Salesforce Master Data Management: Best Practices, Guide, and Tips for MDM

With information being one of the most valuable assets a business can have, data management at scale is one of the core competencies for this day and age. Hundreds of thousands of organizations rely on CRM systems, like Salesforce, which extend this very core competency by allowing a host of benefits associated with better information management.

However, knowing how to exploit the full feature set of such a platform is every bit as important as deploying one. Many benefits go off the radar because of incorrect settings or just simple mistakes. Salesforce is especially important in this context, providing a multitude of features that revolve primarily around data management. Here, Master Data Management as a process is extremely valuable since it covers the reliability, consistency, and accuracy of the most sensitive information in the entire environment.

What is Master Data Management (MDM) in Salesforce?

MDM is a comprehensive process of managing important information in Salesforce environments. The purpose of MDM is to create a single centralized source of truth for all users to work off of in order to manage customers, employees, products, locations, and more. 

The abovementioned “single source of truth” is also often referred to as the “Golden Record” – a single representation of a data entity in the ecosystem that is considered to be the most accurate, up-to-date, and complete version of that information in the entire company’s infrastructure.

It is above and beyond traditional data storage due to its capability to provide a number of policies, technologies, and strategies for master data consistency and uniformity in all Salesforce-adjacent environments. 

Why is MDM important for businesses? 

Master Data Management is critical and indispensable for businesses operating in Salesforce environments. Data, in today’s landscape, is the biggest asset for any company, and maintaining this very important resource with high efficiency confers a raft of advantages in terms of improved data quality, ease of regulatory compliance, and enhanced operational efficiency.

How does MDM work in Salesforce? 

The primary advantage of MDM in Salesforce is centralization – the ability to serve as a unified storage location for information from multiple sources to simplify its management in multiple ways. MDM in this context can be separated into a number of important elements, including:

• Data Consolidation
• Data Cleansing
• Data Enrichment
• Data Governance
• Data Distribution
• Continuous Monitoring

The purpose of all these elements should be mostly self-explanatory, with Data Cleansing being responsible for identifying and eliminating inaccuracies or inconsistencies and Data Governance being responsible for creating and implementing procedures and regulations for accessing and modifying master data. It is also incredibly important to audit and update master data on a regular basis to ensure its relevance and quality in MDM.

What are the key components of MDM in Salesforce?

MDM is usually comprised of a number of key components that are used to create accurate and relevant master data in its final state. Some of these components can be very similar to the elements we have discussed above, but there are also some elements that are completely new, as well. With that in mind, here are the most noteworthy components of Salesforce MDM:

• Data Model that is used to represent the relationships between entities in Salesforce in a clear and accurate fashion.
• Data Quality is ensured by using either built-in or third-party tools that offer validation, deduplication, cleansing, and other features.
• Data Governance is what defines how the master data has to be managed, as mentioned above.
• Data Integration made possible by using flexible APIs of Salesforce in various integration tools to simplify data synchronization from multiple sources.
• Data Stewardship implies the designation of responsibilities for managing and overseeing master data in MDM.
• Management of Metadata relies on tools and features to ensure the relevance and value of master data with its connection to metadata in Salesforce environments.
• Data Security combines all of the processes responsible for ensuring the integrity and safety of master data.

All of these components are essential for creating a robust and convenient MDM strategy in Salesforce to make sure that the most valuable information is managed and protected to a necessary degree.

How does MDM compare with CDP?

Customer Data Platform, or CDP, is a concept that might seem similar to MDM at first. However, there are multiple differences between the two, with the most noteworthy examples being the fact that their target purposes differ from each other.

MDM is focused on presenting consistency and uniformity to core business data, while CDP is focused on providing a consistent customer database to be accessed with other systems or tools. Not only do they differ in target data, but their target audiences and integration capabilities also differ from one another.

With all that in mind, we should also mention that MDM and CDP can even be used alongside each other for improved data lifecycle management, enhanced regulatory compliance, comprehensive customer data overview, and many other advantages.

What are the benefits of implementing MDM in Salesforce?

Master Data Management implementation in Salesforce is a highly beneficial process that is far more than just regular data organization workflows. Companies should be able to improve their operations in multiple ways via standardization, centralization, and other important processes applied to critical business data.

In this section, we would like to review three of the biggest advantages MDM can offer to Salesforce environments: data quality improvements, customer relationship enhancements, and better decision-making.

How can MDM improve data quality?

Data quality in Salesforce is improved in MDM through a number of mechanisms and features. The first example of such a feature is automated data validation – a real-time process that can filter out information at the system’s point of entry, preventing problematic or corrupted information from entering the storage environment.

Contextual deduplication, on the other hand, should be able to save some storage space without affecting the important data elements by using sophisticated algorithms in its identification and merging processes. Historical tracking is another useful feature that can assist with future auditing and potential rollback operations by preserving a comprehensive history of data changes in the environment.

Data enrichment workflows can be set up to automatically enhance existing master data with information from trusted sources, and cross-object consistency is a comprehensive mechanism that ensures the validity and completeness of all master data.

What role does MDM play in enhancing customer relationships?

MDM’s value in customer relationship management is just as high as in data quality, with multiple factors being responsible for improving customer relationships in a myriad of ways.

Customer data consolidation offers a comprehensive overview of a customer with all of its needs and potential touchpoints, leading to a higher degree of personalization in future interactions. The accuracy and completeness of master data can also assist with improving lead quality and better prospect prioritization, with far more tailoring capabilities than ever before.

The aforementioned customer profiles, with their information consolidated in the same environment, dramatically reduce the complexity of customer service interactions, making it a lot easier to resolve issues and even anticipate certain needs beforehand. The same kind of data management tends to somewhat reduce the complexity of marketing efforts in terms of personalization and segmentation, driving better efficiency for current and future marketing campaigns.

The client-oriented nature of most modern businesses often makes this particular advantage more important than any other, considering how the company’s overall success is in direct correlation with customer satisfaction, driving more sales, better publicity, and so on.

How does MDM contribute to better decision-making?

Decision-making is another facet of marketing that Master Data Management tends to improve dramatically, including improved real-time analytics, better resource allocation, and higher accuracy for predictive modeling. Additionally, we should also mention more accurate performance measurements, better risk management, and easier location for market opportunities as important advantages of a properly configured MDM in relation to decision-making.

Master Data Management can be used to transform raw information into a valuable strategic asset in order to enhance decision-making, improve data quality, and even enhance customer relationship management. It is a great option for improving both the present and future marketing efforts of a specific company, considering how data-centric the entire business environment has become in recent years.

What are the best practices and useful tips for implementing MDM in Salesforce?

MDM implementation in Salesforce environments can be reasonably challenging and complicated without prior knowledge of the topic. With that in mind, we would like to offer three major best practices to work off of:

• Creating a Master Data strategy
• Utilizing various tools and applications to assist with MDM realization.
• Ensuring regulatory compliance during and after the implementation.

Defining your Master Data strategy. What is the action plan for MDM?

Trying to implement MDM without a clear and defined strategy is a borderline impossible endeavor due to the overall complexity of the subject. As such, we would like to offer a basic Master Data strategy that can be used to guide businesses along the MDM implementation process:

1. Data identification is the first logical step in this sequence, with the primary goal of finding critical data domains and data sections – product information, account hierarchies, or customer information.
2. Data ownership assignment should be performed directly after the identification process in order to define the accountability chain for quality control, ongoing maintenance, and other processes for each of the data domains that store sensitive information.
3. Each successful MDM implementation has a clear and specific goal behind it that is not just an abstract improvement of data management processes. Setting clear objectives for the MDM initiative (for example, to reduce the total percentage of duplicate records in the system) should dramatically reduce the complexity of implementation while also avoiding potential confusion during later stages.
4. The same logic should be used for the entire implementation plan, and breaking it down into smaller actionable phases with clear goals should help tremendously when it comes to MDM implementation.
5. A set of comprehensive data standards defined beforehand should also improve the accuracy of the implementation if you know what data types and data elements should have specific quality requirements or specific format.
6. Try to think through how exactly master data is going to flow inside and outside of your Salesforce environment to facilitate easier integration from the get-go.
7. All such strategies should be designed and thought through with scalability and growth in mind to make sure that a company would not have to re-do its entire MDM strategy from scratch in a month’s worth of growth time.
8. Thorough monitoring with the ability to gather several valuable metrics should be set up beforehand to be able to analyze your key performance indicators and measure the success of the implementation in the long run.
9. Keeping an eye out on Salesforce’s release notes would also be a great idea to ensure that you are not missing some sort of technology or feature that might be able to assist (or hinder) the capabilities of MDM integration in the future.

What tools and technologies should you use for MDM?

Another important element of MDM integration is the variety of features and tools that are used to perform the integration process and simplify it to a certain degree. For example, many features of Salesforce can be used to enforce specific goals of the MDM integration plan. That way, Validation Rules, and Duplicate Management can both be utilized to enforce standards in terms of data quality.

Data.com is a data management solution that is also owned by Salesforce, offering a convenient way to validate and enrich existing information, including master data, making it useful in certain aspects of MDM implementation. Data integration is another valuable aspect of this process that can be assisted by a selection of integration solutions – with MuleSoft being one of the most prominent examples of such software (also owned by Salesforce).

There is a large market of convenient MDM solutions that can be found on the Salesforce AppExchange store, with convenient features for governance, data cleansing, advanced matching, and other necessary elements of MDM integration. 

The AppExchange store is also not the only place to look for useful software to assist with MDM integration, either. Many third-party data management solutions for Salesforce are not technically considered traditional MDM tools despite their ability to cover multiple elements of the aforementioned integration process.

GRAX is a good example of one such solution, providing a comprehensive data management platform for Salesforce with features such as data backup, archiving, and more. Its vast versioning capabilities are able to maintain data lineage for a long time, and the commitment to following compliance matters results in high-performance data protection capabilities along with a separate cloud environment that is compliant with many regulations.

GRAX also excels in data recovery processes, ensuring that master data stays safe and recoverable no matter what happens with the original storage environment. Its data archiving capabilities can be of great assistance in cases where there is a need to reduce total storage consumption without losing access to specific information (including master data that is subject to MDM integration).

All in all, GRAX’s capabilities in data management can be used as a great addition to MDM integration processes to offer convenient data historicization, backup, archiving, and other capabilities.

How can you ensure data governance and compliance?

Getting back to the topic of MDM integration, the combination of compliance and data governance is the last significant best practice that we would like to cover here. Strong data governance practices are mandatory to ensure data integrity and avoid catastrophic losses caused by the lack of compliance with a specific regulatory framework or local law.

In most cases, creating a Data Governance Committee dedicated to dealing with data compliance in the entire organization is highly recommended for any business above a certain size. The primary purpose of this committee is to oversee all the practices, standards, and data policies that have to be implemented to remain compliant – and that includes Master Data Management.

Establishing dedicated Data Stewardship positions to assign responsibilities for enforcing governance policies and monitoring data quality should alleviate the burden of making sure that all of the data domains follow the necessary regulations, reducing the possibility of human error.

Using Role-Based Access Control in Salesforce’s own security capabilities to safeguard information from being accessed with some sort of malicious purpose. The primary purpose of such a system is to work as a prevention mechanism that restricts user access to sensitive information if the user’s position in the system is not permitted to access the information in question.

Creating audit trails should establish a detailed record of changes with all master data, ensuring its security and simplifying error resolution. Having a clear-cut audit trail for all sensitive information would dramatically improve the company’s ability to detect potential issues and find the root of the problem with specific data pieces if such a need arises.

A lot of traditional compliance recommendations can also be implemented in this situation, such as regular compliance audits, ongoing training, compliance check automation, and so on. Another good idea is to create a clear and structured sequence of actions that should be followed to resolve issues with data quality as they appear, providing a standardized approach to future situations.

Setting up sharing rules for more valuable information is a great way of preventing unauthorized access to specific objects or records. Additionally, the most sensitive records can also be protected with field-level security capabilities from either Salesforce’s own toolset or via a third-party security provider.

These recommendations should offer a good idea of how to create a flexible MDM framework in Salesforce to ensure compliance, improve business value in the long term, and even improve data quality. They also do not represent the complete extent of what can be done to improve MDM realization processes, with other less relevant recommendations being a bigger focus on validation, the usage of workflow rules, regular integration plan reviews, etc.

Similar to most business processes these days, MDM is also an ongoing process that should be refined and improved upon as time goes on to ensure its competency in the present and the future of the company.

What challenges can you face when implementing MDM in Salesforce?

Master Data Management offers many benefits to its Salesforce users, but it also comes with a number of challenges that all MDM users should be aware of, including all the different ways of avoiding these issues.

What are common pitfalls to avoid?

• The inability to recognize the full scope of the MDM as an effort, especially in the context of multi-instance Salesforce environments.
• Lack of strong leadership support causing the loss of momentum and resources.
• Problematic data governance approach.
• The creation of an extremely complex data model without a real need for it. 
• Inability to address the root causes of poor data quality.

With that being said, a lot of these issues have clearly defined resolutions, some of which we have already outlined in our recommendations above. 

Performing a thorough data landscape assessment should make it easier to understand how much effort would be necessary to perform the MDM integration. The existence of a data governance committee with clear data management policies should eliminate most (if not all) reasons for poor data quality and inconsistent information management.

How to address challenges with data quality?

With that being said, an issue of data quality might also be far more difficult and complex to resolve with just one step. If the clear data governance plan does not help, it would be a good idea to perform the full range of potential resolutions and actions, starting with data quality assessment.

The assessment in question should be able to provide a relatively clear understanding of the root of the problem, making it a lot easier to resolve down the line. Of course, data issues connected with critical data elements should be prioritized in the resolution queue for obvious reasons.

More minor issues can be resolved with some of the automated rule sets, including both built-in tools and third-party applications with features such as standardization, validation, duplicate management, etc. An ongoing maintenance system would also be a good idea in order to eliminate the necessity to perform manual data checks and information cleansing processes.

It should also be mentioned that there are some cases where the data quality issues are far too severe to resolve. In that situation, data migration might be a better option of the two – even if it would also require its own detailed plan with data cleansing, validation, and other steps necessary to transfer existing information to an MDM-enabled environment.

How can you overcome resistance to change?

We should also mention one other issue here that is somewhat more difficult to resolve by traditional means – resistance to change. A certain degree of conservatism is natural for many industries, with some examples remaining surprisingly resistant to any changes in a modern data-driven environment (such as the construction industry).

This single issue requires a different approach and might not even provide immediate results, depending on a number of circumstances. What you can do to overcome resistance to change is:

• Proactively address potential concerns that some users might have.
• Gather input and feedback from end-users by engaging them in the design and implementation of MDM.
• Offer a competent degree of user training with online tutorials, hands-on workshops, as well as support resources, and other means of learning.
• Try to articulate the benefits of MDM in the most direct and simple way, highlighting all of the benefits for both the company and its employees that come from the proper implementation of MDM.
• Build momentum by showcasing the tangible benefits of MDM implementation as early on as you can.

How can you leverage Salesforce tools for MDM?

Master Data Management implementation is practically impossible without taking advantage of both built-in and third-party solutions. Additionally, these capabilities should also enhance the quality of your MDM efforts, which is why ignoring them is out of the question in a modern environment.  

What features of Salesforce support MDM?

There are some Salesforce features that we have already mentioned before in this article, but here we would like to offer a more comprehensive overview of capabilities that might assist with MDM implementation in some way:

• Duplicate Management – a tool for data merging and duplicate detection.
• Salesforce Data Loader – a data exporting tool that works best when importing information in bulk, tends to have impressive performance but little to nothing when it comes to customization.
• Validation Rules – the means of enforcing certain data quality standards at the point of entry.
• Flow and Process Builder – both are used to automate certain elements of data management in order to reduce the number of errors associated with the “human factor” and other similar issues.
• Field History Tracking – a feature necessary to create a comprehensive audit trail for critical master data for legal and security reasons.
• Salesforce Shield – a well-known security solution from Salesforce that offers high-grade encryption and a selection of other security features that can help safeguard master data.
• Custom Metadata Types – the feature of Salesforce that can help create and maintain MDM rules for specific data formats.

How to integrate third-party MDM solutions with Salesforce?

Even if the title does mention third-party solutions, it is important to remember that this term is not just about separate applications under their own brands – it also includes AppExchange solutions and a few other categories of tools. With that in mind, we would like to go over a few pieces of advice when it comes to assisting MDM integration using third-party tools.

Researching the AppExchange market should be the first step, considering how the market in question is created to offer features for Salesforce in the first place. If a solution can store information outside of Salesforce, it should even be possible to create a real-time integration using Salesforce Connect (if the performance of the system allows for it).

Many users resort to the so-called “middleware” integration platforms when it comes to MDM implementation, with MuleSoft from Salesforce being an obvious example. The usage of multiple Salesforce APIs in the process of integration is guaranteed, considering how each API has its own range of use cases anyway. In some cases, it should also be possible to create integrations between Salesforce and external MDM tools using custom Lightning components, although this kind of approach requires a somewhat high level of skill with the environment’s toolset.

On the topic of third-party solutions, we should also mention the entire market of advanced ETL (Extract, Transform, Load) tools that can assist with data integration and other tasks related to MDM. Sliced Bread would be a good example of such a solution, with its intuitive data mapping, seamless integration with multiple data sources, and user-friendly interface for improved convenience.

What are the best practices for Using Salesforce APIs for MDM?

Speaking of APIs, we should also go over this particular topic in more detail, considering how Salesforce has several APIs to work with. REST API and SOAP API might be two of the most well-known examples of what Salesforce has to offer, with the former being suitable for basic data-related operations and the latter being a more complex but slower option.

Other worthwhile mentions in the context of MDM are Bulk API and Composite API. Bulk API is created to work with large data loads, including updates and first-time uploads. Composite API, on the other hand, is more suitable for maintaining data integrity in complex transactions but can be more difficult to work with.

In reality, Salesforce has a multitude of APIs that have their own use cases and advantages, but not all of them are useful in situations such as MDM integration. There are also a few concerns that are worth mentioning in the context of API usage as a whole, including:

• Performing regular reviews and updates for all APIs in use to support older versions while also keeping up with the features and capabilities of newer releases.
• Some APIs have limitations in terms of how many calls per day it can perform, with REST API being one of the most popular examples of such. Planning integration processes around this limitation is mandatory for ensuring good performance at different phases of the MDM integration process.

Conclusion: What are the next steps for implementing MDM in Salesforce?

Master Data Management in Salesforce is a serious initiative with the potential to enhance multiple capabilities of an organization. In this article we have explored the fact that successful MDM implementation requires ongoing commitment, stakeholder engagement, and a lot of careful planning in order for the entire endeavor to become successful.

MDM implementation is a challenging process from a technological standpoint, but the fact that it is also a business transformation initiative makes it even more difficult. Following all the steps and recommendations outlined in this article should make it easier to achieve success in MDM implementation and gain a substantial increase in your company’s value. 

Even if the process itself seems challenging, the end results are all worth it. MDM implementation is not just a short-term improvement to a company’s data management capabilities, and this approach can offer even more advantages in the long run. You can expect the parameters such as total cost of ownership, data security, scalability, customer experience, and even business decision-making to be positively affected by the introduction of MDM in the long run, making it even more valuable in terms of potential implementation.

There is no need to start with the most grandiose plans from the start, either – you can always try to start with minor implementation and build upon immediate successes while generating momentum for more fundamental changes. Persistence is the key here, but it would not be effective without a clear strategy in mind, which is why both of these parameters and characteristics are highly recommended in MDM implementation efforts.

Frequently Asked Questions

What metrics should you track for MDM success?

Due to the nature of the topic, there would not be all that many actual metrics that can be converted directly into performance evaluation. In fact, most of the metrics used for MDM evaluation are somewhat unconventional in their nature.

For example, data quality can be monitored by analyzing the percentage of records that meet the quality criteria in specific fields. Month-over-month reduction in the number of identified duplicate records should offer another view of the data quality of the environment.

It should also be possible to analyze the total data utilization values by inspecting the number of API calls, or report runs for these specific records. Other less conventional metrics include the number of data policy violations, percentage of users completing MDM training, time to perform data-related tasks such as order processing and lead qualification, etc.

How often should you review and update your MDM processes?

The rule of thumb for MDM implementation is to conduct performance reviews on a quarterly basis, preferably aligning them with business reviews for the same quarter to showcase the impact of MDM. However, other elements of a newly integrated environment might require different sets of checks and reviews. For example, it is a common recommendation to perform monthly data quality checks, and performing a comprehensive review of the entire integration process once a year to look for improvements and results would also be a splendid idea.

What is the most effective way to engage stakeholders to gather support for MDM?

Identifying key stakeholders with a stakeholder map would be a great first step in this process, followed by creating customized messaging to address the concerns and interests of each group of stakeholders. What comes next is a necessity to keep all stakeholders in the loop when it comes to all the changes and success stories while also setting up a clear communication channel with these stakeholders to gather input and suggestions from them during and after MDM implementation.