In today’s fast-paced commerce world, data management is everything. It powers decision-making, drives growth, and helps organizations gain a competitive edge. But what happens when the data itself becomes unwieldy, inconsistent, or inaccurate?

Enter a master data management platform (MDM). Using MDM, a business can ensure the information they rely on—such as customer or product data—is accurate, consistent, and easily accessible from a central source.

However, implementing the best master data management for your business is no easy feat. It requires careful planning, execution, and ongoing management. Equally important is the support and expertise of an experienced team that can help maximize value on the MDM initiative post implementation.

In this blog post, we’ll share five critical elements that business and IT leaders must consider when implementing their master data management platform.

Read on to see what master data management services can help set you apart from the competition.


  1. Master Data Management Strategy
    When you have gaps and uncertainty, formulating a data strategy is the perfect starting point. It is tied to every outcome and is central to every step in your master data management journey.

As a critical part of master data management services, your data strategy provides the framework to develop a consistent and cohesive approach to managing your data. It should include the “why” behind your efforts and the nuts and bolts of how you’ll get there.

Consider the following steps:

  • Define your business objectives: Perhaps you need to address a worsening returns problem, better predict and manage inventory levels or publish more product content to support endless aisle. Whatever your goals are, define the “why” behind your data program.
  • Identify data needs: Identify the data and workflows needed to support your business objectives. This means understanding what data is currently available, what data is missing, and what data will be required in the future.
  • Develop data quality requirements: Establish requirements to ensure your data remains accurate, complete, and consistent. This typically involves defining data quality rules, data profiling procedures, and data cleansing processes.
  • Establish data roles: Consider how the data will be used and who will have access to it. This means developing data usage policies, access levels, and sharing guidelines.

By focusing on these steps, organizations can develop an effective data strategy that aligns with their business goals and enables successful master data management.


  1. Migration to the New Master Data Management Platform
    Data migration is a crucial step in implementing master data management. It involves moving your organization’s existing data to your new MDM system. But it’s more complex than a copy-paste job. You’ve invested in a shiny new golden record of data, so it’s essential to create a solid foundation of data quality from the start.

Here are some crucial actions to check off your list when planning for data migration:

  • Data discovery: Understand what data you have and where it’s located. This means conducting a thorough inventory of all the data sources and systems that contain master data.
  • Data mapping: Define how data in your existing systems will be transformed and loaded into the new master data management platform. This requires a deep understanding of the data structure and data relationships.
  • Data cleansing: Decide which errors or inconsistencies are acceptable or unacceptable — and complete the necessary fixes to ensure data accuracy as it’s ingested into the new platform.
  • Data validation: After the data is migrated, it’s crucial to validate that the data in the new MDM system is accurate and complete.

It’s important to remember that data migration is not a one-size-fits-all process. Every business has a unique integration path. Your data migration process should be tailored to meet your organization’s specific needs to get the best master data management results.


  1. Unified Data Model for Master Data Management
    When data is siloed, fragmented, and duplicated, it can be a major headache for companies. Integration between business functions and systems makes sharing information and gaining insights challenging. All of this leads to burgeoning technical debt that can take years to resolve.

The solution? A unified data model as part of a master data management implementation.

A unified data model bridges different systems, enabling organizations to contextualize data sources across multiple services. It helps you break down data silos, analyze data in context, apply machine learning at scale, and gain insights that would be impossible to see otherwise.

When designing a unified data model, it’s essential to approach the process with future-proofing in mind, ensuring every data layer and source serves to increase the understanding of your overarching ecosystem. Your data integrations and connectors should also be built to provide the continuous flow of critical data to support your eCommerce, supply chain, and MDM requirements.

It’s also an effort that shouldn’t stop or stay static as you scale your business. That’s where Data-as-a-Service (DaaS) comes into play. PivotreeDaaS is a fully composable ecosystem of data governance, modelling, integration and syndication tools enabled by ML/AI.  DaaS makes it easy to put your data to work without the need for another new system or platform.


  1. Data Governance to Keep Master Data Management on Track
    Data governance is like the “quality control” of your data. In the context of master data management services, data governance provides the guardrails you need to keep your program on track. Data governance helps ensure that your master data is consistent across all systems and applications and that it aligns with the organization’s overall goals. Data governance can also help support a global expansion.

With solid data governance, you can:

  • Improve the accuracy and completeness of data
  • Increase the efficiency of data-related processes
  • Facilitate collaboration across departments and business units
  • Ensure compliance with regulations and industry standards
  • Reduce the risk of data breaches and other security incidents

The key to successful data governance is getting buy-in and commitment from stakeholders across your organization. This means developing policies, procedures, and tools that ensure your data is appropriately managed and governed.


  1. Change Management for Master Data Management Adoption
    Implementing a master data management platform can disrupt the status quo. Change management is all about thinking through how you’ll manage that transition and ensure it’s smooth and successful.

Here are some key elements to consider:

  • Develop a plan for how the MDM solution will be rolled out, including timelines, milestones, and potential risks.
  • Communicate that plan, as well as the benefits of master data management, to employees to help them understand how it will impact their work.
  • Train your employees on the new system and any new business processes that come along with it. Be sure to listen to any concerns and find solutions together.
  • Engage with organizational stakeholders, including executives, business leaders, and end-users. This can help get everyone on board and excited about the change, so they can help drive it forward.
  • Collect feedback from employees and stakeholders to evaluate the success of the change and identify areas for improvement.

By incorporating change management into your master data management services, you can build stronger support for the MDM solution and ensure it delivers on its promises.


Master Your Data with Pivotree
We’ve covered five crucial elements of master data management — from adopting a data strategy, leaving no stone unturned during data migration, developing a unified data model, setting up the guardrails of data governance, and elevating champions for MDM adoption.

However, master data management isn’t a one-and-done event. It’s a journey. And Pivotree is here to help. Our experienced team can guide you through the entire process and ensure you get the best master data management results.

Ready to get your best master data management results?
Start by learning about one of our most popular turnkey data services, DaaS. Get a taste of DaaS by viewing our infographic, or dive deeper with a free download of our White Paper: Why Organizations Must Make Data Central to the Digital Commerce Landscape.


About Pivotree: Pivotree designs, builds, and manages frictionless commerce experiences for brands and their customers around the world. We provide end-to-end solutions and services in Commerce, Data Management, and Supply Chain for hundreds of brands globally.


1. What are some common challenges or pitfalls that businesses encounter when implementing master data management platforms, and how can these be effectively addressed or mitigated?

When implementing master data management (MDM) platforms, businesses often encounter challenges related to data integration, stakeholder alignment, and technological compatibility. For instance, integrating disparate data sources and systems into a unified MDM platform can be complex and time-consuming, requiring careful planning and execution. Additionally, aligning stakeholders from different departments or business units to agree on data standards and governance policies can present challenges. Technological compatibility issues may arise when integrating legacy systems or dealing with data formats that are incompatible with the MDM platform. To address these challenges, organizations should invest in thorough planning, engage stakeholders early in the process, and leverage technology solutions that facilitate seamless data integration and management.

2. How have businesses successfully implemented data governance frameworks as part of their master data management strategies? Additionally, what are some key considerations for tailoring data governance practices to different industries or business environments?

Implementing data governance frameworks as part of master data management strategies requires careful planning and execution. For example, a retail company successfully implemented data governance by establishing clear policies and procedures for data management, appointing data stewards responsible for data quality and compliance, and implementing technology solutions for data governance enforcement and monitoring. Similarly, a healthcare organization tailored its data governance practices to comply with industry regulations such as HIPAA, ensuring patient data privacy and security. Key considerations for tailoring data governance practices include industry-specific regulations, organizational culture, and data sensitivity levels.

3. What are the strategies or best practices for overcoming resistance to change and fostering employee buy-in during the transition to a new MDM platform? How can businesses effectively communicate the benefits of MDM to employees and stakeholders and address any concerns or uncertainties they may have?

Change management is crucial for ensuring successful master data management adoption and overcoming resistance to change among employees. Strategies for fostering employee buy-in include clearly communicating the benefits of MDM, providing comprehensive training and support, and involving employees in the decision-making process. For example, a manufacturing company successfully implemented MDM by conducting workshops and training sessions to educate employees about the benefits of centralized data management and soliciting feedback to address concerns and uncertainties. Additionally, engaging with organizational stakeholders, including executives and business leaders, can help build support for MDM adoption and drive the change forward. Collecting feedback from employees and stakeholders throughout the process enables organizations to evaluate the success of the change and identify areas for improvement.