sell/Up-sell
 Increased sales order error
 Lower call center productivity
 Delayed sales cycle time (B2B)
 Increased marketing mailing costs
 Mediocre campaign response rate
 Reduced CRM adoption rate

Risk, Compliance Reduced IT Agility
Management

 Heightened credit risk costs  Increased integration costs
 Potential non-compliance risk  Increased the time to bring new projects and services to
market
 Increased report generation costs
 Proliferation of data problems from silos to more
applications


Fragmented data is the source of the problem

Ever proliferating islands of information
…in disparate applications covering multiple
channels, divisions & functions
…duplicated, incomplete, inaccurate data
Call Web Fusion
SFA Center Partner
site App

• Key enterprise processes based on unclean
/ incomplete data
Marketing, sales, service & customer retention
processes, regulatory compliance, new product
introduction,…

• Unclean data makes Analytics invalid
Fusion
ERP 1 ERP2 SCM Legacy
App • Error prone integration
• Slows enterprise agility and innovation


MDM : The source of clean data for the enterprise
Nurture one of your most valuable asset

 Consolidate/Federate shared
information into one place
ETL  Cleanse data centrally

Web
 Share data as a single point of
SFA Call Fusion Partner
Center site App truth as a service

Middleware
Application Integration Architecture MDM BI
Analytics

 Consistency siloed environments
(Integrated Best of Breed)
Fusion  Lower data management costs
ERP 1 ERP2 SCM Legacy
App  Better reporting
ETL  Enterprise foundation for agility
& innovation


The New Age Digital Customer

© Copyright 2010 Hitachi Consulting

Why Customer Hub ?
Unify your Customer View with Customer Hub
Maximize Customer Retention
Provides complete knowledge of customers value and history to improve customer loyalty
Ensures effective marketing and selling while avoiding missteps
Enables sharing of customer information with applications, business processes and point of
contact personnel

Increase Selling Efficiencies
Facilitates accurate up-selling and cross-selling of products and services
Provides accurate product data which reduces order entry errors and decreases days sales
outstanding
Delivers full quality customer and product information at the point of contact

Reduces Cost and Risk
Provides clean data to all applications and business processes increasing ROI from existing
investments
Enables data governance to insure compliance and reduce risk
Accelerates time-to-market of new products and services


Why Organizations engage in Customer Hub Projects?

Benefits

GROWTH EFFICIENCY IT AGILITY COMPLIANCE
Improve CRM Operational Increase IT resiliency Reduce operational
performance to efficiency across in a changing risk and improve
increase revenue and multi-functions of an business landscape regulatory
market share enterprise compliance

CUSTOMERS ON AVERAGE EFFICIENCY OF OPERATIONS EFFICIENCY OF IT EFFICIENCY OF IT OPERATIONS
GENERATED 2%-5% INCREASED INCREASE WITH IMPROVED OPERATIONS RESULTING IN RESULTING IN GREATER
REVENUE FROM SALES WITH PROCESSES AND DATA GREATER AGILITY OF AGILITY OF BUSINESS MODELS
MDM GOVERNANCE BUSINESS MODELS


Customer Hub Styles
Registry Style Consolidation Style Transaction Style
•Various Source System publish • The Consolidation Style MDM • In this architecture, the Hub stores, enhances
their data and a Subscribing Hub has a physically and maintains all the relevant (master) data
Hub stores only the Foreign instantiated, "golden" record attributes.
Keys , Source System Ids and stored in the central Hub • It becomes the authoritative source of truth
Key data values needed for and publishes this valuable information back to
matching • The authoring of the data the respective source systems.
remains distributed across the • The Hub publishes and writes back the
•The Hub runs the cleansing and spoke systems and the master various data elements to the source systems
matching algorithms and data can be updated based on after the linking, cleansing, matching and
assigns unique global identifier events, but is not guaranteed enriching algorithms have done their work.
to the matching records , but to be up to date. Upstream, transactional applications can read
does not send any data back to master data from the MDM Hub, and,
the Source Systems •The master data in this case is potentially, all spoke systems subscribe to
usually not used for updates published from the central system in a
•The Registry Style Hub is to transactions, but rather form of harmonization.
build the “ Virtual Golden View supports reporting; however, it •The Hub needs to support merging of master
of the master entity from the can also be used for reference records. Security and visibility policies at the
Source Systems” operationally. data attribute level need to be supported by
the Transaction Style hub, as well.

Simple & Faster Medium Complex Complex
Short term Gain Mid term Gain Long term Gain


Oracle Enterprise Master Data Management


Gartner Magic Quadrant for Customer Hub Solutions

“UCM has the strength of the Oracle name behind it, leading to an impressive number of
commitments from blue chip names in the Siebel customer base across a range of industries”
John Radcliffe, Gartner, May 2008


Oracle Customer Hub (Siebel UCM) 8.2
Best in Class MDM Solution
Hyperion DRM for Customer Hub

Source
Data Governance Manager MDM
Aware Apps
Systems MDM Analytics
Siebel Siebel

EBS Application
Oracle Customer
Integration
EBS

SAP
Data Quality Hub 8.2

Architecture SAP

JDE JDE

Custom Custom

Operational exchanges
Unclean to clean data(Initial & Delta load)
Hub / Apps


Key Components of Oracle Customer Hub


Example of Customer Data Quality Issue
A Simple Customer Table Sample

Matching Records Non Standard formats

Name Address City State Zip Phone Email

Bob Williams 36 Jones Avenue Newton MA 02106 617 555 000 bob.williams@yahoo.com

Robert Williams 36 Jones Av. MA 02106 617555000
Burkes, Mike and Ilda 38 Jones av. Nweton MA 02106 617-532(9550) mburkes@gmail.com
Jason Bourne,
76 East 51st Newton MA 617-536-5480 6175541329
Bourne & Cie.
… … … … … … …

Mis-fielded data
Multiple Names

Typos
Mixed business and Missing Data
contact names

Commercial in Confidence 16


Customer Data Problems today

COMPLETENESS

CONFORMITY

CONSISTENCY

DUPLICATION

INTEGRITY

ACCURACY


Oracle Enterprise Data Quality Functionality in a Glance
Feature Functionality Examples Oracle Offering

Understand data status & Name: LN+FN (CHS, KOR,
Profiling/Pattern deduce meaning from JPN); FN+MN+ PN+LN
OEDQ Profiling Server
Detection unstructured patterns (Latin); Tel# is null 30%

Create structured records Address field -> Address
Parsing and from unstructured data Line 1, City, State,… OEDQ Parsing &
Standardization Spot and correct errors; Nationality: US, USA, Standardization Server
transform to std format American-> USA

Address Valid address 809 Newel rd, PALO ALTO
Validation / identification and 94301 -> 809 Newel Road, OEDQ Cleansing Server
Cleansing correction Palo Alto, CA 94303-3453

Matching and Spot / eliminate duplicates & Haidong Song = 宋海东
OEDQ Matching Server
Linking identify related entities =

Attach additional attributes Haidong Song: “single, Universal DQ Connector +
Enrichment and categorizations 1 child, Summit Estate, D&B connector + AIA 2.5 PIP
DoNot Mail” for Acxiom

* OEDQ is formerly known as Datanomics Data Quality Application

Data Governance Leadership


Data Governance ( DG )

DG is all about establishing the
strategies, objectives and policies
to effectively manage corporate
data by specifying accountability
on data and its related processes
including decision rights.
For example, DG defines
• Who owns the data;
• Who creates records;
• Who can update them; and
also,
• Who arbitrates decisions when
data management
disagreements arise.

People, processes and technologies are the building blocks for Data Governance


Data Governance Technology Requirements

Define, Communicate &
Easily Operate hub
Enforce
Define enterprise master data • Execute day-to-day hub operations
Define and view data policies (Consolidate, Cleanse, Share & Master)
Data accountability • Perform data steward tasks, such as
Escalation process merge/unmerge
Administer hub

Monitor hub operations Fix data issues

• Analyze hub DQ metrics • Fix import errors and resubmit
corrected data
• Track sources of bad data
• Proactively watch & repair data
• Monitor hub transaction load
• Tune data quality rules


Potential Data Governance Leadership Council
Leadership Layer
Client DG Leadership Council · Sponsorship, Oversight & Approval

Roles and Responsibilities
Data Governance Committee Executive Layer
· Approve Strategy Roadmap
· Align Business and IT Goals
Subject Area Business Owners IT Domain Owners · Align to Client Strategy
Customer/Contact, Booking, Services etc. Client IT Systems · Approve Project Prioritization
· Advocate Compliance

Management Layer
Development · Recommend Strategy and Goals
Lead / Business Data Managers IT Architect & Maintenance
Technical · Prioritize and Execute Projects
Manager
Manager · Define Standards and Policies
· Advocate Compliance
· Act as Subject Matter Experts (SMEs)

IT Data IT Application IT Integration
Process Stewards Data Stewards Personnel Personnel Personnel Operations/Execution Layer
· Sales Process · Source Steward · Stewardship of Data, Data SME
· MDM Specialist
· Service Process · End User Steward · DBA · Application Leads · DQM Specialist · IT/System/Database Administration (DBAs)
· Orders/Bookings · Data Hygiene · ETL Specialist · Technology Leads · DQ Tools
· Data Modeler · Project Delivery Specialist · Interface Daily with Customer Groups
· Cancellation Steward
· Ensure Compliance

Consumer Base

Business IT Enterprise Wide


DG Council Task Force
Leadership Council

• Champions of the DG Council provides the Leadership, Sponsorship and Overall Vision & Direction Serves as the Final
Authority on all decisions
• The council would typically consists of a Chief Sponsor ( MDM )and top leadership from Business & IT (for e.g. CIO, VP
Operations etc.)
Governance Committee
• Defines business strategies and champions the importance of data governance & data quality domain-specific data, processes, and business
rules throughout Client Organization
• Sets priorities for domain-specific data quality improvement projects
• Arbitrates competing interests and makes final decisions regarding issues the Management Layer is unable to resolve

Business Data Managers & IT Administrators
• Responsible for managing specific domain-data sets and is responsible for the data stewardship and quality of that data
• Recommend specific data projects to support better Data Governance and Data Quality efforts
• Responsible for assigning IT resources to support various data projects and initiatives
• Responsible for the upkeep of IT systems and tools to support better Data Management

Data Stewards Process Stewards
• Stewardship of the data for a particular domain (e.g. Customer) • Responsible for entering data for each business process (e.g.
• Perform data cleansing, and other data quality activities for that Sales , Marketing, Order Entry, Service Request etc.)
data domain • Aid better data quality by supporting data corrections and
• Ensure data standards and compliance communication
• Perform audits and security checks • Provide inputs to data collection process improvements for the
• Serve as a liaison between IT & business with regards to data specific process domain
• Serve as SME for specific data sets within the process domain

Commercial in © Copyright 2010 Hitachi Consulting 23

Data Governance Program Activities
Data Governance Activities
High-level Activities Detailed tasks

1. Establish Data
Define Data Governance Establish Establish Data Identify DG Council Formalize & Kick off Data Governance
Governance Leadership
Organization Framework Leadership Council Governance Committee Champions Leadership Organization internally
Organization

Define & Refine Leadership Nominate Data
Roles & Responsibilities Governance Lead

2. Establish Data
Establish Governance Refine Data Governance Charter after Define Data Governance Review & Refine Data
Governance Charter &
Charter & Vision socializing with the Leadership Goals & Objectives Governance Goals & Objectives
Vision

Define Data Governance Subject Area Owners & IT Domain Owners
Foundations & Framework Communicate Charter & Vision to their teams

3. Establish the Data
Identify Business Data Identify IT Management Define Data Governance Review & Refine Data Governance Define Standards,
Governance Framework
Managers for Customer Master Resources Framework Process Framework Processes Policies & Procedures
Processes

Establish Data Governance Define Stewardship
Compliance & Monitoring Framework Roles & Responsibilities

4. Operationalize Align standards with vision & Establish processes to manage Define/Refine additional policies
Standards & Policies strategy; Refine standards; and monitor standards & policies around audit & security

5. Establish the
Identify and Align Identify/Recruit Identify IT, Technical Define & Refine Stewardship Formalize the operational Data
Stewardship Processes
Process Stewards Data Stewards & Project Resources Processes including DQ Processes Governance Organization
& Organization

6. Formalize & Kick Off
Publish, Communicate and Kick Off Data Formalize & Kickoff Customer
Customer Master Data
Governance Organization across the Enterprise Data Governance Initiative
Governance Initiative


Process Definitions and Improvement Activities

Process Definitions & Improvement Activities

1. Establish Data Refer & Align with Data
Governance Processes Governance Roadmap

2. Refine Program/
Identify Current Program Refine/Redefine Program Identify Current Change
Project Management
management Framework Management Framework Management Framework
Processes

Identify project Management
Refine/Redefine Change Establish Change
processes in place and refine/
Management Framework Control Processes
adopt to MDM/DG projects

3. Refine Business
Inventory current Business Processes Identify process improvements
Processes to support
with touch point to customer data for each process
MDM/DG Processes

Refine/Redefine business process to Implement Identified
align better with future state MDM Changes


Metrics Definition & Monitoring Activities

Metrics Definitions & Monitoring Activities

1. Establish Governance Identify & Define Governance Operationalize Monitor & Report Governance
Metrics & Stewardship Metrics Governance Metrics & Stewardship Metrics

2. Establish Data Quality Identify & Define Data Quality Operationalize DQ Metrics for each system
Metrics Metrics for Customer Domain (Oracle CRM on Demand , BRM etc..)

Monitor & Report Governance
& Stewardship Metrics

3. Refine System SLAs Refine/Define System SLAs Operationalize System Monitor & Report System
and System Metrics and Metrics SLAs Metrics SLAs and Metrics


Data Governance – Key Takeaways

 Establish Data Governance Leadership Council

 Establish Data Governance procedures
 To ensure data standards and compliance around
 Data Consolidation
 Data Cleansing
 Data Governance
 Data Sharing
 Data Protection
 Data Analysis
 Data Decay

Commercial in Confidence © Copyright 2010 Hitachi Consulting

Some Examples of DG Council Action Items
 Addition of any global languages needs DGC approval

 Rules to curtail data decay need to be formalized .e.g.. All golden records that are
not updated for the last 6 months needs revisit from customer calls.
 Hierarchy Management of customers needs to be visited occasionally, as new
branches can be added to accounts.
 Exception management process (DQ Assistant)related functionality needs revision
and monitoring from DGC.
 Any updates for Transports and Connectors w.r.t. change, upgrade etc needs DGC
approval
 Any changes to Authorization and Registry services needs approval of DGC


Customer Hub
Data Stewardship Best Practices


Data Stewardship with OCH 8.2 v …


Data Stewardship with OCH 8.2 v
 Data Steward performs the following operations on a day to day basis
using the Data Stewardship application screens provided with OCH 8.2
o Suspect Match
o Merge Request
o Incoming Duplicate Overview
o Guided Merge & Unmerge
o Incomplete Records
o Survivorship Rules
o Data Decay Management

 The idea is to present the features available and supported by Oracle
Customer Hub 8.2 v

 This is only sample set of functionalities and you may choose to
explore other options and enhancements available with the product


Merge

UC Matching Threshold Scores M Merging
UCM calculates
Process
Matching UCM process the Record is updated
Record is sent back
Threshold score record based on based on
to boundary
Record is sent back to
based on the the Matching Survivorship Rules
system
boundary system
defined attributes Threshold

 There are 3 possible outcomes:

Threshold Type Threshold Score Description
Auto Threshold >= 90 UCM will automatically merge the two records
(Auto-merge) (except for Sales Records)
Manual Threshold <90 and =>70 UCM will flag the records to have a Data
Steward review and determine whether or not
to merge

Auto Threshold <70 UCM will create a new record and publish the
(Create New Record) record to the boundary systems


Merge Criteria used within UCM
UCM Merging Process
 Threshold Score:
 90% or above - the incoming record will merge with the existing record using the
survivorship rules*
 Less than 90% greater than 70% - the incoming record will be potentially merged depending
on the Data Steward’s decision

If the Matching Threshold score falls within this
range, the Survivorship Rules will apply
* Sales Records will
never be auto merged
Matching Threshold

Accounts Attributes Survivorship Rules

• Account Name >=90% • Recent – Incoming value will always
survive
• Main Phone
• History – Existing value will always
• Address <90% survive
• City
• Source – The value from the
• State >=70% source will survive., External
• Postal Code
Systems or Siebel.

<70%


Create and Merge Accounts
 Data Stewards needs to review the record within the “Incoming Duplicates” screen
when a Matching Threshold score is within the range of >= 70 and < 90

 Data Stewards will determine if the record needs to be merged with another record
or should be treated as a new record

Matching Threshold
Survivorship
Accounts Attributes Rules
Link and
>=90% Update
• Account Name
• Main Phone <90%
• Address Data Steward
• City >=70%
• State Create
• Postal Code New
<70% Create New
Record


Incoming Duplicate Process
Manual Link and Update Process Create and Merge Accounts

Data Steward
logs onto Data Steward Data Steward Data Steward
Record
Incoming queries for their reviews Yes selects “Link and
Matches?
Duplicates record incoming record Update”
Screen in UCM

No

UCM updates
Data Steward record using
selects “Create” Survivorship
Rules

UCM updates
record as a new End
record

 All Data Stewards will see the same records within the “Incoming Duplicates”
Screen


Link and Update a Record
 After reviewing the record information, the Data Steward can return to
the “Incoming Duplicates” Screen to “Link and Update” or “Create New”
 When a Data Steward selects “Link & Update”, UCM will update the
record based on the predefined survivorship rules

Link and Update


Create a New Record

 After reviewing the record information, the Data Steward can return to the
“Incoming Duplicates” Screen to “Link and Update” or “Create New”
 If the Data Steward selects “ Create New”, UCM will update the record as a new
record and no survivorship rules are applied

Create New


Guided Merge and Un Merge Process
UCM Existing Duplicates Create and Merge Accounts
 The “Existing Duplicates” screen is only used when records are loaded into UCM
using a batch process
 Only potential duplicates will be displayed in the “Existing Duplicates” screen
 Potential duplicates can be view “Duplicate Contacts” under Administration-
Data Quality and “Existing Duplicates” under Administration – Universal
Customer screen.

Potential Duplicate Records

Merge Button


Unmerging Records
Unmerging Records

 The Unmerge Profile Screen is where the account and contact
records can be unmerged: Records that were
merged within the
“existing Duplicate”
screen

Un Merge Button


Merge, Un Merge and Reject Records

Reject Button

Guided Merge Button
Merge Button


Guided Merge
Guided Merge allows end-user to review duplicate records and propose merge by
presenting three versions of the duplicate records and allows end user to decide how
the record in the UCM should look like after the merge task is approved and committed.
• Victim: the record that will be deleted (from master BC)
• Survivor: the record that will be (from master BC)
• Suggested: output from Surviving Engine (transient to the task)


Incomplete Records processing
Data Steward will analyze and re-process the Incomplete data through UCM Batch
process.


UCM Survivorship Rules
Survivorship Rules UCM Merging Process
UCM calculates
Matching UCM process the Record is
Record is sent
Threshold score record based on updated based
back to
based on the the Matching on Survivorship
boundary system
defined Threshold Rules
attributes

 Survivorship Rules are used to automate the quality of the master customer data.
 Once a record is determined to be merged, UCM will compare each attribute
within a record and update the record accordingly
 Data Steward will change the Survivorship rule weight age depends on source
system’s and surviving field in Master record level.
 There are three comparison methods used by Survivorship rules:
• Recent – Incoming value will always survive
• History – Existing value will always survive
• Source – The value from the source will survive a.k.a., External Systems or Siebel.

Remember that whether a record is auto merged by UCM or manually selected to be
merged, the survivorship rules will apply.

43

Survivorship Rule Example - Source
New incoming record from Siebel (primary source) Existing Record within UCM ( from Siebel )

Name Verizon Name Verizon
Phone Number 4085467880 Phone Number 5105467880
Fax Number 4086548980 Fax Number 4086548980
Street Address 5649 Tasman Drive Street Address 5649 Tasman Drive
City San Jose City San Jose
State CA State CA
Postal Code 93425 Postal Code 93425
Country USA Country USA

Best version UCM record

Name Verizon
Phone Number 4085467880
Fax Number 4086548980

Street Address 5649 Tasman Drive

City San Jose
State CA
Postal Code 93425
Country USA


UCM Survivorship Rule set View


Enhanced Data Stewardship Capabilities


For any Questions & Clarifications

Twitter : din2win
Email : dinwin@hotmail.com
Dinesh.Chandrasekar@Hitachiconsulting.com


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