Master Data Management to achieve a Single Source of Truth
Revenue Operations teams are often held to a standard of truth. How many times have you heard that your CRM (Salesforce, Hubspot, or whichever you use) is your Single Source of Truth? In my opinion, this is a very high bar to meet. The concept has long existed in business systems applications. The core idea of an SSOT was to eliminate data redundancy and inconsistencies by having one central location for all critical information, reducing the risk of conflicting data across different teams or systems.
Early attempts to implement SSOT faced difficulties due to the sheer volume of data, diverse data formats, and the need to integrate information from various systems, making it hard to maintain a truly unified repository.
As technology advanced, the SSOT concept evolved to incorporate data governance practices, data lineage tracking, and sophisticated data integration tools, allowing for a more robust and flexible approach to managing a single source of truth.
So today I’m going to lay out an initial foundation for your team to start setting up a Master Data Management strategy for you to achieve your single source of truth.
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What is Master Data Management (MDM)? For the largest of enterprises this is a well known term. For earlier stage firms this may be a new term. But the concept itself should be well known. MDM is a process that involves business and IT (or in our case, Revenue Operations) working together to create and maintain a single source of truth for critical data in an organization.
My approach requires a few pillars to help organizations scale. It quickly becomes apparent: without strong MDM policies, your data infrastructure is a house of cards. From inaccurate reports to workflow bottlenecks, a weak MDM foundation can erode trust and slow growth.
Let’s break down four key principles of an effective MDM framework that keeps your revenue engine firing on all cylinders.
Enrichment
Normalization
Data Sources and Sync Intervals
Data Quality and Data Conflict Resolution
Data Enrichment
Just buy a data enrichment tool and you’re done right? ZoomInfo, Apollo, LeadIQ, Cognism. There are many options out there each competing along a couple of different vectors. Some are trying to be all-in-one-tools, data orchestration providers, or regionally relevant. But lately there has been a movement towards waterfall enrichment. This approach was once reserved only for companies that could afford more than one enrichment solution or hiring overseas contractors.
It worked like this.
Waterfall enrichment is a progressively layered reference process. Start with the basics: account names, domains, and industries. Then move downstream to append richer data like technographics, firmographics, and intent signals. The goal isn’t to flood your database with every possible data point but to prioritize what adds clarity and operational value. For example, enriching industries through a source like ZoomInfo allows your teams to segment accounts with precision, fueling targeted campaigns and more relevant conversations. If ZoomInfo doesn’t have the data you need then use Apollo to find the missing data point. If Apollo doesn’t have the data point then use a third, then a fourth. If all else fails, then send it for manual review. That’s the waterfall.
Now it isn’t realistic for every company to acquire multiple solutions. It’s just not budget friendly. To sail around this, companies are now finding solutions that offer these credits to you through their platform. Meanwhile, that vendor essentially purchases credits at a wholesale price from the original data provider. RevOps enrichment has started to move to a manufacturer > distributor > retailer model.
Data Normalization
If data is the new oil, then normalization is the refining process that turns crude information into something actionable. Without standardized data—like consistent country codes, industry classifications, or naming conventions—your CRM becomes a minefield of errors. Imagine one record labeled “US” and another “United States.” When systems can’t reconcile these discrepancies, reporting breaks, and manual clean-up becomes the norm. Investing in normalization policies ensures that your entire org speaks the same language when it comes to customer data.
The most common fields to normalize include the following:
Company and contact information
Company Name: Standardize capitalization, abbreviations, and naming conventions (e.g., "Inc." vs. "Incorporated").
Website Domain: Use consistent formats for URLs (e.g., without "www.").
Contact Name: Format names consistently (e.g., "John Smith" instead of "Smith, John").
Email Address: Ensure lowercase and validate format.
Location information
Country: Use ISO 3166-1 alpha-2 or alpha-3 codes (e.g., "US" or "USA" instead of "United States").
State/Province: Normalize using full names or abbreviations (e.g., "CA" for California).
City: Standardize capitalization (e.g., "San Francisco" instead of "san francisco").
Postal Code: Ensure consistent formats (e.g., "94105" vs. "94105-1234").
Address Lines: Remove unnecessary characters and standardize abbreviations (e.g., "St." for "Street").
Industry and company data
Industry: Use a consistent classification system, such as NAICS, SIC, or custom categories (e.g., "Information Technology" instead of "IT").
Revenue: Normalize units and ranges (e.g., "1M-10M USD" instead of "$1,000,000").
Employee Count: Standardize ranges (e.g., "10-50 employees" instead of "Small Business").
Ownership Type: Use clear categories (e.g., "Private," "Public," "Non-Profit").
Contact information
Phone Numbers: Apply consistent formats with country codes (e.g., "+1 (415) 555-1234").
Preferred Contact Methods: Normalize field values (e.g., "Email" instead of "e-mail").
Time Zones: Standardize by region (e.g., "PST" instead of "Pacific Standard Time").
Dates and times
Date Formats: Use ISO 8601 (YYYY-MM-DD) or the standard format for your region.
Timestamps: Include time zones or convert to UTC for consistency.
Categorization and tags
Lead/Account Status: Standardize terms (e.g., "Active," "Inactive").
Lead Source: Use consistent categories (e.g., "Webinar" instead of "Webinar Sign-Up").
Opportunity Stages: Ensure stages are aligned with CRM naming conventions (e.g., "Closed Won" instead of "Won").
Custom Fields: Document and apply consistent values across systems.
Unique identifiers
Customer/Account IDs: Ensure IDs are unique and consistently formatted across systems.
SKU/Product Codes: Standardize formats for product and service identifiers.
Currency
Currency Codes: Use ISO 4217 codes (e.g., "USD" for U.S. Dollars).
Monetary Values: Normalize decimal points and separators (e.g., "$1,000.00" vs. "1000,00").
Data Sources and Sync Intervals
Not all data sources are created equal. Part of a strong MDM policy is defining which sources of truth are authoritative for specific fields. For example, marketing might rely on LinkedIn or Clearbit for firmographics, while Sales Ops leans on Salesforce for opportunity details. This clarity reduces redundancy and eliminates conflicting updates. Equally important is sync cadence—determining how often your systems talk to each other. Whether it’s real-time updates or weekly batch processes, the sync intervals should reflect the velocity of your business while minimizing system strain.
I often combine point ETL solutions such as Coefficient for a manageable sized dataset and leverage other scalable options to manage much larger datasets.
There’s a tendency to want instant data fixes within your CRM. But setting your sync intervals too frequently may cause your API limits to be reached. Once this occurs then the entire data system may be out of date for a prolonged period of time. Instead, encourage your teams to work on a sync interval which is reasonable. Perhaps data syncs are 15 minutes for organizations that have larger API calls or use it for critical fields. Daily syncs should be leveraged where possible to reduce API calls. Use these syncs for important, yet not operationally sensitive data fields.
Here is a quick guide to inform your thinking:
Real-Time Sync:
Use real-time sync for data critical to daily operations, like lead assignments, opportunity updates, or customer support cases. Examples include marketing leads routed to sales or updates to opportunity stages that need immediate visibility.
Recommended Use Case: High-velocity sales teams or time-sensitive workflows like lead follow-ups.
Hourly Sync:
Suitable for moderately time-sensitive data, like updating customer records or syncing product usage metrics from other systems.
Recommended Use Case: Mid-sized organizations that need regular updates but don’t require instant changes.
Daily or Nightly Sync:
Ideal for less time-critical data like enrichment fields (e.g., industry classification or revenue updates) or back-office systems like ERP integrations.
Recommended Use Case: Companies with slower sales cycles or when syncing large data sets to reduce strain on APIs during business hours.
Weekly Sync:
Reserved for low-priority data, such as historical records, bulk imports, or reporting updates.
Recommended Use Case: Data used for forecasting, quarterly planning, or compliance purposes.
Resolving Data Quality and Conflicts
No system is immune to bad data. Duplicate records, missing fields, and conflicting updates are inevitable. But how you resolve these issues separates high-performing teams from chaotic ones. Implement workflows that flag conflicts—like a recent update from sales versus older data from marketing—and define escalation paths for resolution. Whether it’s automated tools, human oversight, or both, having a playbook for addressing quality issues ensures your team spends less time firefighting and more time driving revenue.
Master Data Management may not win you applause in your next all-hands, but its impact is felt everywhere—cleaner dashboards, better alignment, and fewer headaches. By embedding these principles into your MDM strategy, you’re not just managing data; you’re building the backbone for scalable growth.