A Glossary of Key Terms in Data Migration & Integration for HR & Recruiting
In today’s data-driven world, the ability to seamlessly move and connect information across various HR, recruiting, and operational systems is not just an advantage—it’s a necessity. For HR and recruiting professionals, understanding the core concepts of data migration and integration is crucial for building efficient, automated workflows, ensuring data accuracy, and leveraging technology to its fullest potential. This glossary provides clear, authoritative definitions for key technical terms related to moving and connecting data, helping you navigate the complexities of modern HR tech stacks and automation initiatives.
Extract, Transform, Load (ETL)
ETL is a fundamental three-step process in data warehousing and integration, crucial for consolidating data from disparate sources into a unified repository. In the context of HR and recruiting, ETL might involve extracting candidate data from an Applicant Tracking System (ATS), transforming it by standardizing formats or enriching it with specific HR-related tags, and then loading it into a CRM like Keap or a data warehouse for advanced analytics. This process ensures data consistency and quality, making it ready for reporting, compliance, or further automation. For instance, an ETL process could clean up inconsistent job titles or merge duplicate candidate profiles before they enter a new system, saving recruiters countless hours of manual data hygiene.
API Integration
API (Application Programming Interface) integration refers to the seamless communication between two or more software applications using their respective APIs. For HR and recruiting, this is vital for creating automated workflows that span multiple systems. Imagine a scenario where a new hire’s information automatically flows from your ATS to your HRIS (Human Resources Information System) and then triggers a provisioning request in your IT system, all without manual intervention. API integration facilitates this real-time or near real-time data exchange, eliminating manual data entry, reducing errors, and accelerating processes like onboarding, candidate screening, or background checks. It’s the backbone of connected HR tech ecosystems.
Data Mapping
Data mapping is the process of creating a direct relationship between data fields in two distinct data models or databases. This is a critical step in any data migration or integration project for HR, ensuring that data points from a source system (e.g., “CandidateName”) are correctly matched to corresponding fields in a target system (e.g., “FirstName” and “LastName”). Incorrect data mapping can lead to lost data, miscategorized information, or failed integrations, causing significant operational headaches. When migrating from an old HRIS to a new one, meticulous data mapping ensures that every piece of employee history, performance review, or compensation data finds its correct new home, preserving data integrity and historical records.
Data Cleansing
Data cleansing, also known as data scrubbing, is the process of detecting and correcting (or removing) corrupt, inaccurate, incorrectly formatted, duplicate, or irrelevant records from a dataset. In HR and recruiting, clean data is paramount for effective decision-making and efficient operations. Poor data quality can lead to misdirected communications to candidates, inaccurate compliance reporting, or flawed analytics on recruitment pipelines. Automating data cleansing—for example, removing duplicate candidate entries, standardizing address formats, or updating outdated contact information—improves the reliability of your HR data, enhances the candidate experience, and ensures your automated workflows operate on dependable information.
Legacy System
A legacy system refers to an old method, technology, computer system, or application program that is still in use, typically because it continues to fulfill its purpose adequately, but it might be outdated or incompatible with newer technologies. In HR, this often means older HRIS, payroll, or applicant tracking systems that lack modern API capabilities, scalability, or user-friendly interfaces. While functional, legacy systems can become significant bottlenecks for automation and digital transformation, making data migration to newer platforms a complex but necessary undertaking. Understanding legacy system limitations is the first step toward modernizing your HR tech stack for improved efficiency and strategic capabilities.
Data Lake
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It’s designed to hold raw data in its native format until it’s needed for analysis, making it highly flexible. For HR and recruiting, a data lake could store diverse information like resumes (unstructured text), interview notes, social media profiles, performance reviews (structured and semi-structured), and ATS data, providing a single source for comprehensive talent analytics. Unlike a data warehouse that stores pre-processed data, a data lake enables HR leaders to explore and derive insights from a vast array of raw data, fostering innovation in talent acquisition and management strategies.
SaaS Migration
SaaS (Software as a Service) migration is the process of moving data, applications, and sometimes entire business processes from an on-premise system or one SaaS platform to another. This is increasingly common in HR and recruiting as organizations adopt cloud-native solutions for greater flexibility, scalability, and cost-efficiency. For example, migrating from an on-premise payroll system to a cloud-based HRIS, or moving candidate data from an older ATS to a more modern, AI-powered recruitment platform. SaaS migration requires careful planning, data mapping, and testing to ensure data integrity, minimize downtime, and seamlessly transition operations without disrupting critical HR functions like payroll or hiring.
Data Governance
Data governance refers to the overall management of the availability, usability, integrity, and security of data used in an enterprise. It encompasses the people, processes, and technology required to manage and protect organizational data. In HR and recruiting, robust data governance is critical for ensuring compliance with regulations like GDPR or CCPA, maintaining candidate privacy, and safeguarding sensitive employee information. It establishes policies for data retention, access control, data quality, and audit trails. Strong data governance supports ethical data usage, reduces legal risks, and builds trust with employees and candidates, providing a framework for responsible data automation.
Data Pipeline
A data pipeline is a set of processes that moves data from one system to another, often performing transformations along the way. Think of it as an automated conveyor belt for your data. In HR, a data pipeline could automatically move newly submitted applications from a career page, parse the resumes for keywords, enrich candidate profiles with publicly available data, and then push them into a recruiter’s workflow within an ATS. These pipelines automate what would otherwise be manual, repetitive tasks, ensuring that data is consistently and reliably delivered to the right place at the right time, minimizing delays and improving the speed and efficiency of recruitment and HR operations.
Data Warehouse
A data warehouse is a large repository that stores integrated data from one or more disparate sources, providing a central view of business information. Unlike operational databases that handle day-to-day transactions, data warehouses are optimized for querying and analysis. For HR and recruiting, a data warehouse could consolidate data from your ATS, HRIS, payroll system, and performance management software, offering a holistic view of your workforce. This enables HR leaders to conduct deep historical analysis on hiring trends, employee turnover, diversity metrics, or compensation patterns, providing strategic insights that drive evidence-based decision-making and long-term talent strategy.
Data Silo
A data silo refers to a collection of data that is isolated from other parts of an organization, often stored in a system that is not integrated with other systems. These silos prevent a unified view of information and hinder cross-departmental collaboration. In HR, examples include candidate data isolated in an old spreadsheet, employee training records stored only within a learning management system, or compensation data trapped in a legacy payroll system. Data silos are major obstacles to automation and data-driven decision-making, leading to inefficiencies, duplication of effort, and an incomplete understanding of the workforce. Breaking down data silos through integration is key to achieving a “single source of truth.”
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, this means having a definitive, consistent record for core entities like “Employee,” “Candidate,” or “Job Role” across all systems. MDM prevents inconsistencies (e.g., an employee having different addresses in the HRIS and payroll system) and ensures that all departments are operating with the same, reliable information. Implementing MDM is critical for organizations looking to integrate multiple HR systems and maintain high data quality for compliance, reporting, and automation.
Real-time Integration
Real-time integration refers to the process of connecting systems and applications in a way that allows data to be exchanged and synchronized instantly, as soon as it is created or updated. This contrasts with batch processing, where data is transferred periodically. In HR and recruiting, real-time integration is invaluable for time-sensitive processes. For example, when a candidate accepts a job offer in the ATS, real-time integration can immediately trigger a new employee record creation in the HRIS, initiate onboarding tasks, and send notifications to relevant managers. This eliminates delays, enhances the new hire experience, and ensures that all systems are constantly updated with the most current information, critical for agile operations.
Batch Processing
Batch processing is a method of processing data in groups or “batches” at scheduled intervals, rather than continuously or in real-time. Data is collected over a period and then processed together. While less immediate than real-time integration, batch processing is highly efficient for large volumes of data that do not require instant updates. In HR and recruiting, examples include nightly payroll runs, weekly updates of employee benefits data from an HRIS to an insurance provider, or monthly reports on recruitment metrics. It allows systems to manage resources effectively by processing large tasks during off-peak hours, ensuring data consistency while minimizing impact on live operational systems.
Data Security
Data security encompasses the protective measures taken to prevent unauthorized access to computer data, including data alteration or destruction. For HR and recruiting, data security is paramount due to the highly sensitive nature of employee and candidate information, including personal identifiable information (PII), financial details, and health records. Robust data security measures involve encryption, access controls, regular audits, incident response plans, and compliance with data protection regulations. Ensuring the confidentiality, integrity, and availability of HR data is not just a legal obligation but also crucial for maintaining trust with employees and candidates, preventing breaches that could have severe reputational and financial consequences.
If you would like to read more, we recommend this article: CRM Data Protection for HR & Recruiting: Mastering Onboarding & Migration Resilience
