How to Connect Master Data Management With Azure Synapse Analytics?

Updated January 4th, 2024
Synapse master data management

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Integrating a master data management tool with Azure Synapse Analytics, Microsoft’s analytics platform, helps organizations achieve data accuracy, consistency, and enhanced analytics. How?

Master data management is a vital use case that complements the Azure Synapse Analytics technology. Master data management helps organizations maintain accurate, consistent, and reliable critical data, such as customer and product information, by creating a centralized repository and establishing data governance rules.

Azure Synapse’s integrated analytics and data warehousing capabilities enable businesses to analyze and visualize this master data effectively. By combining master data management with Synapse, organizations can make informed decisions, enhance operational efficiency, and ensure data accuracy, supporting their data-driven initiatives while avoiding the risks associated with poor data management.


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Table of contents

  1. Why should we use a master data management tool with Azure Synapse Analytics?
  2. Azure Synapse Analytics overview
  3. What is master data management?
  4. Steps for evaluating a master data management tool for Synapse
  5. Guidelines and tips for effective implementation
  6. Master data management for Azure Synapse Analytics: Related reads

Why should we use a master data management tool with Azure Synapse Analytics?

Implementing master data management is crucial because it:


Azure Synapse Analytics overview

Azure Synapse Analytics is Microsoft’s analytics service that brings together big data and data warehousing into one unified platform, delivering insights at cloud scale for faster decision-making.


What is master data management?

Master data management is a method of managing the organization’s critical data, ensuring it’s consistent, accurate, and accessible across the enterprise. It empowers businesses to make informed decisions, enhance operational efficiency, and foster data-driven growth.

Combining a master data management tool with Azure Synapse Analytics enables organizations in several ways. For example, Azure Synapse’s robust analytics and data warehousing capabilities empower businesses to efficiently analyze and visualize centralized, high-quality master data.

This synergy of a master data management tool and Azure Synapse Analytics results in the following benefits:

  • Informed decision-making: Master data management ensures that the data across the organization is accurate and consistent. When this high-quality data is fed into Azure Synapse Analytics Analytics, the insights and analysis derived are much more reliable and support better decision-making.
  • Enhanced efficiency: Master data management streamlines data workflows and improved collaboration boosts operational efficiency. When this organized data is used in Azure Synapse Analytics, it reduces the time and effort required to process and analyze data.
  • Regulatory compliance: Master data management ensures compliance with regulatory requirements. This ensures that the data used for analysis is not only high quality but also compliant with regulations and standards.
  • Competitive advantage: Holistic data views from master data management drive data-driven initiatives, giving a competitive edge in today’s data-centric environment.

Steps for evaluating a master data management tool for Synapse

To evaluate the best master data management tool for Azure Synapse Analytics and making a clear business case for its procurement, consider the following points:

1. Assess business needs


  • Understand the specific master data management requirements and objectives within your organization.
  • Identify data domains and critical data elements that require centralized management.
  • Determine the scale, complexity, and volume of master data to be managed.

2. Compatibility and integration


  • Evaluate how well the master data management tool integrates with existing systems, databases, and data sources.
  • Ensure compatibility with the organization’s preferred cloud platform or data infrastructure.

3. Scalability and performance


  • Assess the master data management tool’s scalability to handle future data growth.
  • Test the tool’s performance under various workloads to ensure it meets data processing demands.

4. Data governance and security


5. User-friendliness and adoption


  • Consider the tool’s user interface and ease of use for business users and data stewards.
  • Evaluate the availability of training and support resources for users.

6. Cost analysis


  • Calculate the total cost of ownership, including licensing, infrastructure, and ongoing maintenance.
  • Compare pricing models and cost structures with competing master data management solutions.

7. Proof of concept (PoC)


  • Conduct a PoC to validate the master data management tool’s functionality and performance in your specific use case.
  • Involve key stakeholders in the evaluation process to gather feedback.

8. Vendor reputation and support


  • Research the vendor’s track record, customer reviews, and case studies.
  • Evaluate the level of customer support, including documentation and community resources.

9. ROI and business benefits


  • Build a business case that outlines the expected return on investment (ROI) and tangible benefits of implementing the master data management tool.
  • Highlight how it will mitigate risks, improve data quality, and enhance decision-making.

10. Implementation plan


  • Develop a clear implementation strategy, including timelines, resources, and responsibilities.
  • Define key performance indicators (KPIs) to measure the success of the master data management implementation.

11. Change management


  • Plan for change management to ensure smooth adoption of the master data management tool across the organization.
  • Communicate the benefits and importance of master data management to all relevant stakeholders.

12. Long-term sustainability


Consider the long-term viability of the master data management solution and its ability to evolve with changing data and business needs.

By following these steps and presenting a well-structured business case that highlights the alignment of the selected master data management tool with organizational objectives and benefits, you can effectively procure the right tool for your organization.


Guidelines and tips for effective implementation

There are several common pitfalls while implementing a master data management tool with Azure Synapse Analytics. Let’s understand some of them.

1. Complexity underestimation


Failing to fully comprehend the complexity of data integration and management within the Master data management solution.

2. Inadequate data governance


Not having a well-defined data governance strategy and clear ownership of data.

3. Overambitious scope


Attempting to tackle too many data domains simultaneously, leading to project overwhelm.

4. Stakeholder engagement


Insufficient involvement of business stakeholders in the master data management process.

5. Data quality oversight


Overlooking data quality issues, which can undermine the accuracy and reliability of master data.

6. Lack of phased approach


Neglecting a phased integration approach, which can disrupt operations.

To avoid these pitfalls, organizations should prioritize careful planning, engage stakeholders, and implement a well-structured master data management strategy with a clear focus on data governance, quality, and incremental integration.



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