A Glossary of Key Terms: Data Integrity & Duplication Concepts
In the fast-paced world of HR and recruiting, reliable data isn’t just a convenience—it’s the backbone of efficient operations, informed decision-making, and successful talent acquisition. Poor data integrity and unchecked duplication can lead to wasted time, incorrect outreach, compliance risks, and ultimately, a compromised candidate or employee experience. This glossary provides HR and recruiting professionals with a foundational understanding of key terms related to maintaining clean, accurate, and actionable data within their critical systems. Master these concepts to safeguard your data, streamline your workflows, and elevate your strategic impact.
Data Integrity
Data integrity refers to the overall accuracy, completeness, consistency, and reliability of data over its entire lifecycle. In HR and recruiting, this means ensuring that candidate profiles, employee records, job requisitions, and performance metrics are free from errors, omissions, or unauthorized alterations. High data integrity ensures that automated workflows, such as candidate screening or onboarding triggers, execute correctly based on factual information. For instance, if a candidate’s status isn’t updated accurately, automated follow-ups might be sent inappropriately, damaging the candidate experience. Maintaining data integrity is crucial for compliance with privacy regulations like GDPR and CCPA, as well as for generating trustworthy analytics that inform recruitment strategies and HR planning.
Data Duplication
Data duplication occurs when the same information exists in multiple records within a single system or across different systems. In an HR context, this often manifests as duplicate candidate profiles (e.g., a candidate applying through different channels or over time), duplicate employee records, or redundant job postings. Duplication can arise from manual data entry errors, system integrations gone awry, or a lack of standardized data input processes. For recruiters, duplicate records lead to inefficient outreach, sending the same communication multiple times, or even reaching out to candidates already in the pipeline under a different entry. Resolving data duplication is essential for a “single source of truth,” preventing wasted effort, and ensuring a consistent candidate and employee journey.
Master Data Management (MDM)
Master Data Management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official shared master data assets. For HR and recruiting, MDM would involve creating a definitive, trusted source for key data entities like candidates, employees, job roles, and organizational units. This means consolidating data from various HRIS, ATS, payroll, and performance management systems into a coherent, standardized view. Implementing MDM helps prevent data silos and ensures that all departments are working from the same, reliable information, which is critical for compliance, reporting, and automated processes such as unified candidate communication or seamless employee lifecycle management.
Single Source of Truth (SSOT)
A Single Source of Truth (SSOT) is a concept that aims to ensure all users in an organization are basing business decisions on the same data. It is a system or repository where all data originates and is maintained, preventing discrepancies that arise when different departments or systems hold conflicting versions of the same information. In HR, establishing an SSOT for candidate or employee data means that whether you’re looking at an ATS, HRIS, or payroll system, the core details (name, contact, status) are consistent and current. Achieving SSOT eliminates confusion, reduces manual cross-referencing, and is foundational for reliable automation, allowing processes like offer letter generation or benefits enrollment to pull verified data without error.
Data Validation
Data validation is the process of ensuring that data inputs are clean, correct, and useful for their intended purpose. It involves checking data for accuracy and consistency before it is stored or processed by a system. For HR and recruiting, data validation checks might include verifying that an email address is in a valid format, a phone number contains the correct number of digits, a date of birth is plausible, or a salary range falls within an acceptable spectrum. Implementing data validation rules at the point of entry (e.g., in an application form or CRM) prevents bad data from entering your systems, reducing the need for costly data cleansing later and ensuring that automated triggers based on these fields function correctly.
Data Normalization
Data normalization is the process of structuring data in a database to reduce redundancy and improve data integrity. It involves organizing the columns and tables of a relational database to minimize data duplication and improve data consistency. While often a technical database concept, its impact on HR is significant. For example, ensuring that a candidate’s skills are stored in a normalized table prevents the same skill from being entered in slightly different ways (“project management,” “proj mgmt,” “PM”), making it easier to search, filter, and automate skill-based matching. Normalized data supports more accurate analytics and makes it simpler to integrate with other systems without encountering data format inconsistencies.
Data Cleansing/Scrubbing
Data cleansing, also known as data scrubbing, is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. It involves identifying incomplete, incorrect, inaccurate, or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. In HR, data cleansing might involve merging duplicate candidate profiles, updating outdated contact information, correcting typos in job titles, or removing incomplete application entries. Regular data cleansing is vital for maintaining the quality of your talent pipeline, ensuring that outreach efforts reach the right people, and providing a solid foundation for predictive analytics and automated recruitment campaigns.
Data Governance
Data governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods. In HR, data governance outlines who is responsible for the accuracy of employee data, how long candidate data should be retained (and subsequently purged), and what privacy standards must be upheld. It ensures that data policies are defined, communicated, and enforced across the organization. Robust data governance is crucial for regulatory compliance (e.g., GDPR, CCPA), minimizing data breaches, and maintaining trust with employees and candidates, especially when automating sensitive data processes.
Referential Integrity
Referential integrity is a property of data which, when satisfied, requires every value of one attribute (column) of a relation (table) to exist as a value of another attribute in a different or the same relation (table). In simpler terms, it ensures that relationships between data tables remain consistent. For example, if you have a table of employees and another table of the departments they work in, referential integrity ensures that every employee is assigned to a department that actually exists in the department table. This is critical in HRIS and ATS systems to prevent “orphan” records or broken links between related data, ensuring that an employee’s performance review is always linked to their current role, or a candidate’s application to an active job requisition.
Primary Key (in context of CRM)
A Primary Key is a special relational database column (or combination of columns) designated to uniquely identify each record in a table. Its main purpose is to enforce entity integrity. In an HR CRM or ATS, a primary key might be an auto-generated numerical ID for each candidate profile, an employee ID number, or a unique requisition ID for a job opening. This unique identifier ensures that even if two candidates have the same name, their records can be distinctly managed. For automation, primary keys are fundamental; they allow systems like Make.com to accurately connect and update specific records across various platforms, preventing data mix-ups and ensuring that the correct data is always referenced.
Unique Identifier
A unique identifier is an identifier that is guaranteed to be unique among all identifiers used for a particular set of items. While a primary key is a specific database concept, a unique identifier is a broader term that encompasses any value (like an email address, employee ID, or a system-generated UUID) that ensures a specific record or entity can be differentiated from all others. In HR, using unique identifiers (e.g., a candidate’s email as a primary unique identifier in an ATS) is crucial for preventing duplicate entries and for ensuring that when integrating systems or merging data, the correct records are matched. This is especially important for deduplication efforts and for maintaining a “single source of truth” for each individual.
Record Merging
Record merging is the process of combining two or more duplicate or related records into a single, comprehensive record. This involves identifying which fields from each record should be retained, updated, or discarded to create a master record that contains the most accurate and complete information. In HR and recruiting, record merging is frequently performed on duplicate candidate profiles where an individual may have applied multiple times or been entered into the system through different channels. Automating record merging (or providing tools for manual merging) significantly improves data quality, ensures a holistic view of each candidate, and prevents recruiters from duplicating outreach efforts or missing key information by only reviewing partial records.
Data Harmonization
Data harmonization is the process of bringing together data from different sources and making it consistent and compatible. This involves standardizing data formats, units, naming conventions, and classifications so that data from various systems can be accurately compared, analyzed, and integrated. For HR, this might mean ensuring that “Project Manager,” “PM,” and “Project Lead” are all mapped to a single, standardized “Project Manager” role title across all systems. Harmonization is critical for accurate reporting across different HR platforms, enabling comprehensive talent analytics, and ensuring that automated workflows (e.g., skill matching for job requisitions) can function effectively without being hindered by inconsistent data representation.
GDPR/CCPA Compliance (related to data integrity)
The General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are stringent data privacy laws that mandate how organizations collect, process, and store personal data. While primarily focused on privacy rights, they intrinsically link to data integrity. Compliance requires that personal data is accurate, complete, and up-to-date. This means HR and recruiting teams must have robust processes for data validation, cleansing, and governance to ensure candidate and employee data is correct, that irrelevant or outdated data is purged, and that individuals’ requests for data correction or deletion can be fulfilled efficiently. Failure to maintain data integrity can lead to significant compliance fines and reputational damage.
CRM Data Hygiene
CRM data hygiene refers to the practices and processes involved in maintaining clean, accurate, and up-to-date data within a Customer Relationship Management (CRM) system, or in the HR context, an Applicant Tracking System (ATS) or HRIS. This encompasses regular activities like deduplicating records, updating contact information, correcting errors, removing outdated entries, and standardizing data formats. For HR and recruiting professionals, excellent CRM data hygiene ensures that automated recruitment campaigns are targeted correctly, candidate pipelines are precise, and reporting provides reliable insights into hiring performance. Proactive data hygiene minimizes manual effort, maximizes the effectiveness of automation, and prevents critical data from becoming a liability.
If you would like to read more, we recommend this article: HighLevel HR & Recruiting: Master Contact Merge Recovery with CRM-Backup
