Develop a Master Data Management Practice and Platform
Are you sure you have a master data problem?
- The volume of enterprise data is growing rapidly and comes from a wide variety of internal and external data sources (e.g. ERP, CRM). When data is located in different systems and applications, coupled with degradation and proliferation, this can lead to inaccurate, inconsistent, and redundant data being shared across departments within an organization.
- Data kept in separate soiled sources can result in poor stakeholder decision making and inefficient business processes. Some common master data problems include:
- The lack of a clean customer list results in poor customer service.
- Hindering good analytics and business predictions, such as incorrect supply chain decisions when having duplicate product and vendor data between plants.
- Creating cross-group consolidated reports from inconsistent local data that require too much manual effort and resources.
Our Advice
Critical Insight
- Everybody has master data (e.g. customer, product) but not master data problems (e.g. duplicate customers and products). MDM is complex in practice and requires investments in data governance, data architecture, and data strategy. Identifying business outcomes based on quality master data is essential before you pull the trigger on an MDM solution.
Impact and Result
This blueprint can help you:
- Build a list of business-aligned data initiatives and capabilities that address master data problem and realize business strategic objectives.
- Design a master data management practice based on the required business and data process.
- Design a master data management platform based on MDM implementation style and prioritized technical capabilities.
Member Testimonials
After each Info-Tech experience, we ask our members to quantify the real-time savings, monetary impact, and project improvements our research helped them achieve. See our top member experiences for this blueprint and what our clients have to say.
9.3/10
Overall Impact
$25,656
Average $ Saved
14
Average Days Saved
Client
Experience
Impact
$ Saved
Days Saved
Integra LifeSciences Corporation
Guided Implementation
10/10
$12,999
5
Nozom Alkhebrat IT Co.
Guided Implementation
9/10
$4,000
5
Loto-Québec
Guided Implementation
10/10
$25,000
20
Alm Media, LLC
Guided Implementation
9/10
$62,999
10
Washington Technology Solutions
Guided Implementation
9/10
N/A
N/A
Great contextual and conceptual models provided, planning to immediately leverage.
Peoples Bank
Workshop
10/10
$23,939
35
Understanding data governance terminology will have huge impact moving forward for our team and our management team. Reddy's background in the fina... Read More
Central Bank of Barbados
Guided Implementation
10/10
N/A
N/A
That session was well pitched and helped to set the tone for possible next steps as it relates to master data and master data management (MDM). Thi... Read More
Rogers Communications Canada Inc.
Guided Implementation
10/10
N/A
N/A
Anu was really helpful. She listened to the requirements and what is needed from her. She helped to review the deck I had put together for senior l... Read More
New Mexico Department Of Health
Guided Implementation
9/10
N/A
N/A
Very informative, answered our questions and the templates displayed were very helpful. Very knowledgeable and provided a lot of information in th... Read More
Owens Corning
Guided Implementation
6/10
N/A
N/A
Best part of experience - open dialog regarding best practices and insights to thinking "out of the box". worst part of experience - would have l... Read More
Fernco Inc
Workshop
10/10
N/A
N/A
Worse: Fernco is in the process of developing a Data Quality culture so to be able estimate time and dollar savings from MDM workshop is difficult ... Read More
Circular Materials
Workshop
10/10
$25,000
10
Appreciated the hands-on modelling work with Reddy and Dirk. We will be walking away from the workshop with a good foundation of models that captu... Read More
ITsPeople
Guided Implementation
9/10
$23,559
12
Great Clips Inc.
Guided Implementation
9/10
N/A
N/A
The team we talked with with knowledgeable, and they delivered what they promised.
Colliers International Canada
Guided Implementation
10/10
N/A
N/A
Very knowledgeable consultant, good interaction. We are just starting our journey; it is too early to assess the time or dollar savings.
Fusion Superplex
Guided Implementation
8/10
$25,000
50
Werner Co.
Guided Implementation
8/10
$62,999
10
I love the thought leadership. I wish master data was easy.
Northern Ontario School of Medicine
Guided Implementation
10/10
N/A
120
Everyone went smoothly, everything was great. Not 'worst' parts to report thus far.
Cengage Learning
Guided Implementation
8/10
N/A
50
Fisheries and Oceans Canada
Guided Implementation
10/10
$25,000
50
Igor was very knowledgeable, delivered relevant materials synthesized from the Info-Tech website resources, and has offered access to other relevan... Read More
Blommer Chocolate Company
Guided Implementation
8/10
$12,742
10
Provided practical thoughts on how to get started.
Amedisys Holding, LLC
Guided Implementation
1/10
N/A
N/A
Both teams were both completely unprepared for the call.
CF Industries Enterprises Inc
Guided Implementation
8/10
$2,419
2
Dollar General
Guided Implementation
10/10
N/A
N/A
AAA Club Alliance, Inc
Guided Implementation
9/10
N/A
N/A
Igor is very knowledgeable about this area and the challenges companies face in moving forward implementing MDM. Lots of good ideas!
North Country HealthCare
Guided Implementation
10/10
$31,833
120
Shearman & Sterling LLP
Workshop
9/10
N/A
N/A
We have spent a lot of time figuring out the agenda and finalizing the SOW, and logistic. We also had missed on our first meeting with the busines... Read More
Athens International Airport
Guided Implementation
10/10
N/A
N/A
Clark Builders
Guided Implementation
7/10
N/A
5
Unified Health Services
Guided Implementation
10/10
N/A
20
All great. Very helpful. This info will help us jumpstart our data architecture efforts.
Workshop: Develop a Master Data Management Practice and Platform
Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
Module 1: Develop a Vision for the MDM Project
The Purpose
- Identification of MDM and why it is important.
- Differentiate between reference data and master data.
- Discuss and understand the key challenges and pains felt by the business and IT with respect to master data, and identify the opportunities MDM can provide to the business.
Key Benefits Achieved
- Identification of what is and is not master data.
- Understand the value of MDM and how it can help the organization better monetize its data.
- Knowledge of how master data can benefit both IT and the business.
Activities
Outputs
Establish business context for master data management.
- High-level data requirements
Assess the value, benefits, challenges, and opportunities associated with MDM.
- Identification of business priorities
Develop the vision, purpose, and scope of master data management for the business.
- Project vision and scope
Identify MDM enablers.
Interview business stakeholders.
Module 2: Document the Current State
The Purpose
- Recognize business drivers for MDM.
- Determine where master data lives and how this data moves within the organization.
Key Benefits Achieved
- Streamline business process, map the movement of data, and achieve a common understanding across the company.
- Identify the source of master data and what other systems will contribute to the MDM system.
Activities
Outputs
Evaluate the risks and value of critical data.
Map and understand the flow of data within the business.
- Data flow diagram with identified master data sources and users
Identify master data sources and users.
- Business data glossary
Document the current architectural state of the organization.
- Documented current data state.
Module 3: Document the Target State
The Purpose
- Document the target data state of the organization surrounding MDM.
- Identify key initiatives and metrics.
Key Benefits Achieved
- Recognition of four MDM implementation styles.
- Identification of key initiatives and success metrics.
Activities
Outputs
Document the target architectural state of the organization.
- Documented target state surrounding MDM.
Develop alignment of initiatives to strategies.
- Data and master data management alignment and strategies
Consolidate master data management initiatives and strategies.
Develop a project timeline and define key success measures.
Module 4: Develop an MDM Practice and Platform
The Purpose
- Get a clear picture of what the organization wants to get out of MDM.
- Identify master data management capabilities, accountabilities, process, roles, and governance.
Key Benefits Achieved
- Prioritized master data management capabilities, accountabilities, process, roles, and governance.
Activities
Outputs
Identify master data management capabilities, roles, process, and governance.
Build a master data management practice and platform.
- Master Data Management Practice and Platform
Develop a Master Data Management Practice and Platform
Are you sure you have a master data problem?
Analyst Perspective
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The most crucial and shared data assets inside the firm must serve as the foundation for the data maturing process. This is commonly linked to your master data (such as customers, products, employees, and locations). Every organization has master data, but not every organization has a master data problem. Don't waste time or resources before determining the source of your master data problem. Master data issues are rooted in the business practices of your organization (such as mergers and acquisitions and federated multi-geographic operations). To address this issue, you will require a master data management (MDM) solution and the necessary architecture, governance, and support from very senior champions to ensure the long-term success of your MDM initiative. Approaching MDM with a clear blueprint that provides a step-by-step approach will aid in the development of your MDM practice and platform. |
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Ruyi Sun |
Rajesh Parab |
Executive Summary
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Your Challenge |
Common Obstacles |
Info-Tech’s Approach |
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Your organization is experiencing data challenges, including:
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MDM is useful in situations such as a business undergoing a merger or acquisition, where a unique set of master data needs to be created to act as a single source of truth. However, having a unified view of the definitions and systems of record for the most critical data in your organization can be difficult to achieve. An organization might experience some pain points:
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Info-Tech Insight
Everybody has master data (e.g. customer, product) but not a master data problem (e.g. duplicate customers and products). MDM is complex in practice and requires investments in data governance, data architecture, and data strategy. Identifying business outcomes based on quality master data is essential before you pull the trigger on an MDM solution.
What is master data and master data management?
- Master data domains include the most important data assets of an organization. For this data to be used across an enterprise in consistent and value-added ways, the data must be properly managed. Some common master data entities include customer, product, and employees.
- Master data management (MDM) is the control over master data values to enable consistent, shared, contextual use across systems, of the most accurate, timely, and relevant version of truth about essential business entities (DAMA DMBOK).
- The fundamental objective of MDM is to enable the business to see one view of critical data elements across the organization.
- MDM systems will detect and declare relationships between data, resolve duplicate records, and make data available to the people, processes, and applications that need it. The end goal of an MDM implementation is to make sure your investment in MDM technology delivers the promised business results. By supplementing the technology with rules, guidelines, and standards around enterprise data you will ensure data continues to be synchronized across data sources on an ongoing basis.
Info-Tech’s Data Management Framework Adapted from DAMA-DMBOK and Advanced Knowledge Innovations Global Solutions. See Create a Data Management Roadmap blueprint for more information.
Why manage master data?
Master data drives practical insights that arise from key aspects of the business.
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Customer Intimacy |
Innovation Leadership |
Risk Management |
Operational Excellence |
|---|---|---|---|
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Improve marketing and the customer experience by using the right data from the system of record to analyze complete customer views of transactions, sentiments, and interactions. |
Gain insights on your products, services, usage trends, industry directions, and competitor results, and use these data artifacts to support decisions on innovations, new products, services, and pricing. |
Maintain more transparent and accurate records and ensure that appropriate rules are followed to support audit, compliance, regulatory, and legal requirements. Monitor data usage to avoid fraud. |
Make sure the right solution is delivered rapidly and consistently to the right parties for the right price and cost structure. Automate processes by using the right data to drive process improvements. |
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85% of customers expect consistent interactions across departments (Salesforce, 2022). |
Top-decile economic performers are 20% more likely to have a common source of data that serves as the single source of truth across the organization compared to their peers (McKinsey & Company, 2021). |
Only 6% of board members believe they are effective in managing risk (McKinsey & Company, 2018). |
32% of sales and marketing teams consider data inconsistency across platforms as their biggest challenge (Dun & Bradstreet, 2022). |
Your Challenge
Modern organizations have unprecedented data challenges.
- The volume of enterprise data is growing rapidly and comes from a wide variety of internal and external data sources (e.g. ERP, CRM). When data is located in different systems and applications, coupled with degradation and proliferation, this can lead to inaccurate, inconsistent, and redundant data being shared across departments within an organization.
- For example, customer information may not be identical in the customer service system, shipping system, and marketing management platform because of manual errors or different name usage (e.g. GE or General Electric) when input by different business units.
- Data kept in separate soiled sources can also result in poor stakeholder decision making and inefficient business processes. Some issues include:
- The lack of clean customer list results in poor customer service.
- Hindering good analytics and business predictions, such as incorrect supply chain decision when having duplicate product and vendor data between plants.
- Creating cross-group consolidated reports from duplicate and inconsistent local data requires too much manual effort and resources.
On average, 25 different data sources are used for generating customer insights and engagement.
On average, 16 different technology applications are used to leverage customer data.
Source: Deloitte Digital, 2020
Common Obstacles
Finding a single source of truth throughout the organization can be difficult.
Changes in business process often come with challenges for CIOs and IT leaders. From an IT perspective, there are several common business operating models that can result in multiple sets of master data being created and held in various locations. Some examples could be:
- Integrate systems following corporate mergers and acquisitions
- Enterprise with multi-product line
- Multinational company or multi-geographic operations with various ERP systems
- Digital transformation projects such as omnichannel
In such situations, implementing an MDM solution helps achieve harmonization and synchronization of master data and provide a single, reliable, and precise view of the organization. However, MDM is a complex system that requires more than just a technical solution. An organization might experience the following pain points:
- Failure to identify master data problem and organization’s data needs.
- Conflicting viewpoints and definitions of data assets that should reside in MDM across business units.
Building a successful MDM initiative can be a large undertaking that takes some preparation before starting. Understanding the fundamental roles that data governance, data architecture, and data strategy play in MDM is essential before the implementation.
“Only 3 in 10 of respondents are completely confident in their company's ability to deliver a consistent omnichannel experience.”
Source: Dun & Bradstreet, 2022
Insight summary
Overarching insight
Everybody has master data (e.g. customer, product) but not a master data problem (e.g. duplicate customers and products). MDM is complex in practice and requires investments in data governance, data architecture, and data strategy. Figuring out what the organization needs out of its master data is essential before you pull the trigger on an MDM solution.
Phase 1 insight
A master data management solution will assist you in solving master data challenges if your organization is large or complex, such as a multinational corporation or a company with multiple product lines, with frequent mergers and acquisitions, or adopting a digital transformation strategy such as omnichannel.
Organizations often have trouble getting started because of the difficulty of agreeing on the definition of master data within the enterprise. Reference data is an easy place to find that common ground.
While the organization may have data that fits into more than one master data domain, it does not necessarily need to be mastered. Determine what master data entities your organization needs.
Although it is easy to get distracted by the technical aspects of the MDM project – such as extraction and consolidation rules – the true goal of MDM is to make sure that the consumers of master data (such as business units, sales) have access to consistent, relevant, and trusted shared data.
Phase 2 insight
An organization with activities such as mergers and acquisitions or multi-ERP systems poses a significant master data challenge. Prioritize your master data practice based on your organization’s ability to locate and maintain a single source of master data.
Leverage modern capabilities such as artificial intelligence or machine learning to support large and complex MDM deployments.
Blueprint Overview
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1. Build a Vision for MDM |
2. Build an MDM Practice and Platform |
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|---|---|---|
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Phase Steps |
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Phase Participants |
CIO, CDO, or IT Executive Head of the Information Management Practice Business Domain Representatives |
Enterprise Architecture Domain Architects Information Management MDM Experts Data Stewards or Data Owners |
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Phase Outcomes |
This step identifies the essential concepts around MDM, including its definitions, your readiness, and prioritized master data domains. This will ensure the MDM initiatives are aligned to business goals and objectives. |
To begin addressing the MDM project, you must understand your current and target data state in terms of data architecture and data governance surrounding your MDM strategy. With all these considerations in mind, design your organizational MDM practice and platform. |
About Info-Tech
Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.
We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.
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A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.
Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.
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Guided Implementation 1: Build a vision for MDM
- Call 1: Identify what master data management means to the organization.
- Call 2: Discuss the strategic plans of the business and establish a business context for master data management.
- Call 3: Determine master data management strategies.
Guided Implementation 2: Create a plan and roadmap for the organization’s MDM program
- Call 1: Discuss the results of the master data management capabilities evaluation.
- Call 2: Plan initiatives for master data management.
- Call 3: Create a roadmap and discuss next steps.
Contributors
Info-Tech Contributors:
- Reddy Doddipalli
- Igor Ikonnikov
- Irina Sedenko
- Anu Ganesh
- Wayne Cain
- Reddy Doddipalli
- Imad Jawadi
- Andy Neill
- Steve Wills
External Contributors:
- Selwyn Samuel, Director of Enterprise Architecture, Furniture manufacturer
- Julie Hunt, Consultant and Author, Hub Designs Magazine and Julie Hunt Consulting
- David Loshin, President, Knowledge Integrity Inc.
- Three anonymous contributors
Build a Robust and Comprehensive Data Strategy
Create a Data Management Roadmap
Establish Data Governance
Build a Data Architecture Roadmap
Sprint Toward Data-Driven Culture Using DataOps
Develop a Master Data Management Practice and Platform
Build a Data Integration Strategy
Build a Data Pipeline for Reporting and Analytics
Build an Extensible Data Warehouse Foundation
Build a Reporting and Analytics Strategy
Modernize Data Architecture for Measurable Business Results
Create a Customized Big Data Architecture and Implementation Plan
Break Open Your DAM With Intuitive Metadata
Perform a Data Audit
Create an Architecture for AI
Launch Your AI Proof of Value
