Develop a Master Data Management Strategy and Roadmap

Make sure your most important data is accurate and accessible across your business units to ensure optimal decision support and to monetize your data assets.

Analyst Perspective

"Organizations shy away from MDM because they don’t know where to start, fear the expense and disruption, and don’t truly know the benefits for the effort involved.

MDM is valuable for all sizes of organizations; bigger ones will use the expensive tools but smaller ones can still benefit from MDM using simple tools and processes.

Use an approach to drive real and practical MDM into your organization by starting with reference data; doing this right and first makes the rest of MDM go so much smoother because it is easy, non-contentious, and fast and builds momentum based on success. A strong MDM foundation enables better use of key data assets to drive greater business results and data monetization." (Steven Wilson, Senior Director, Research & Advisory Services, Info-Tech Research Group)

Our understanding of the problem

Executive summary

Situation

• 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.
• For a consistent view of master data domains the data should be controlled by Master Data Management (MDM).

Complication

• Simple in concept, MDM is complex in practice and requires investments in governance, technology, and planning.
• Master data is pervasive throughout the business and is often created and captured in highly disparate sources that often are not easily shared across business units and applications.

Resolution

• Ready? Make sure that your practice has the necessary pre-requisites for MDM. Don’t waste time and resources before you have identified your first group of data to begin your MDM solution, and the necessary architecture, governance, and support from very senior champions to ensure the ongoing success of your MDM initiative.
• Aim. Figuring out what the organization needs out of its master data is essential before you pull the trigger on a MDM strategy; alignment to strategic business needs will ensure buy-in and support through project disruptions to the business.
• Fire! Approaching MDM with a clear-cut blueprint that provides you with a solid plan and organized direction will help you stay adaptable when unexpected nuances of organization requirements or road blocks crop up.

Info-Tech Insight

MDM is only for the “big guys.” Not so! Big MDM systems are complex and require deep commitment to deploy and run, but MDM for SMEs to monetize data is still a critical requirement to achieve success.

MDM can be difficult and expensive. It can be, so start small. Use the Info-Tech tools to help identify quick-win opportunities for establishing MDM in your organization then grow it organically and as value is realized.

Good MDM requires good reference data. Get your reference data in order with MDM processes and ensure the MDM data is usable in your BI and analytics platforms.

To differentiate and remain competitive in today's marketplace organizations are becoming more data-driven

Analytic Competitor

Leveraging data as a competitive advantage makes data a central enabler to an organization’s operations and strategy.

Master data is the lifeblood data of the organization – those who manage their master data effectively become industry leaders through better strategic planning.

Data and the use of data analytics has become a centerpiece to effective modern business. Top-performing organizations across a variety of industries have been cited to use analytics five times more than lower performers. (Source: MIT Sloan Management Review)

Master data management is a critical dimension of data management

Fundamental Objective of Master Data Management

What is Included in the Scope of MDM?

• Party Data (Employees, Customers, etc.)
• Product/Service Data
• Financial Data
• Location Data

Successful MDM Requires a Comprehensive Approach

For master data management to be successfully planned, implemented, and maintained it must include effective capabilities in the critical processes and sub-practices of data management (see Layer 1: Data Management Enablers).

For more information on Info-Tech’s Data Management, see the description of its approach and layers in the appendix.

Master data management provides opportunities to use data for analytical and operational purposes with greater accuracy

MDM provides a single consolidated and accurate view of an organization's most valuable data assets.

Sales data for customer A = Marketing data of customer A = Analytical data including customer A

An investment in MDM will improve the opportunities for using the organization’s most valuable data assets.

• Data is more easily shared across the organization’s environment with greater accuracy and trust.
• Multiple instances of the same data are consistent.
• MDM enables the ability to find the right data more quickly.

Think of it in the context of your own organization.

• How will MDM improve the ability for accurate data to be shared across business processes (Operational MDM)?
• How will MDM improve the quality of reports for management reporting and executive decision making (Analytical MDM)?

It is important to keep customer information up to date... 30% of customers will never buy from a company again if they encounter even one shipping error. (Material Handling and Logistics Magazine)

Master data drives practical insights that arise from key aspects of the business

A Single Source of Truth

Why Manage Master Data?

1. Customer Intimacy
   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
2. Innovation Leadership
   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.
3. Risk Management
   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.
4. Operational Excellence
   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.

Improved master data management can drive better customer relations

What is a business without the customer? Customer retention, satisfaction, and how much value an organization creates from each customer drives revenue growth and ultimately determines the success of the organization. Effective MDM can improve customer relations, stickiness, wallet/mind share, and customer retention. Consider the following:

In a survey of 428 enterprises, the best-in-class (BIC) performers in their ability to have a complete customer data view, performed significantly better in customer value, satisfaction, and retention than those who did not – the industry laggards.

86% of consumers will pay for a better customer experience. (Oracle Customer Experience Impact Report, 2011)

Modern organizations have unprecedented data challenges

Why is there an increasing need for dedicated investments in MDM within an organization's data management practice?

1. Too much data volume, variety, and velocity from more and more sources.
2. Disorganized and disparate data across multiple systems and applications*
3. Conflicting viewpoints and definitions of data assets that should reside in MDM.

*25% of organizations have 15 or more data repositories. (The Information Difference MDM Landscape, 2014)

The Solution

The critical, underlying master data that feeds the applications and processes driving an organization, as well as the reference data that regulates how different data silos relate to each other, must be managed effectively to gain clear and business-relevant insights.

Finding a single source of truth throughout the organization can be difficult

The Challenge

Data is created by many different sources, in many different locations, and changed often. Having a unified view of the definitions and systems of record for the most critical data in your organization can be difficult to achieve.

15 - Median number of systems per organization generating master data in 2008 – this number did not improve through 2014 (The Information Difference, 2014).

80 - Number of organizations that have two or more data repositories (IBM, 2006).

Fast Fact: There are 128,000 change of address requests made every day in the US, according to the US Postal Service.

You can't make sense of big data without master data

Although MDM is used for managing structured data from more traditional sources, it also plays an essential role in ensuring new data sources (semi-structured & unstructured big data) are also managed.

Big data is a top priority for modern organizations…

But with its enormous volume of unstructured data, big data can’t be leveraged without having a dependable reference.

Master data is that reference.

Since 2013, Info-Tech has surveyed over 20,000 business stakeholders as part of our CIO Business Vision program.

We asked them to rank the importance of the following 12 core IT services…

Top Upcoming Technology Innovations for CEOs

1. Mobile for Employees
2. Big Data - Analytics
3. Internal Collaboration Tools
4. Mobile for Customers
5. Social Media for Engagement
6. Big Data - Collection
7. Cloud for Application Functionality
8. Cloud for Agile Infrastructure
9. Social Media for Acquisition
10. Internet of Things for Product Innovation

Master Data Focus: Party master data in the Energy Industry

Customer Master Data

Energy utility companies have unprecedented challenges in the modern world. Customers have increasingly high expectations of power reliability while also becoming more concerned about carbon emissions. In addition, government regulations are increasing the need for transparency in the industry. To meet these challenges, energy companies need a holistic understanding of customer master data in real time. The result is a Smart Grid system that improves service and reduces costs using the singular, clear, and actionable view of energy customers provided by customer master data management.

$3.4B - Funding from the U.S. government for Smart Grid projects

1. Optimal customer relations
2. Timely outage notifications
3. Strategic energy usage advice
4. Reduce costs and waste from excess production

Master Data Focus: Product master data in the Automotive Industry

Suppliers and Materials Master Data

During the auto industry crisis of 2008-2010, the industry experienced major overhauls due to poor product planning and a lack of understanding of what products its customers wanted. To improve efficiency and gain customer trust, it needed to drastically improve the management of its product data. Successful MDM was implemented by a large automobile company by effectively communicating with its data stewards, having a comprehensive change management program, and establishing clear internal data rules that complied with increasingly stringent external guidelines. (PWC, 2011)

1. Reduce product delays to customer
2. Synchronize processes
3. Improved product development

Master Data Focus: Financial master data in the Banking Industry

Transactional Master Data

An investment bank implemented MDM to improve its transactional master data and the efficiency of its operations.

50% - Reduction in straight-through-processing failure rate.

$2M - Annual savings from decreased need for manual error fixing of failed stock trades.

$50M - Reduction in yearly costs due to increased liquidity from fewer "stuck" trades.

How? By keeping the business benefits of MDM front and center and starting with realistic master data domain goals, implementing a MDM solution succeeded in providing a unified and dependable view of stock transactions and the ability to clearly see interactions within the context of other data domains. (Informatica, 2010)

Master data management is not just another technology project

"Too often there is a disconnect between what the IT team considers 'required' or 'sufficient' in an MDM project vs. the rapid value business groups want to and can achieve from integrated customer information." (Mehmet Orun, Data Strategy Leader, Salesforce)

Master data management needs to be driven by business goals and objectives.

For master data to be a strategic asset of the business, the business and IT processes that support its use, access, delivery, and management must work together in alignment.

Master Data Management Cycle

Master data management is possible

Info-Tech Research Group’s approach to implementing master data management will align your goals with the drivers of the organization, taking off the veil of MDM and ensuring success.

The first MDM hub solutions entered the market in 2004. Over time, the maturity of the solutions and the ability to complete a successful implementation has grown. MDM implementation success is rapidly improving with maturity. There is more cross-talk between processes and technology than ever before.

In 2011, 82% of MDM initiatives were rated as successful, compared with 25% in 2008. (The Information Difference)

"For a long time, master data management had a bad reputation because it took a long time to implement. I think we have now gone beyond that with new approaches." (Julies Hunt, Software Industry Analyst, Hub Designs Magazine)

This blueprint's two-phase structure helps clients build a MDM practice for a business-aligned single version of the truth

Info-Tech delivers

Use the step-by-step advice within this blueprint to adopt an iterative approach to creating information strategies for the organization and building a plan for creating a well-aligned master data management practice. Following the phases outlined in the blueprint will support your organization in developing master data strategies and building a roadmap that will enable end users to:

• Have access to quality master data.
• Have master data to support the analytical and operational use for each relevant business area.
• Use master data that is properly secured.

BLUEPRINT RESOURCES

• MDM Readiness Assessment
• Business Needs Assessment
• MDM Capabilities Assessment and Initiative Planning Tool
• Strategic MDM Roadmap
*This blueprint contains additional resources that support the creation of interim deliverables and the execution of project steps.

Workshop overview

Contact your account representative or email Workshops@InfoTech.com for more information.

Navigate the two phases of the blueprint using this table of contents

Phase 1: Build a Vision for MDM

• Step 1: Assess Your Organizational Readiness for MDM
  Tool: MDM Readiness Assessment
• Step 2: Determine Business Requirements for MDM
  Tool: MDM Business Needs Assessment
• Step 3: Make Key Decisions for Your MDM Program
  Template: MDM Business Case Presentation Template
  Template: Project Charter Template

Phase 2: Create a Plan and Roadmap for the Organization's MDM Program

• Step 1: Perform a MDM Capabilities Assessment
  Tool: MDM Capabilities Assessment and Initiative Planning Tool
• Step 2: Develop MDM Initiatives
  Tool: MDM Capabilities Assessment and Initiative Planning Tool
• Step 3: Create a Roadmap and Plan of Action for Master Data Management
  Template: MDM Roadmap Template
Appendix
Info-Tech Research Group's Information Management
Framework
Additional Research
Bibliography

Use these icons to help direct you as you navigate this research

Use these icons to help guide you through each step of the blueprint and direct you to content related to the recommended activities.

This icon denotes a slide where a supporting Info-Tech tool or template will help you perform the activity or step associated with the slide. Refer to the supporting tool or template to get the best results and proceed to the next step of the project.

This icon denotes a slide with an associated activity. The activity can be performed either as part of your project or with the support of Info-Tech team members, who will come onsite to facilitate a workshop for your organization.

Develop a Master Data Management Strategy and Roadmap - Project Overview

	Phase 1 Build a Vision for MDM	Phase 2 Create a Plan and Roadmap for the Organization's MDM Program
Best-Practice Toolkit	Executive Brief 1.1 Assess Your Organizational Readiness for MDM MDM Readiness Assessment 1.2 Determine Business Needs for MDM MDM Business Needs Assessment 1.3 Make Key Decisions for Your MDM Program Business Case Presentation Template Project Charter Template	2.1 Perform a MDM Capabilities Assessment MDM Capabilities Assessment and Initiative Planning Tool 2.2 Develop MDM Initiatives MDM Capabilities Assessment and Initiative Planning Tool 2.3 Create a Roadmap and Plan of Action for Master Data Management MDM Roadmap Template Appendix
Guided Implementations	Identify what master data management means to the organization. Discuss the strategic plans of the business and establish a business context for master data management. Determine master data management strategies.	Discuss the results of the master data management capabilities evaluation. Plan initiatives for master data management. Create a roadmap and discuss next steps.

Info-Tech offers various levels of support to best suit your needs

Diagnostics and consistent frameworks used throughout all four options

Develop a Master Data Management Strategy and Roadmap

PHASE 1

Build a Vision for MDM

Phase 1 Overview

Detailed Overview

• Step 1: Assess Your Organizational Readiness for MDM
• Step 2: Determine Business Needs for MDM
• Step 3: Create a Strategic Vision for Your MDM Program

Outcomes

• Determine your organization’s needs and readiness for a MDM strategy, and gain long-term business support by presenting a convincing case for MDM.
• Concretely understand why your organization needs MDM and the scope of the project that will bring the most business benefit.

Benefits

• Take the veil off MDM by having a better understanding of how your organization will benefit most from a MDM practice and develop the precise strategy to get you there.

Phase 1 outline

Complete these steps on your own, or call us to complete a guided implementation. A guided implementation is a series of 2-3 advisory calls that help you execute each phase of a project. They are included in most advisory memberships.

Guided Implementation 1: Build a Vision for MDM

Proposed time to Completion: 2-4 weeks

Step 1

Assess Your Organizational Readiness for MDM

You are here

Step 1: Assess Your Organizational Readiness for MDM

This step will walk you through the following activities:

• 1.1.1 - Build a solid foundation of knowledge surrounding master data, reference data and MDM.
• 1.1.2 - Determine where your organization is lacking MDM prerequisites in the areas of people, processes, technology, and data.
• 1.1.3 - If not yet ready, develop a plan for becoming ready for MDM, or determine alternatives that will bring benefits to the business.

This step involves the following participants:

• Data Stewards / Data Custodians
• Head of Information Management
• Information Management Team

Outcomes of this step

• An understanding of master data, MDM, and the prerequisites necessary to embark on a MDM program.
• Determine if there a need for MDM in the organization.
• If MDM is not yet what the organization needs, determine a plan that will help you get to a ready state.

Master Data Management (MDM) is essential to any organization where data is shared across systems

Master Data

Addresses critical business entities that fall into four broad groupings: party (customers, suppliers); product (products, policies); location (e.g. physical spaces and segmentations); and financial (contracts, transactions).

This data is typically critical to the organization, less volatile, more complex in nature, contains many data elements, and is used across systems.

Why do you need master data?

While data sets are often used for different purposes, the same types of data are often shared across the organization (in different departments and across systems). Master data is the accurate set of that data that is used across the organization and in multiple systems.

Master Data Management

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.

What is the goal of MDM?

The end goal of a 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

Implement a management strategy using reference data first to make tackling master data easier

Many organizations struggle to get started with master data because they have not defined the reference data that supports it. Don’t make the same mistake.

"You need to know the difference between reference data and master data. Focus on the reference data first. Generally, you need to have references in line before you can connect it to your master data." (Ken Bergman, Architect)

Reference Data

Simple lists of data that are typically static and help categorize other data using code tables.

Examples: lists of countries, states, postal codes, general ledger charts of accounts, currencies, or product codes.

Reference data serves as a great starting place for a MDM project

Loading information into the warehouse or a MDM hub usually requires reconciling reference data from multiple sources. By getting reference data in order first, MDM will be easier to implement.

Reference data also requires a relatively small investment with good returns so the value of the project can easily be demonstrated to stakeholders.

An example of how reference data makes master data easier to tackle is:

• The master list of an organization’s customers needs an attribute of an address. By maintaining a list of postal codes or cities as reference data, this is made much easier to manage than simply allowing free text.

Info-Tech Insight

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.

Recognize the prerequisites for MDM before diving into more specific readiness requirements

1. Organizational Buy-in
   
   
   • Ensure there is someone actively invested and involved in the progress of the project. Having senior management support, especially in the form of an executive sponsor or champion, is necessary to approve MDM budgets and resourcing.
   • MDM changes business processes and practices that affect many departments, groups, and people – this type of change may be disruptive so sponsorship from the top ensures your project will keep moving forward even during difficulties.
   • Consider developing a cross-functional master data team, involving stakeholders from management, IT, and the business units. This group can ensure that the MDM initiative is aligned with and supports larger organizational needs, and everyone understands their role.
2. Understanding the existing data environment
   
   
   • Knowing the state of an organization’s data architecture, and which data sources are linked to critical business processes, is essential before starting a MDM project.
   • Identify the areas of data pain within your organization and establish the root cause. Determine what impact this is having on the business.

To embark on a successful MDM strategy, the organization’s data environment– its people, processes, technology, and data – needs to be ready

"You can implement data governance without MDM, but you can’t implement MDM without data governance." (Steve Putman, Data Management Consultant, SAS)

Info-Tech's MDM Readiness Assessment will help measure the organization's data culture and its preparedness for MDM

Master Data Management Readiness Assessment

Building a successful MDM initiative can be a large undertaking that takes some preparation before starting. Make sure you are prepared by determining the present state of your organization’s people, processes, technology, and data.

Use the Master Data Management Readiness Assessment to assess the current state of the organization’s culture and readiness for MDM.

Don’t waste precious IT time and resources if the organization is not prepared for MDM.

"You can have a successful implementation that never does the business any good…MDM has fallen victim to that." (Julie Hunt, Software Industry Analyst, Hub Designs Magazine)

Use the results of the MDM Readiness Assessment to determine if the project has a red light or a green light

The MDM Readiness Assessment will help you make the decision to stop the MDM project now and put more effort in getting ready for the project, or to continue on the path to MDM.

Instructions

1. Use the findings from your readiness assessment to determine which MDM categories have the highest needs, and where your organization is least prepared to begin the undertaking (for an example, see below).
2. Document and review the findings in Tab 2 to determine the appropriate course of action for the categories that have high need and/or low readiness.
3. Don’t panic if the organization isn’t ready at the moment, yet there appears to be a need for MDM. Remember: it is better to postpone a project that will cost a considerable amount of time and money than to go into it unprepared.

For example: if the People area is lacking, ensure that the right roles are in place with the skills necessary for oversight of the MDM project.

If the organization is not yet ready for MDM, use Info-Tech’s resources to improve capabilities that are lacking

Use the resources listed below to begin improvements in the indicated areas that are important for getting MDM off the ground.

Step 2

Determine Business Readiness for MDM

You are here

Step 2: Determine Business Needs for MDM

This step will walk you through the following activities:

• 1.2.1 - Determine which data domain contains the most critical master data in the organization for a MDM strategy.
• 1.2.2 - Plan for the future of how the organization gets the most out of its master data by keeping the end in mind – determine the capabilities that will scale with your MDM strategy to fulfill the needs of the master data users.

This step involves the following participants:

• Data Stewards / Data Custodians
• Head of Information Management
• Information Management Team

Outcomes of this step

• Determine the ideal data domain target for the organization based on where the business is experiencing the largest pains related to master data and where it will see the most benefit of MDM.

There are several key considerations when defining which data is master data in the organization

Recognize what differentiates master data from regular data sets so you don’t waste time with too much data or incorrect data.

Unlike reference data, master data values are usually not limited to predefined domain values.

When selecting what data should be considered master data, consider the following:

• Complexity. As the number of elements in a set increases, the likelihood that the data is master data also increases.
• Volatility. Master data tends to be less volatile. The more volatile data is, the more likely it is transactional data.
• Risk. The more likely data may have a risk associated with it, the more likely it should be managed with MDM.
• Value. The more valuable a data set is to the organization, the greater the chance it is master data.
• Sharing. If the data set is used in multiple systems, it likely should be managed with a MDM system.

Begin by documenting the existing data sources within the organization.

Use Info-Tech’s Master Data Management Business Needs Assessment tool to determine master data sources.

Info-Tech Insight

Master data types can be divided into four main domains

Your domain(s) will help dictate the capabilities your MDM solution should include

MDM solutions can be domain-specific or be designed to support multiple domains. Based on your domain selection results, identify the high-level design capabilities you will require.

Baseline Capabilities

• Data modelling: Ability to model complex relationships between internal application sources and other parties.
• Workflow: Ability to support flexible and comprehensive workflow-based capabilities.
• Information quality: Ability to profile, cleanse, match, link, identify, and reconcile master data in different data sources to create and maintain the “golden record.”
• Loading, integration, synchronization: Ability to load data quality and integrate middleware so there is a bidirectional flow of data. Enable data migration and updates that prevent duplicates within the incoming data and data found in the hub.
• Security: Ability to control access of MDM and report on activities. Ability to configure and manage different rules and visibilities.

Industry-Specific Capabilities

• Certain MDM solutions are geared toward particular industries, such as healthcare.

Single-Domain Capabilities

• Pre-template data models for either party or product domains.
• Highly-domain specific adaptors including aspects of customer acquisition, data cleansing, product attribute standardization, and flexible model support.

Transition Capability: Flexibility in the data model for adding new data domain sources.

Multi-Domain Capabilities

• Lineage: Preserve raw data initially received in the hub and provide a record of all changes for auditing golden records.
• Modelling: Enable flexibility so that platform behavior changes according to data modeling changes. Hub should represent relationships and entities found in the data.

Info-Tech Insight

More specific capabilities exist depending on the overall strategy of the MDM solution. If additional domains or categories are expected to be added to the MDM umbrella in the future, being able to smoothly integrate that data into the existing MDM solution is imperative.

As an organization desires greater value from master data, its MDM practice needs to be extensible to accommodate

One of the biggest challenges in MDM recently has been the shift from single-data domain practices to multi-data domain practices.

Multi-domain MDM is a solution that manages multiple types of master data in one repository.

The history of the MDM industry makes multi-domain MDM difficult. The first MDM systems were customer data integration (CDI) solutions or product information management (PIM).

Organizations that were quick to implement these types of MDM solutions for their departments have already invested in addressing one domain per department with little or no integration with other business units (BUs). The lack of integration makes it more difficult or even impossible to find a “single version of the truth” in these siloed organizations.

By implementing multi-domain from the beginning, an organization is better able to support growth across all dimensions and BUs.

The trend toward big data is driving some organizations to adopt multi-domain MDM. The variety of data that big data provides can apply to more than one domain and business unit. This makes it more complex to integrate big data into single-domain MDM, but the benefits of the elusive golden record across the enterprise is worth the effort.

Main Benefits of Multi-Domain MDM

1. Cost effectiveness: The platform can be implemented in a single instance; avoids redundant cost, training, and maintenance resulting from multiple data silos.
2. Easier to maintain: Single architecture, unified governance, and better analytics based on cross-functional (business unit) taxonomy.
3. Everyone in the organization is working from the same underlying data. IT is not wasting time regularly reconciling multiple sets of redundant master data.
4. Prevention of MDM failure: Provides a single technology stack for master data instead of distinct, uncoordinated approaches to separate master data domains (built on multiple technology stacks).

Capture the unique needs of your organization for a MDM strategy using the Info-Tech Business Needs Assessment

Master Data Management Business Needs Assessment

Understanding the data sources present in the organization and how the business uses this data is critical to implementing a successful MDM strategy.

Use the Master Data Management Business Needs Assessment to assess the business-critical data to target in your MDM strategy.

After you have a better understanding of what data will benefit from a MDM strategy, narrow down your choice of technical solution by keeping in mind recommended capabilities.

Info-Tech Insight

Keep in mind: regardless of domain and organizational needs, there are two core capabilities that form the foundation of any MDM project. Without organizational buy-in and an understanding of the existing data environment, there is no chance for a MDM implementation to be successful.

Step 3

Create a Strategic Vision for Your MDM Program

You are here

Step 3: Create a Strategic Vision for Your MDM Program

This step will walk you through the following activities:

• 1.3.1 - Understand the true goal of MDM – ensuring that the needs of the master data users in the organization are fulfilled.
• 1.3.2 - Create a plan to obtain organizational buy-in for the MDM initiative.
• 1.3.3 - Organize and officialize your project by documenting key metrics, responsibilities, and goals for MDM.

This step involves the following participants:

• Data Stewards / Data Custodians
• CEO, CDO, or CIO
• Information Management Team

Outcomes of this step

• Obtain business buy-in and direction for the MDM initiative.
• Create the critical foundation plans that will guide you in evaluating, planning, and implementing your immediate and long-term MDM goals

MDM is not just IT’s responsibility; make sure the whole organization is involved throughout the project

Master data is created for the organization as a whole, so get business input to ensure IT decisions fit with corporate goals and objectives.

MDM belongs to the entire organization – not a specific department – and should be created with the needs of the whole organization in mind.

The ownership of master data is the responsibility of the business. IT is responsible for the MDM project’s technology, support, platforms, and infrastructure; however, the ownership of business rules and standards reside with the business.

MDM requires IT and the business to form a partnership. While IT is responsible for the technical component, the business will be key in identifying master data.

"There is often a disconnect between what the IT team considers the implementation of a MDM project vs. what the business group would consider the value driven from integrated customer information." (Mehmet Orun, Data Strategy Leader, Salesforce)

Info-Tech Insight

More often than not, the development agenda of an organization is driven by business initiatives or new applications. Internal IT projects like MDM, no matter how compelling the benefits are, may get pushed to the background. One way to approach this is by attaching MDM to another business project (e.g. CRM or BI projects).

Keep the priorities of the users of master data at the forefront of your MDM initiative

To fully satisfy the needs of the users of master data, you have to know how the data is consumed. Information managers and architects must work with business teams to determine how organizational objectives are achieved by using master data.

Steps to understanding the users of master data and their needs:

1. Identify and document the users of master data – some examples include business units such as marketing, sales, and innovation teams.
2. Interview those identified to understand how their strategic goals can be enabled by MDM. Determine their needs and expectations.
3. Determine how changes to the master data management strategy will bring about improvements to information sharing and increase the value of this critical asset.

"Too often there is a concentration on the details of data integration and the creation of the records to be stored in the master data repository without understanding who the consumers of that master data are and what they intend to do with that data." (David Loshin, Knowledge-Integrity, Incorporated)

Info-Tech Insight

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.

Interview business stakeholders to understand how IT’s implementation of MDM will enable better business decisions

30-60 Minute Interview Sessions

Activity: Panel and Individual Interview with the Business

Objective

Instructions

1. Identify which members of the business you would like to interview to gather an understanding of their current data issues and desired data usage. (Recommendation: Gather a diverse set of individuals to help build a broader and more holistic knowledge of data consumption wants or requirements.)
2. Prepare your interview questions.
3. Interview the identified members of the business.
4. Debrief and document results.

Tactical Tips

• Include members of your team to help heighten their first-hand knowledge of the business.
• Identify a team member to operate as the formal scribe.
• Keep the discussion as free flowing as possible; it will likely enable the business to share more.
• Don’t get defensive – one of the goals of the interviews is to open communication lines and identify opportunities for change, not create tension between IT and the business.

Info-Tech Insight

Consider interviewing as a panel to help save time and promote more discussion. Creating a group discussion in the interviews can help make individuals feel more comfortable contributing and enable them to build on each other’s comments.

Info-Tech Insight

Prevent the interviews from being just a venue for the business to complain about data by opening the discussion of having them share current concerns and then focus the second half on what they would like to do with data and how they see master data assets supporting their strategic plans.

A worldwide leader in beauty products kept the business needs in its MDM vision to implement a strategy that brought real value

CASE STUDY

Industry: Personal Care

Source: IBM Global Business Services

L’Oréal

As a worldwide leader in beauty products, L’Oréal has data spread out across many different geographic and business regions on multiple systems. In addition, beauty products differ from culture to culture depending on the local standards of beauty. This changes the data dimensions of each product across regions. Coupled with the lack of a single master system, this meant that L’Oréal had many challenges finding a reliable source of truth in its product data.

As a result of inaccurate product data, L’Oréal experienced higher costs of product distribution. It also paid higher taxes due to stringent regulatory requirements that could not be met due to an inability to accurately report on its packaging.

Master Data Management Strategy

Sponsored by high-level management, L’Oréal’s IS Project Director initiated a MDM project to align product information across all parties, including supply, manufacturing, distribution, and retail.

The technological integration of the systems was the easy part of the MDM project – the difficulty lay in understanding the organizational structure and its link to the business processes.

Bringing in a third party, L’Oréal was able to implement a group of MDM repositories that gave it a single version of truth, yet with the flexibility across different locations and business units, which was important to the business.

Benefits of reliable MDM to L’Oréal’s product management:

1. Synchronization – less time wasted on searching multiple systems
2. Less manual time spent on fixing incorrect data or missing data
3. Clear, unified, and business-guided rules of product data creation and management
4. Strategic cross-functional initiatives in packaging and marketing
5. Greater insight into processes downstream from product development

Ensure buy-in for the MDM project by aligning the MDM vision and the drivers of the organization

Value to IT

• Single view of the customer
• Promotes common enterprise systems
• Data is easier to maintain
• Improves data consistency and accuracy
• Reduced operating costs and time

Value to Business

• Optimize customer value
• Improved ability to integrate acquired entities
• Improved productivity
• Enables other data dimensions such as big data to be harnessed
• Ability to rely on data

MDM will help the business...

• Save costs
• Generate revenue
• Increase compliance
• Support future growth of the organization

"If an organization only wants to look at MDM as a tech project, it will likely be a failure. It takes a very strong business and IT partnership to make it happen."

Optimize buy-in by using language specific to your industry

(Source: Oracle, “Building the Business Case for Master Data Management”)

Use Info-Tech’s Master Data Management Business Case Presentation to help you secure business buy-in

Master Data Management Business Case Presentation

This presentation should be used to help obtain momentum for the ongoing master data management initiative and continued IT- business collaboration.

Master data management and the state of processes around data can be a sensitive business topic. To overcome issues of resistance from the operational or strategic levels, create a well-crafted business case.

Key Messages to Convey to the Business

Principles of Master Data Management
• Master data management is not just a technology solution – it is a partnership between IT and the business.
• Master data is the lifeblood of the organization and a business asset.
• Master data management can increase the organization’s competitive edge by reducing costs and increasing operational efficiency.

Business Value
• Process improvements that come with increased master data quality: reduced errors, trustworthy data, increased data sharing, and usability.
• How the project relates to: Customer Optimization, Risk Management, Regulatory Compliance, Product/Service Innovation, and Operational Excellence.

Collecting business-oriented metrics can be time consuming and difficult, but they are important

IT metrics are generally overarching and can be applied to MDM projects in a variety of organizations. Business metrics on the other hand tend to be unique and vary between organizations.

IT Metrics

• Broad metrics that can typically be applied to any organization, in any industry.
• Information is easily gathered by IT, making these metrics easy to measure.
• Will not necessarily show the value of MDM to the organization, but these metrics will help IT track the success of the MDM project.

Examples of IT-oriented metrics:

• Decrease in duplicate records.
• Fewer process delays.
• Increase in reporting accuracy.

Business Metrics

• Organization specific.
• More complex and often involve multiple stakeholders, so it may be more difficult to capture measurements.
• Clearly illustrate the value of MDM to the organization as a whole.

Examples of business-oriented metrics:

• Increased compliance.
• Cost savings.
• Increased revenue.
• Higher rates of customer retention.
• Streamlined customer service program.

Metrics will play a key role in showing the value of MDM

Activity: Brainstorming Session(s)

Participants: Project Manager, Project Team and Related MDM Professionals

Without metrics, it will be difficult to tie the value of implementing MDM back to the reasons for starting the project.

Stakeholders will expect to see quantifiable results quickly, and metrics make this possible.

When it comes to delivering metrics to the business, MDM may not necessarily show the same “wow” results sales figures would. However, MDM is incredibly important to the business because it supports operations.

Many metrics will be specific to the MDM project being completed. However, there are a few overarching metrics that can be used in almost any MDM project (e.g. duplicate master data records and number of master-data-related help desk tickets).

To baseline yourself, document:
1. The estimated number of duplicate master data records in your master data.
2. The estimated number of business requests annually regarding poor data quality or lack of trust in data.

Use Info-Tech’s project charter to support your team in organizing their master data management plans

Master Data Management Project Charter

Use your charter as a project management tool.

Use this master document to centralize the critical information regarding the objectives, staffing, timeline, budget, and expected outcome of the project.

Prior to project launch, prevent confusion by creating a clear plan that outlines the essential information and project steps.

Consider the common pitfalls which were mentioned earlier that can cause IT projects to fail. Plan and take clear steps to avoid or mitigate these concerns.

Build project management in at the beginning, using these areas:

• Project Purpose
• Goals and Objectives
• Domain Selection
• Rationale
• Scoping and Timeline
• Key Milestones
• RACI Chart
• Executive Signatures

Use business language and drivers to outline the value behind executing this master data management project

Project Manager and Project Sponsor

Output: Key Project Messaging

Participants: Project Manager, Project Sponsor

Results Documentation: Document results in the relevant sections of the project charter.

Overview

Define the value proposition behind addressing master data strategies and developing the organization's master data management practice.

Instructions

1. Craft a clear statement/message outlining the business value associated with executing the project.
2. Identify the business benefits expected from the outcomes of the project (recommendation: consider the strategic business drivers).
   • Mapping project and MDM practice benefits back to business drivers, not IT pain points, will help make a meaningful case for how master data management is a key business enabler and a valuable project to undertake.

Consider

• Why is this project critical for the business?
• Why should this project be done now, instead of delayed further down the road?

Sample Mission Statement

Using the insights from analyzing [Company Name]’s consistent and reliable customer master data, the project will provide opportunities to improve customer intimacy through enhancements to the customer acquisition, growth, and support functions.

Define your master data management goals and objectives

Goals & Objectives

It is important to be clear and transparent about the goals and objectives of your MDM project, for those on the team as well as stakeholders and key decision-makers. Although this will be an ongoing process and may feel like an uphill battle, make sure to start with the end in mind.

Your goals and objectives should be practical and measurable (we will address metrics and KPIs shortly).

Goals and objectives should be mapped back to the reasons for MDM that we identified in the Executive Brief. It is okay if your reasons are not below, just be sure to keep the business in mind when defining your goals.

1. Customer Engagement Optimization
2. Risk Management
3. Operational Excellence
4. Regulatory Compliance

Example Objectives

• Creating a roadmap that will enable you to stage your organization’s MDM strategy incrementally.
• A fit-for-purpose and collaborative approach that first addresses easy data domains such as reference data. The initiative will then target high priority and impactful master data assets and design a practice to support them.
• Align the organization’s IT and business capabilities in MDM for the requirements of the organization’s business processes and the data that supports it.

Define the success criteria and expected outcomes of the project

Input: Stakeholder Expectations

Output: Defined Success Criteria

Materials: MDM Project Charter

Participants: Project Manager, Project Sponsor

Overview

Master data management as a concept can change based on the organization and with definitions and expectations varying heavily for individuals. Ensure alignment at the outset of the project by outlining and attaining agreement on the expectations and expected outcomes (deliverables) of the project.

Instructions

Clarify the expectations of the project and determine the final outcomes expected at the end of the project.
1. Project Management (project sponsor and manager) discuss and determine what successful completion of the project looks like.
   • What deliverables must be completed at the end of the project?
   • What evaluation and planning must be completed?
2. Document expected outcomes of the project in the charter.

Recommended Outcomes

• A completed and approved roadmap
• Outline of an action plan (immediate next steps)
• Documented data strategies
• Collaboration plans between IT and the business related to master data management

Measurements of ROI and the quantitative benefits of the planning performed at this stage will come later on with the implementation of initiatives identified in the roadmap and the realization of master data management strategies.

Outline the plan for completing the project

Use the sample material in the charter and the Develop Your Timeline for the MDM Project section to support developing your project plans.

Activity: Planning Meeting

Output: Project Scope, Outcome Expectations, Project Plan, List of Expected Deliverables and Artifacts

Materials: MDM Charter Template, MDM Business Needs Assessment Tool

Participants: Project Manager, Project Sponsor

Instructions

1. As a group, discuss and document the following:
   • Scope of the project
     • Identify both in scope and out of scope items.
     • Make sure to identify the data domain areas being evaluated.
   • Project plan
     • Include project steps and milestones
   • Project expectations and outcomes
   • Expected project deliverables and artifacts
2. Document the plans for your project in the associated sections of the project charter to align with the outcomes and deliverables associated with the project.

Recommended project scope based on the blueprint content and structure

1. Align Master Data Management Plan with the Business
   Step: Determine the master data requirements of the business


2. Evaluate Master Data Management Capabilities
   Step: Perform an evidence-based assessment of current practices
   Step: Determine target capability levels


3. Create a roadmap
   Step: Develop alignment strategies and improvement plans
   Step: Create a strategic roadmap and plan of action

Identify the resourcing requirements for your project

Activity: Project Planning Meeting

Objective

Create a project team that has representation of both IT and the business (this will help improve alignment and downstream implementation planning).

Instructions

1. Determine the resourcing plan for the project.
2. Identify at what steps project participants and members will be engaged.
3. Outline the responsibilities for each member of the project team:
   • Support the formal identification of responsibilities by associating resources with items in a work breakdown structure tool.
4. Evaluate resourcing plan against additional project and workload expectations to ensure realistic expectations are in place.

Business roles to engage

• Data Owners (for subject area data)
• Data Stewards who are custodians of business data (related to subject areas evaluated)
• Data Scientists or other power users who are heavy consumers of data
If business user says they do not have the time to participate in a hands-on engagement, check to see if they are willing to participate in an interview to help identify data requirements.

IT roles to engage

• Data Architect(s)
• Any data management professionals who are involved in modeling data, managing data assets, or supporting the systems in which the data resides.
• Database administrators or data warehousing architects with a deep knowledge of data operations.
• Individuals responsible for data governance.

Use the structure of the project charter for further support.

Document results in your charter.

Research support

Create Project Management Success

Adopt Organizational Change Management Best Practices

If you want additional support, have our analyst guide you through this phase as part of an Info-Tech workshop

Book a workshop with our Info-Tech analysts:

• To accelerate this project, engage your IT team in an Info-Tech workshop with an Info-Tech analyst team.
• Info-Tech analysts will join you and your team onsite at your location or welcome you to Info-Tech’s historic Toronto office to participate in an innovative onsite workshop.
• Contact your account manager (www.infotech.com/account), or email Workshops@InfoTech.com for more information.

The following are sample activities that will be conducted by Info-Tech analysts with your team:

• 1.1.1 - What does master data management mean to your organization?

  Definitions and implementations of master data management vary greatly depending on environments and organizational consumption of master data. To begin the MDM workshop the facilitator will frame the four-day engagement by understanding the current practices of master data management and how it is positioned in the organization.

• 1.2.1 - Establish the needs of the organization.

  To develop an improved understanding of the data environment and what master data domains should be of focus for the MDM initiative, workshop participants will use the stakeholder interviews as well as the MDM Business Needs Assessment.

If you want additional support, have our analyst guide you through this phase as part of an Info-Tech workshop

Book a workshop with our Info-Tech analysts:

• 1.3.1 - Interview key business stakeholders.

  To develop an improved understanding of the strategic plans of the business, current master data usage, and data requirements, workshop participants will lead interviews with business stakeholders.

  Debrief business interviews and discuss the implications.

  The Info-Tech facilitator will guide the workshop participants in evaluating the findings of the business interviews, discuss their implications on the project plans, and identify key takeaways.



• 1.3.2 - Discuss strategies to optimize business support for MDM.

  To make sure that participants have the buy-in and alignment with the business to ensure that the MDM project is sustainable, the facilitator will discuss techniques for framing the project in language that will show its value. Workshop participants will brainstorm key benefits, pains, and metrics of success related to MDM to populate the MDM Business Case Template.

  Brainstorm and discuss key master data goals and metrics.

  To clarify the long-term goals and understand what a successful MDM project will look like to the organization, workshop participants will generate and document ideas of goals and metrics. This list and the priorities related to the data assets will be documented and used to help plan the gradual scope increase of the MDM initiative.

Develop a Master Data Management Strategy and Roadmap

PHASE 2

Create a Plan and Roadmap for the Organization's MDM Program

Use Info-Tech’s tools and templates to guide you in designing, organizing, and promoting a comprehensive MDM program

Assessment and Planning Resources	Final Documentation

Phase 2 Overview

Detailed Overview

• Step 1: Perform a MDM Capabilities Assessment
• Step 2: Develop MDM Initiatives
• Step 3: Create a Roadmap and Plan of Action for Master Data Management
• Appendix
  • Info-Tech’s Information Management Framework
  • Research Contributors
  • Bibliography

Outcomes

• Define your “as-is” MDM state, framed in the Info-Tech MDM capabilities framework.
• Determine what your end state for the MDM strategy should look like, and come up with plans to bridge the gaps.
• Create a strategic roadmap for the MDM initiative.

Benefits

• Make sense of MDM as it relates directly to your organization, and use this knowledge to create a tailored MDM solution that gives the most value out of the critical data assets of your organization.

Phase 2 outline

Call 1-888-670-8889 or email GuidedImplementations@InfoTech.com for more information.

Complete these steps on your own, or call us to complete a guided implementation. A guided implementation is a series of 2-3 advisory calls that help you execute each phase of a project. They are included in most advisory memberships.

Guided Implementation 2: Create a Plan and Roadmap for the Organization's MDM Program

Proposed time to Completion: 6-8 weeks

Step 1

Perform a MDM Capabilities Assessment

You are here

Step 1: Perform a MDM Capabilities Assessment

This step will walk you through the following activities:

• 2.1.1 - Understand the Info-Tech Research Group MDM framework.
• 2.1.2 - Measure the organization’s current data governance surrounding MDM.
• 2.1.3 - Measure the organization’s current data architecture surrounding MDM.
• 2.1.4 - Understand capabilities surrounding the technology aspect of MDM.
• 2.1.5 - Understand other crucial MDM capabilities.

This step involves the following participants:

• Data Stewards / Data Custodians
• Head of Information Management
• Information Management Team

Outcomes of this step

• Using the organization’s current capabilities surrounding MDM enablers, including data governance and architecture, determine the gaps and priorities for implementing a successful MDM initiative.

As a dimension of information management, MDM has to be backed by key enablers

Info-Tech’s Master Data Management Framework is a two-tiered framework that reflects the importance of relevant master data enablers in building a sustainable MDM program.

Assess the organization’s MDM capabilities to generate an accurate picture of the current state of MDM

In order to know where to begin with MDM initiatives, you have to know your current state.

Participants: Project Manager, Project Team and Related IT Professionals

Materials: MDM Capabilities Assessment and Initiative Planning Tool

As you learn about the data management capabilities that surround a successful MDM project in the following slides, critically assess where your organization currently stands using the Info-Tech MDM Capabilities Assessment and Initiative Planning Tool.

Keeping these capabilities in mind will help provide overarching guidance for the development of a MDM governance and architecture.

Other capabilities, such as Data Risk Management and Data Quality Management, are collectively critical to the MDM project and will make the difference between success and failure.

These principles and guidelines will help with both the initial design of the MDM program, as well as its ongoing maintenance.

Assess the organization’s current MDM implementation as well as its master data assets and systems

Understanding the data sources present in the organization and how the business organizes and uses this data is critical to implementing a successful MDM strategy.

How the organization’s data flows through IT systems is a convenient way to define your MDM initiatives.

Operational MDM

As you manage data in an operational MDM system, the data gets integrated back into the systems that were the source of the data in the first place. The “best records” are created from a combination of data elements from systems that create relevant data (e.g. billing system, call center, reservation system) and then the data is sent back to the systems to update it to the best record. This includes both batch and real-time processing data.

Analytical MDM

Generates “best records” the same way that operational MDM does. However, the data doesn’t go back to the systems that generated the data but rather to a repository for analytics, decision management, or reporting system purposes.

Discovery of master data is the same for both approaches, but the end use is very different.

The approaches are often combined by technologically mature organizations, but analytical MDM is generally more expensive due to increased complexity.

Document the organization’s long-term plans for MDM in the Current MDM Implementation Tab of the Info-Tech Capabilities Assessment and Initiative Planning Tool.

Effective data governance will create the necessary roles and rules within the organization to support MDM

Data governance and data architecture are two of the most important capabilities of MDM and, along with the tools used by IT to implement MDM, are cornerstones of the MDM framework.

Data Governance

Involves an organizational committee or structure that defines the rules of how data is used and managed – rules around its quality, processes to remediate data errors, data sharing, managing data changes, and compliance with internal and external regulations.

What is required for governance of master data?

Defined roles, including data stewards and data owners, that will be responsible for creating the definitions relevant to master data assets.

"Organizations shouldn’t spend months in a room trying to define their data governance rules in isolation. Governance rules should be defined by key business initiatives or key business problems at the time." (Mehmet Orun, Data Strategy Leader, Salesforce)

Ensure MDM success by defining roles which represent the essential high-level aspects of MDM

Regardless of the maturity of the organization or the type of MDM project being undertaken, all three representatives must be present and independent. Effective communication between them is also necessary.

Business Representative

Role ensures:
• MDM business requirements are defined.
• MDM business matching rules are defined.

Technology Representative

Role ensures:
• MDM technology requirements are defined.
• MDM support is provided.
• Infrastructure to support MDM is present.

Governance Representative

Role ensures:
• MDM roles and responsibilities are clearly defined.
• MDM standards are adhered to.

The following roles need to be created and maintained for effective MDM:

• Data Owners are accountable for:
  • Data created and consumed.
  • Ensuring adequate data risk management is in place.
• Data Stewards are responsible for:
  • The daily and routine care of all aspects of data systems.
  • Supporting the user community.
  • Collecting, collating, and evaluating issues and problems with data.
  • Managing standard business definitions and metadata for critical data elements.
For a more detailed description of the Data Steward role, refer to Info-Tech’s Data Steward Job Description.

"The roles could be called processes, but the processes will change based on maturity, whereas the roles do not. If you try to combine the roles, you will inevitably fail."" (Ken Bergmann, Architect)

Another crucial aspect of implementing MDM governance is defining match rules for master data

Matching is important for removing redundancy, improving data quality, and providing more comprehensive data overall.

One of the main challenges in MDM is matching, merging, and linking data from multiple systems about the same item, person, group, etc. This is particularly challenging for data about people (or the Party domain).

Matching attempts to remove redundancy, improve data quality, and provide information that is more comprehensive.

Matching is performed by applying inference rules. Data cleansing tools and MDM applications often include matching engines used to match data.
- Engines are dependent on clearly defined matching rules, including the acceptability of matches at different confidence levels.

Despite best efforts, match decisions sometimes prove to be incorrect. It is essential to maintain the history of matches so that matches can be undone when discovered to be incorrect.

Match-Merge Rules vs. Match-Link Rules

Match-Merge Rules:
• Match records and merge the data from these records into a single, unified, reconciled, and comprehensive record.
• If rules apply across data sources, create a single unique and comprehensive record in each database.
• Complex due to the need to identify so many possible circumstances, with different levels of confidence and trust placed on data values in different fields from different sources.
• Challenges include the operational complexity of reconciling the data and the cost of reversing the operation if there is a false merge.

Match-Link Rules:
• Identify and cross-reference records that appear to relate to a master record without updating the content of the cross-referenced record.
• Easier to implement and much easier to reverse.
• Simple operation; acts on the cross-reference table and not the individual fields of the merged master data record, even though it may be more difficult to present comprehensive information from multiple records.

Data architecture will assist in producing an effective data integration model for the technology underlying MDM

Data governance and data architecture are two of the most important capabilities of MDM and, along with the tools used by IT to implement MDM, are cornerstones of the MDM framework.

Before designing the MDM architecture, consider:

• How the business is going to use the master data.
• Architectural style (this is often dependent on the existing IT architecture, but generally, organizations starting with MDM find a hub architecture easiest to work with).
• Where master data is entered, updated, and stored.
• Whether transactions should be processed as batch or real-time.
• What systems will contribute to the MDM system.
• Implementation style:
  • This will help ensure the necessary applications have access to the master data.

Data quality is directly impacted by architecture.

• With a MDM architecture, access, replication, and flow of data are controlled, which increases data quality and consistency.
• Without a MDM architecture, master data occurs in application silos. This can cause redundant and inconsistent data.

Central to a MDM program is the implementation of an architectural framework

Info-Tech Research Group’s Reference MDM Architecture uses a top-down approach to show the interdependent relationship between layers.

A top-down approach shows the interdependent relationship between layers – one layer of functionality uses services provided by the layers below and in turn, provides services to the layers above.

Info-Tech Research Group’s Reference MDM Architecture can meet the unique needs of different organizations

Applications represent the source and/or target systems for a MDM system.

Interfaces represent the different transport methods used by applications and reporting.

Data is distributed and transformed using data integration technologies (e.g. ESB or ETL). Although data migration technologies can be found on either side of the MDM hub, two separate tools are not needed.

Reference and reporting represents MDM target systems

The MDM service layers that make up the hub are:

• Virtual Registry. The virtual registry is used to create a virtual view of the master data (this layer is not necessary for every MDM implementation).
• Interface Services. The interface services work directly with the transport method (e.g. Web Service, Pub/Sub, Batch/FTP).
• Rules Management. The rules management layer manages business rules and match rules set by the organization.
• Lifecycle Management. This layer is responsible for managing the master data lifecycle. This includes maintaining relationships in the system, helping with data quality through profiling rules, keeping authoring logs, etc.
• Base Services. The base services are responsible for managing all data (master, history, metadata, and reference) in the MDM hub.
• Security. Security is the base layer and is responsible for protecting all layers of the MDM hub.

An important architectural decision concerns where master data should live

All MDM architectures will contain a system of entry, a system of record, and in most cases, a system of reference.

Collectively, these systems identify where master data is authored and updated and which databases will serve as the authoritative source of master data records.

System of Entry (SOE)

System of Record (SOR)

System of Reference (SORf)

MDM deployments typically use one of four implementation styles: consolidation, registry, coexistence, and transactional

These styles are complementary and see increasing functionality; however, organizations do not need to start with consolidation.

All organizations are different; identify the architecture and implementation needs of your organization

The implementation style an organization chooses is dependent on organizational factors such as the purpose of MDM and method of use.

Architecture is not static – it must be able to adapt to changing business needs.

Some master data domains may require that you start with one implementation style and later graduate to another style, while retaining the existing data model, metadata, and matching rules. Select a starting implementation style that will best suit the organization.

Organizations with multi-domain master data may have to use multiple implementation styles. For example, data domain X may require the use of a registry implementation, while domain Y requires a coexistence implementation.

Consolidation implementation style

Master data is created and stored in application systems and then placed in a centralized MDM hub that can be used for reference and reporting.

Advantages

• Prepares master data for enterprise data warehouse and reporting by matching/merging.
• Can serve as a basis for coexistence and/or transactional MDM.

Disadvantages

• Does not provide real-time reference because updates are sent to the MDM system in batch mode.
• New data requirements will need to be managed at the system of entry.

Registry implementation style

Master data is created and stored in applications. Key identifiers are then linked to the MDM system which are used as reference for operational systems.

Advantages

• Quick to deploy.
• Can get a complete view of key master data identifiers when needed.
• Data is always current since it is accessed from the source systems.

Disadvantages

• Depends on clean data at the source system level.
• Can be complex to manage.
• Except for the identifiers persisted in the MDM system, all master data records remain in the applications, which means there is not a complete view of all master data records.

Coexistence implementation style

Master data is created and stored in existing systems, and then synced with the MDM system to create an authoritative record of master data.

Advantages

• Easier to deploy workflows for collaborative authoring.
• Creates a complete view for each master data record.
• Increased master data quality.
• Allows for data harmonization across systems.
• Provides organizations with a central reference system.

Disadvantages

• Master data is altered in both the MDM system and source systems. Data may not be up to date until synchronization takes place.
• Higher deployment costs because all master data records must be harmonized.

Transactional implementation style

All master data records are materialized in the MDM system which provides the organization with a single, complete source of master data at all times.

Advantages

• Functions as a system of record, providing complete, consistent, accurate, and up-to-date data.
• Provides a single location for updating and managing master data.

Disadvantages

• The implementation of this style may require changes to existing systems and business processes.
• This implementation style comes with increased cost and complexity.

Choosing the appropriate technology and tools for MDM is an integral aspect of the MDM solution

First, define detailed requirements for the MDM platform. Make sure to keep the big picture in mind. Do not pick requirements that only address a single department; instead, understand what capabilities will benefit the entire organization.

A MDM platform should include certain core capabilities:

• Master data hub: Functions as a system of reference, providing an authoritative source of data in read-only format to systems downstream.
• Data modelling: Ability to model complex relationships between internal application sources and other parties.
• Workflow management: Ability to support flexible and comprehensive workflow-based capabilities.
• Information quality: Ability to profile, cleanse, match, link, identify, and reconcile master data in different data sources to create and maintain the “golden record.”
• Loading, integration, synchronization: Ability to load data quality tools and integrate middleware so there is a bidirectional flow of data. Enable data migration and updates that prevent duplicates within the incoming data and data found in the hub.
• Interoperability: Ability to work with related integration and data quality tools.
• Security: Ability to control access of MDM and the ability to report on activities. Ability to configure and manage different rules and visibilities.
• Ease of use: Including different user interfaces for technical and business roles.
• Scalability and high performance/high availability: Ability to expand or shrink depending on the business needs and maintain a high service level.

Other requirements may include:
• MDM solution that can handle multiple domains on a single set of technology and hardware.
• Offers a broad set of data integration connectors out of the box.
• Offers flexible deployments (on-premise, cloud, as-a-service).
• Supports all architectural implementation styles: registry, consolidation, coexistence, and transactional.
• Data governance tools: workflow and business process management (BPM) functionality to link data governance with operational MDM.

There are several deployment options for MDM platforms; pick the one best suited to the organization’s business needs

Make sure you consider the strategic vision and plan for growth of your organization. It’s likely you’ll be adding more data domains down the road.

MDM solutions come in various shapes and sizes. Some of the more prominent types include:

Decide whether building or buying a solution is best

Is a full MDM suite necessary, or could less pricey data integration tools do the trick? Before deciding to build or buy a solution, make sure to outline high-level MDM requirements and solution objectives.

MDM Build

• When looking into an internal build situation, data volume is a deciding factor. Designing the build around current data volume, without taking into consideration the need to scale to handle future data volumes will cause certain failure of the project.
• The need for constant updates will tax developer resources and processing times, increasing the total cost of ownership.
• The components of a build solution usually contain a mix of vendor tools and in-house development. Ensuring the systems blend together seamlessly can be a complicated process.

MDM Buy

• A buy scenario is more suitable for enterprises that handle high volumes of data, as a commercial product can manage the data without loss of performance.
• Vendor supplied MDM solutions often provide complete functionality in the package, including data steward applications and the ability to be configured to perform real-time processing.
• Commercial MDM products tend to provide a higher level of data accuracy and usually require significantly less time to implement.
• A vendor provided MDM solution is scalable and will grow with the organization.

If you choose to buy a MDM platform, consider leveraging Info-Tech’s Master Data Management Solution RFP Template after you have determined your final selection criteria. This will help you get the vendors’ best efforts and get the most bang for your buck by making the process competitive.

Info-Tech Insight

The first considerations when making this decision are overall implementation time and costs. Remember to also look at ongoing costs of enhancement, maintenance, and technical support. Regardless of whether you build or buy, the MDM strategy needs to consider the future vision and plan for growth.

Capitalize on trends in the MDM technology space to increase your competitive edge

Trends in Master Data Management:

Cloud Computing
• Cloud computing has contributed to an increase in data quality issues. MDM can help synchronize, clean, and govern these data sets.
Business Intelligence
• With MDM, the organization becomes aware of which data is most critical. When the organization begins analyzing the data, it can quickly target the most critical data.
Social Media
• MDM integrates customer data and social media data to help give organizations a 360-degree view of the customer.
Business Process Management Solutions
• BPM can help improve organizational efficiency through the automation and optimization of workflows; MDM ensures the right and trusted critical data is used in the optimization initiatives.
Big Data Management
• MDM can be used to include new critical data sources in a more orderly fashion.

"Going forward from the 2016 timeframe, I think there is a great change in the market where these two offerings [Informatica Big Data Relationship Management and Informatica MDM] will become one singular offering." (Prash Chandramohan, Informatica)

Data strategy planning capabilities will ensure that MDM is aligned with the business vision for data usage

Data strategy planning involves identifying and translating business objectives and capability goals into strategies for improving data usage by the business and enhancing the capabilities of MDM.

Data Strategy

A description of how the organization is looking to be able to view and use data in order to support the achievement of a business capability or strategic goal.

Data Management Strategy

Planning derived from the business’s data strategies that identify the data management sub-practices and capabilities that support the data strategy in being realized. The activity and the planning outcomes are derived from analysis of the data barriers and the vision for the “to be” environment of the business.

Steps to Data Strategy Planning

1. Analyze business strategies
2. Identify requirements for data
3. Develop data strategies
4. Identify direct and indirect data management enablers
5. Design data management strategy

Data operations management creates the processes and policies that bring master data through its lifecycle

Data operations management is the planning, control, and support for data assets across the data lifecycle from creation and acquisition, through archival to purge (Data Management Book of Knowledge, 2009).

Objectives of Data Operations Management

• Implement and follow policies and procedures to manage master data at each stage of its lifecycle.
• Maintain the technology supporting the flow and delivery of data (applications, databases, systems, etc.)
• Control the delivery of data within the system environment.

Indicators of Successful Data Operations Management

• Effective delivery of master data assets to end users.
• Successful maintenance and performance of the technical environment that collects, stores, delivers, and purges organizational data.

Secure the critical master data assets of the organization with data risk management

Data risk management ensures data assets are sufficiently protected and able to be accessed in secure and controlled manners throughout their lifecycle and as they sit at rest or in flight within the organization’s IT infrastructure and business environment.

Objectives of Data Risk Management

• Data is managed with consideration and adherence to the confidentiality, integrity, and availability needs of the business.
  • A balance exists between managing risk of data usage and making data widely visible and available to the business.
• Data assets within the organization’s environment adhere to regulatory standards.
• The IT environment storing the data is proactively addressing data concerns and evolving its protection policies and management practices to address evolving technologies and new threats/concerns.

Critical Success Factors for Data Risk Management

• Sensitive data assets (e.g. PCI data) and areas with high vulnerabilities and downside risks are classified and have controlled access and tight management.
• Security awareness training is present across the organization.
• Data is protected throughout its lifecycle.
• Decisions of technology usage and data access take into account security principles and incorporate security controls during their planning and implementation.
• The organization proactively tests its environment for security threats/weaknesses.

Guarantee that usable and trustworthy master data is available to the business with data quality management

Data quality encompasses planning, implementation, and control activities that apply quality management techniques to measure, assess, improve, and ensure the fitness of data for use (Data Management Book of Knowledge, 2009).

Data quality tools can be part of the MDM product or stand alone. Recognizing when you need to start with data quality and when you should implement data quality as part of a MDM solution can be difficult.

Data quality on its own

If you have an ERP or CRM system that doesn’t have data quality capabilities built in, bringing in a separate data quality tool could relieve some of the biggest pains due to poor data quality. This implementation will also help develop training and skills for future MDM projects. Just as with a MDM solution, think with the end in mind – enterprise-wide data quality is the goal.

Data quality with MDM

Most MDM hubs have data quality capabilities built in, but it may not serve the full purposes of the business. Implementing a data quality solution with a MDM hub as the first “patient” can help with creating an enterprise-wide master data initiative. The business rules that are created for MDM can be adopted for other initiatives and generate higher ROI than expected.

Info-Tech Insight

There are many excellent, time-tested data quality tools for purchase. Don’t fall prey to a quick and easy in-house solution that may take more time and effort to adapt in the long run as the organization grows.

To better manage its master data and get real-time insight, a leading life insurance company improved data quality of its existing CRM system

CASE STUDY

Industry: Insurance

Trillium Software: Case Studies – Success Stories

DELA

Providing life and funeral insurance policies for 3.7 million members in two different countries makes it difficult for DELA to generate a single view of its customers. Add to this that customer household and policy data was stored on 12 different systems and a MDM strategy became a necessity.

Master Data Management Initiative

DELA decided not to implement a dedicated MDM hub and instead used a CRM system as its hub for master data. In turn, this made real-time data quality checks of this business-critical asset a reality. Repairing the long-standing errors in the master data records such as misspellings, multiple entries, and incorrect field entries took time. Standards were created for automatic data cleansing after these initial repairs, and staff were trained in master data quality maintenance.

Results

Due to the realization by DELA that its master data needed to be treated as a critical business asset, effort was put into changing the processes surrounding its master data for the better. This resulted in enhanced customer service through streamlined processes and better access to comprehensive customer information.

"Exposure to cloud-based data services, whether they are reference data sets, data quality maintenance tools, or match and stewardship engines, has a huge benefit, especially to smaller organizations that may not have as much staff to really build up these tools." (Mehmet Orun, Data Strategy Leader, Salesforce)

Build a sustainable MDM program with practice evolution capabilities

Practice evolution involves being flexible. Master data doesn’t exist in a vacuum; it permeates an entire organization and, if managed correctly, can influence decisions in every functional area. Make sure the culture of the organization is taken into consideration.

Pay attention to how people are reacting to a change. Create a structure for dealing with different types of reactions. Be prepared for fear and resistance – it will happen.

Define a strategy to manage the culture and business process change.

Deploy iterative, consistent, and formal MDM communication mechanisms:

• Clear vision for the change and a plan to drive it.
• Strong and visible leadership.
• Early (and frequent) communication to affected stakeholders to educate them on the value of MDM and engage them in the process.
• Opportunities for and responsiveness to feedback.
• Measurement of results.
• Aligned policies/practices, rewards, and recognition.

"MDM initiatives will have a greatly reduced chance for success if they cannot handle change in an agile and flexible manner." (Julie Hunt, Software Industry Analyst, Hub Designs Magazine)

Value of Practice Evolution

• Data is never static; therefore the practices managing and supporting it need to be dynamic and extensible.
• A mentality of continuous improvement allows natural updates to occur and enables the organization to take advantage of new opportunities related to data.

Establish effective MDM technology communication with corporate enterprise integration

Although not part of the essential capabilities of a successful MDM program, enterprise integration is a “next steps” capability of the MDM initiative. This ensures that the integration environment of the organization is designed, implemented, and maintained to facilitate the successful flow of data cross the environment and create an interoperable and scalable platform that can effectively manage inter- and intra-organization integration scenarios (includes integration practices at both the data and application layers).

Objectives for a well-designed and well-executed integration environment

• It is scalable to fit increasing data processing volumes and introductions of new systems into the IT environment.
• It is able to manage the entry of data from external environments.
• It is able to effectively process and deliver data within the time targets required for the business process or objectives

Indicators of an effective enterprise integration strategy and program

• The integration environment includes comprehensive services for all enterprise information.
• Computing power and toolsets support a consistent and scalable delivery of data, including:
  • Integration tools and middleware
  • Common protocols and languages
• Integration and data access is designed in alignment with the business’s data privacy and security framework (effectively protecting all data-in-motion).
• The integration environment is open, product-neutral, and supports intra/inter-organizational integration.

Shared Data is:

• Consistent
• Trustworthy
• Secure

Combine MDM with BI and reporting to enhance the usability and insights captured from the organization’s data

Also not an essential capability of MDM, yet intimately linked to the uses of master data, business intelligence (BI) involves analytical capabilities centered around metrics and measures that gauge past performance and guide business planning.

Having a single source of truth in the data that is fed into BI and reporting systems will improve the accuracy and relevancy of the insights generated.

This will mean that data-driven business plans can be trusted and will drive more value than with unreliable source data.

Business Intelligence

• Reports and Dashboards
• OLAP Queries
• Data Discovery

Questions Asked in BI Scenarios

• What happened?
• How many?
• How often did it happen?

Analytics

• Descriptive Modelling
• Predictive Analytics
• Text Analytics
• Multimedia Analytics
• Optimization and Simulation

Questions Asked in Advanced Analytics Scenarios

• Why is this happening?
• What if….
• What will happen next?
• What is the best outcome?

Step 2

Develop MDM Initiatives

You are here

Step 2: Develop MDM Initiatives

This step will walk you through the following activities:

• 2.2.1 - Determine the end goals of the organization’s MDM strategy in the form of target states.
• 2.2.2 - Determine where the largest gaps are between your current and target states.
• 2.2.3 - Prioritize initiatives based on where the largest gaps are and the importance of the capabilities that are lacking.

This step involves the following participants:

• Data Stewards / Data Custodians
• Head of Information Management
• Information Management Team

Outcomes of this step

• Prioritized and tangible initiatives that can be completed to ensure that the MDM strategy is successful.

Conduct a gap analysis of MDM capabilities to prioritize your initiatives in the MDM strategy

Keep the goal in mind by documenting target state objectives. This will help to measure the highest priority gaps in the organization’s MDM capabilities.

Now that you know where the organization currently stands, follow these steps to begin prioritizing the initiatives:

1. What are you trying to accomplish? Determine target states that are framed in quantifiable objectives that can be clearly communicated. The more specific the objectives are, the better.
2. Evaluate the “delta,” or difference between where the organization currently stands and where it needs to go. This will be expressed in terms of gap closure strategies, and will help clarify the initiatives that will populate the road map.
3. Determine the relative business value of each initiative, as well as the relative complexities of successfully implementing them. These scores should be created with stakeholder input, and then plotted in an effort/transition quadrant map to determine where the quickest and most valuable wins lie.

1. Current State
   • Organization objectives
   • Functional needs
   • Current operating models
   • Technology assets
2. Gap Closure Strategies
   Initiatives involving:
   • Organizational changes
   • Functional changes
   • Technology changes
   • Process changes
3. Target State
   • Performance objectives (revenue growth, customer intimacy, growth of organization)
   • Operating model improvements
4. MDM Program Management Roadmap
   • Prioritized, simplified, and compelling vision of how the organization will implement MDM and transform how master data is used

Use the results of your MDM capabilities assessment to guide you in designing your MDM initiatives

Activity: Team Discussion and Whiteboarding Exercise

Input: Target State and Gap Analysis Results, Strategy Planning Outcomes

Output: Preliminary Initiative Plans

Materials: MDM Capabilities Assessment and Initiative Planning Tool

Participants: Project Manager, Project Team and Related DM Professionals

Analyze Gap Analysis Results -› Brainstorm Alignment Strategies Document MDM Initiatives

Instructions

Analyze Gap Analysis Results
1. As a group discuss the high-level results on tab 5. Gap Analysis Results. Discuss the implications of the gaps identified.
2. Do a line item review of the gaps between current and target levels for each assessed capability by using tab 4. Target State and Gap Analysis.

Brainstorm Alignment Strategies
3. Brainstorm what effort and activities will be necessary to support the practice in building its capabilities to the desired target level. Ask the following questions:
• What activities must occur to enable this capability?
• What changes/additions to resources, process, technology, business involvement, and communication must occur?

Document Data Management Initiatives
4. Group alignment strategies and like activities into like initiatives.
5. Continue to evaluate the assessment results in order to create a comprehensive set of master data management initiatives that supports your practice in building capabilities.

Use effort/transition mapping techniques to support you in prioritizing and sequencing initiatives

~ 30-90 minutes

Materials: Master Data Management Capabilities Assessment and Initiative Planning Tool

Input: Initiative Planning

Output: Effort and Feasibility Analysis

Overview

Unfortunately, most organizations don’t have unlimited resources and time to dedicate to improving their data architecture. As a result, prioritizing and sequencing initiative projects based on feasibility and benefits can help to create immediate value and help your team in maintaining momentum as they implement longer running initiatives.

Instructions

1. Evaluate the different initiatives planned by the team to address identified performance gaps.
2. Consider the constraints, effort, and benefits associated with each project. Using these considerations, plot the initiatives on an effort map. (Customize how this activity is performed if necessary. If it better suits project planning, change the axis to reflect risk or time required.)
3. Use the findings of this map to help sequence and stage your initiatives on a timeline that best aligns with business priorities and implementation capabilities.

Evaluate initiative planning against considerations of dependencies, feasibility, and business priorities

Activity: Brainstorming Session

Input: Business Priorities, Practice Investments and Capabilities, Initiative Plans

Output: Analysis of Initiatives

Participants: Project Manager, Project Team and Related MDM Professionals

Instructions

1. Before proceeding to effort mapping for each initiative consider the following key questions:
• What preceding activities/initiatives must be in place before this initiative can be implemented?
• Is this initiative feasible considering the organization’s environment and the resourcing, scope, and capabilities of the Data Management practice?
• How does this initiative line up to business priorities? Does it directly correspond and support the achievement of a data strategy and the business’s broader strategic goals?
2. Use the results of this analysis to help frame your roadmap plans and your sequencing of master data management initiatives.

Step 3

Create a Roadmap and Plan of Action for MDM

You are here

Step 3: Create a Roadmap and Plan of Action for MDM

This step will walk you through the following activities:

• 2.3.1 - Pinpoint key areas within the strategy where an iterative approach will provide the biggest benefits.
• 2.3.2 - Create your roadmap and plan for communicating it to others.

This step involves the following participants:

• Data Stewards / Data Custodians
• Head of Information Management
• Information Management Team

Outcomes of this step

• A custom-fit strategic roadmap for your organization’s MDM initiative that was built based on the organization’s needs and priorities.
• A strategy for communicating the roadmap to your audience.

Take an iterative approach to MDM to build upon previous successes and decrease the pains of sudden large changes

"Before starting to invest heavily in infrastructure, you need to quantify the needs and the opportunities and then put in place a roadmap that is going to address the business needs as iteratively as possible."

– Mehmet Orun, Data Strategy Leader, Salesforce

Staged and Iterative Approach to MDM:

1. Understand the needs of the business
2. Identify requirements for data
3. Develop MDM strategies
4. Strategic Roadmap
   • a. Implement MDM for reference data
   
   or
   • b. Implement MDM for customer data
   • c. Implement MDM for product data
5. Refine strategies depending on changes in business needs or requirements.

By using this approach, implementation of a management strategy for reference data will establish a strong foundation for future master data services with low complexity and minimal risk, as well as increase trust in IT for implementing future services. It will also provide a consistent definition of values for the implementation of MDM for the selected master data domain.

When targeted data domains such as customer or product master data are incorporated into the MDM strategy after reiterating the business needs and data requirements at a future time, organizational priorities in master data functionalities will be maintained and more easily supported by change management.

Create a master data management roadmap

~ 90 minutes

Activity: Brainstorming and Planning Session(s)

Input: MDM Initiatives Effort Mapping Results, Dependency Results

Output: MDM Roadmap

Materials: MDM Capabilities Assessment and Initiative Planning Tool, Tab 7, MDM Roadmap

Participants: Project Manager, Project Team and Related MDM Professionals

Instructions

1. Use the findings from your effort mapping and initiative planning to sequence, plot, and build your organization’s data management roadmap. Document key information in Info-Tech’s MDM Capabilities Assessment and Initiative Planning Tool, Tab 6. Initiative Planning (e.g. timelines, initiative ownership)
2. Document and review the total roadmap findings in Tab 7. MDM Roadmap.

Create a communication plan for sharing the project outcomes and the next steps for the MDM project

~ 30 minutes

Overview

Prepare to communicate the project’s findings and plans for your MDM project with a formalized plan.

Instructions

1. Create a communication plan that addresses the multitude of executive/high-level stakeholders as well as larger groups that will need to be updated on the changes and the implications.
2. Use the template below to guide you in identifying the different individuals and groups that will need to be communicated with, and determine the specific messaging that should be undertaken.

"Make sure you have constant communication with the sponsors of the plan. Keep them informed; let them know all of the roadblocks you are finding and how you can solve them. Show them the end of the road. It’s going to be a bumpy road, but make sure you can show them how it will look in the end."

– Rafael Villadiego, P.Eng., MBA, University Health Network

Info-Tech Insight

Consider the general literacy of the stakeholders on master data management as you craft your messaging; take the time to define the principles, as well as share the vision and goals for these changes and investments.

Organize your project results and roadmap plans into a final project deliverable

Prerequisites: Completion of Planned Initiatives and Roadmap

Materials: MDM Roadmap Template

Participants: Project Manager, Relevant Project Members

Instructions

1. Organize the outcomes of the project’s strategy planning, assessment, roadmap, and initiative planning activities into a consolidated and presentable version.
2. Review the document and ensure its messaging and recommendations align with the original objectives of the project and outline a clear and persuasive plan to the project’s stakeholders and sponsors.

Info-Tech Insight

Maintain Business Alignment

As you present your project findings and gain approval for your initiative roadmap, make sure you articulate the value of the master data management initiatives and envisioned practice based on business priorities and definitions of value.

If you want additional support, have our analysts guide you through this phase as part of an Info-Tech workshop

Book a workshop with our Info-Tech analysts:

2.1.1

Assess the current maturities and envisioned target state for each applicable master data management component.

The facilitator will guide participants in completing the master data management assessment using a printout version of the tool statements. Depending on workshop size, the assessment can be filled at as a group or with each individual completing their own and achieving consensus through debate.

2.2.2

Analyze the implications of your master data management assessment.

The facilitator will lead the group in reviewing the performance gaps for each MDM component and honing in on critical capability gaps. Using this analysis, the group will begin to envision initiatives and places to build up critical capabilities.

Develop MDM initiatives.

Using the assessment performance gaps and data strategies as critical context and inputs, the facilitator will guide the team in identifying initiatives within and across the components of MDM. Focusing on the critical question: what activities will be necessary to get your capabilities from A-B (current to target)?, the facilitator will support the team in identifying and solidifying specific initiatives that will generate tangible and high impact outcomes.

If you want additional support, have our analysts guide you through this phase as part of an Info-Tech workshop

Book a workshop with our Info-Tech analysts:

2.3.1

Sequence your initiatives into a roadmap.

Using effort, feasibility, priority, and dependency considerations, the facilitator will support your team in analyzing their identified initiatives and mapping out the initiatives and timelines for capability fulfilment across a three-to-five initiatives roadmap.

2.3.2

Plan your project messaging and next steps.

To close out the engagement, the facilitator will guide the team in building a communication plan for specific individuals and groups of the organization that will be impacted and engaged by these changes.

As a final step, the facilitator will debrief with the participants and talk about immediate next steps that they will undertake to complete or carry out the workshop’s activities and plans.

Insight breakdown

MDM is valuable for organizations of all sizes.

MDM can be difficult and expensive, so start small.

Good MDM requires good reference data.

Appendix

Info-Tech’s Data Management Framework
Research Contributors
Bibliography

Data management needs to stand on a solid framework – we used three – DMBOK2, Mike 2.0, & COBIT 5

As part of our research process, we leveraged the frameworks of COBIT 5, Mike 2.0, and DAMA DMBOK2. Contextualizing information management (IM) within these frameworks clarifies its importance and role, and ensures that initiatives are focused on key priority areas.

The DMBOK2 Data Management framework by the Data Asset Management Association (DAMA) provided a starting point for our classification of the components in our IM framework.

Mike 2.0 is a data management framework that helped guide the development of our framework through its core solutions and composite solutions.

The COBIT 5 framework and its business enablers were used as a starting point for assessing the performance capabilities of the different components of information management.

Info-Tech’s Information Management Framework outlines the key data enablers and business information that should likewise be enhanced by and support MDM.

Info-Tech’s Data Management Framework

Info-Tech’s Approach

Info-Tech’s Data Management Framework is designed to show how an organization’s business model sits as the foundation of its data management practice. Drawing from the requirements of the underpinning model, a practice is designed and maintained through the creation and application of the enablers and dimensions of data management.

Please note the components of the model are not meant to reflect a flow diagram, but to instead reflect a taxonomy of capabilities and components needed for effective data management.

Decoding Info-Tech’s Data Management Framework – Layer 1: Business Information

Business Information

Data subject areas provide high-level views of the data assets that are used in business processes and enable an organization to perform its business functions.

Classified by specific subjects, these groups reflect data elements that, when used effectively, are able to support analytical and operational use cases of data.
This layer is representative of the delivery of the data assets and the organization’s consumption of the data.

For a data management practice to be effective it ultimately must show how its capabilities and operations better support the business in accessing and leveraging its key data assets.

Decoding Info-Tech’s Data Management Framework – Layer 2: Information Dimensions

Information Dimensions

Components at the information dimensions layer manage the different types of data and information present with an environment.

At this layer data is managed based on its type and how the business is looking to use and access the data.

Custom capabilities are developed at this level to support:

• Structured data
• Semi-structured data
• Unstructured data

The types, formats, and structure of the data are managed at this level, using the data management enablers to support their successful execution and performance.

Decoding Info-Tech’s Data Management Framework – Layer 3: Data Management Enablers

Data Management Enablers

Info-Tech categorizes data management enablers as the processes that guide the management of the organization’s data assets and support their delivery.

• Govern and Direct
  • Ensures data management practices and processes follow the standards and policies outlined for them.
  • Manages the executive oversight of the broader practice.
• Align and Plan
  • Aligns data management plans to the business’ data requirements.
  • Creates the plans to guide the design and execution of data management components.
• Build, Acquire, Operate, Deliver and Support
  • Executes the operations that manage data as it flows through the business environment.
  • Manages the business’s risks in relation to its data assets and the level of security and access required.
• Monitor and Improve
  • Analyzes the performance of data management components and the quality of business data.
  • Creates and executes plans to improve the performance of the practice and the quality and use of data assets.

Research Contributors

Internal Contributors

• Steven J. Wilson, Senior Director, Research & Advisory Services
• Daniel Ko, Research Manager

External Contributors

• Mehmet Orun, Data Strategy Leader, Salesforce
• Julie Hunt, Consultant and Author, Hub Designs Magazine and Julie Hunt Consulting
• Duane Lyons, Practice Lead, Clarity Solution Group
• Ajay Raina, Director of Enterprise Information Management, Cognizant Technology Solutions
• Tamer Chavusholu, Co-Founder and Manager, KAYGEN Enterprise Solutions
• David Loshin, President, Knowledge Integrity Inc.
• James Pinkett, Information Services Manager, Rocky View County
• Carter Lusher, Analyst Relations, Informatica
• Christopher Burrell, Marketing and PR, IBM
• Nishtha Chouhan, TIBCO Software

Research contributors and experts

Mehmet Orun, Data Strategy Leader
Salesforce

Mehmet Orun leads the Data Solution Advisory Practice at Salesforce.com, advising customers on how to address data quality challenges and maximize the value of enterprise information through proven and emerging practices. Mr. Orun’s philosophies have been shaped through his experiences as a Management Consultant with Ernst & Young, Enterprise Data Architect and MDM Solution Owner at Genentech, and Product Management Director for Salesforce.com where he lead Data.com Data Quality Roadmap and developed the internal Data Operations team. During his career as a data professional, Mr. Orun established himself as a recognized practitioner and thought leader as a result of sharing real-world implementation experiences, often based on leading and emerging practices. He holds a mastery-level CDMP, and was the primary contributor of the Reference and MDM chapter of the DAMA-DMBOK. Mr. Orun received his B.S. in Computer Engineering and his MBA from the University of Missouri-Columbia.

Duane Lyons, Author
Clarity Solution Group

Duane is a Practice Leader at Clarity Solution Group. Prior to joining Clarity, Duane led the Information Transformation practice at Infosys within Financial Services and also led the cross-industry Advanced Visualization CoE. Prior to joining Infosys, Duane led the open systems BI practice of Inforte as well as the Managed Analytics competency. In addition, Duane co-developed the Infosys Data Governance Framework and has helped 10+ clients roll out data governance strategies in the last 2-3 years, and has been a frequent speaker at numerous national and regional MDM & marketing conferences including DMA conventions, NCDM, and DMDays in New York.

Research contributors and experts

David Loshin, President
Knowledge Integrity Inc.

David Loshin is the president of Knowledge Integrity Inc., a consulting, training, and development services company that works with clients on business intelligence, big data, data quality, data governance and master data management initiatives. Loshin writes for many industry publications, including several TechTarget websites; he also speaks frequently at conferences and creates and teaches courses for The Data Warehousing Institute and other educational organizations. In addition, Loshin is the author of numerous books, including Big Data Analytics and Business Intelligence, Second Edition: The Savvy Manager's Guide. He can be reached via his website or by email at loshin@knowledge-integrity.com.

Bibliography

Dewilde, Benjamin, et al. “The Alchemy of Master Data Management.” Westernacher. Web. Nov. 2015. http://www.westernacher.com/sites/default/files/downloads/best_of_breed/MDM%20-%20Whitepaper_EN.pdf.

European Medicines Agency Information Management Division. “EMA Master Data Management Roadmap.” European Medicines Agency. April 2015 Web. Nov. 2015. http://www.ema.europa.eu/docs/en_GB/document_library/Other/2015/04/WC500186290.pdf.

Informatica Corporation. “Master Data Management and Data Migration.” Informatica. Nov. 2014 Web. Nov. 2015. https://www.informatica.com/content/dam/informatica-com/global/amer/us/collateral/white-paper/mdm-in-data-migration-white-paper_2752.pdf.

Loshin, David. “Master Data Management Drivers: Fantasy, Reality and Quality.” Pitney Bowes. 2010 Web. Nov. 2015. http://www.pbinsight.com/files/resource-library/resource-files/master-data-management-drivers-wp.pdf.

Messerschmidt, Marcus, et al. “Hidden Treasure: A Global Study on Master Data Management.” PricewaterhouseCoopers. Nov. 2011 Web. Nov. 2015. https://www.pwc.com/us/en/increasing-it-effectiveness/assets/global-study-master.pdf.

Raina, Ajay. “Synergizing Master Data Management and Big Data.” Cognizant. Aug. 2015 Web. Nov. 2015. http://www.cognizant.com/InsightsWhitepapers/synergizing-master-data-management-and-big-data-codex1414.pdf.

Russom, Philip. “Next Generation Master Data Management” The Data Warehousing Institute. Q2 2012. Web. 1 Dec 2015. https://tdwi.org/research/2012/04/best-practices-report-q2-next-generation-master-data-management.aspx.

Power, Dan and Hunt, Julie. “Multidomain MDM: The Best Value for Your Business.” Hub Designs Magazine. 2013 Web. Nov. 2015. http://www.enterprisemanagement360.com/wp-content/files_mf/1422889419MultidomainMDM.pdf.