A Glossary of Key Terms in Database & Data Structure for Recovery
In today’s fast-paced HR and recruiting landscape, data is the lifeblood of efficient operations, strategic hiring, and compliant record-keeping. Understanding the foundational concepts of databases and data structures isn’t just for IT professionals; it’s crucial for anyone managing candidate information, employee records, or talent acquisition pipelines. This glossary defines key terms, offering HR and recruiting professionals a clearer picture of how data is organized, protected, and recovered, especially in the context of automation and safeguarding vital information.
Database
A database is an organized collection of structured information, or data, typically stored electronically in a computer system. It’s the digital filing cabinet where all your HR and recruiting data—candidate profiles, application histories, employee records, performance reviews, and more—resides. For HR professionals, a robust database ensures quick access to critical information, supports detailed reporting, and serves as the foundation for automated processes like applicant tracking systems (ATS) or CRM integrations. Without a well-structured database, managing large volumes of HR data becomes chaotic, inefficient, and prone to errors, directly impacting hiring speed and compliance.
Data Structure
Data structure refers to a specialized format for organizing and storing data in a computer so that it can be accessed and modified efficiently. Think of it as the blueprint for how data points relate to each other within your HR systems. Common data structures include arrays, linked lists, trees, and graphs, each optimized for different types of operations. For HR and recruiting, understanding data structures helps in designing more efficient ATS, CRM, or HRIS systems that can quickly retrieve candidate information, match skills, or analyze hiring trends. Proper data structuring is vital for high-performance automation, enabling quick searches and seamless data flow between integrated tools like Make.com and your primary HR platforms.
Relational Database
A relational database organizes data into one or more tables (or “relations”) of rows and columns, with a unique key for each row. The relationships between these tables are defined, allowing data to be joined and queried across different entities. Most traditional HRIS, ATS, and CRM systems (like Keap) are built on relational databases because they provide strong data consistency, integrity, and structured querying capabilities. For recruiting, this means candidate data might be in one table, job applications in another, and interview notes in a third, all linked by unique identifiers. This structure facilitates complex reporting, ensures data accuracy, and is ideal for systems requiring predictable, reliable data interactions, especially when automating candidate journeys.
SQL (Structured Query Language)
SQL is the standard language for managing and manipulating relational databases. It’s used to create, retrieve, update, and delete data within these systems. While HR and recruiting professionals aren’t typically writing SQL queries daily, their software platforms rely heavily on it behind the scenes. For example, when an ATS filters candidates by specific skills or retrieves a list of applicants for a particular job, SQL is the engine performing that request. Understanding SQL’s role underscores the importance of well-structured data for efficient querying and reporting, which directly impacts the speed and accuracy of talent acquisition and HR analytics, especially when setting up custom integrations and reporting dashboards.
NoSQL (Non-Relational Database)
NoSQL databases offer a more flexible and scalable alternative to traditional relational databases, designed to handle large volumes of unstructured or semi-structured data. Unlike SQL databases, they don’t rely on fixed schemas, allowing for more dynamic data models. Examples include document, key-value, wide-column, and graph databases. In HR and recruiting, NoSQL databases might be used for storing vast amounts of varied candidate data from multiple sources, social media profiles, or large-scale internal knowledge bases where data structure isn’t always uniform. Their flexibility makes them suitable for applications requiring rapid scalability and agility, such as sentiment analysis of candidate feedback or real-time data streaming in complex automation workflows.
Schema
In the context of databases, a schema is the logical design or blueprint that defines the structure of the database. It specifies how data is organized, including table names, field names, data types (e.g., text, number, date), relationships between tables, and constraints (rules that data must follow). For HR and recruiting professionals, the schema of their ATS or CRM dictates exactly what information can be stored about a candidate or employee and how it relates to other pieces of information. A well-defined schema ensures data integrity, consistency, and efficient retrieval, which is critical for accurate reporting and seamless integration with automation tools. Any changes to a schema, even adding a custom field, can have downstream effects on data entry, reports, and existing automation.
Table
In a relational database, a table is a collection of related data organized in rows and columns. Each table represents a specific entity, such as “Candidates,” “Job Openings,” or “Employees.” Columns define the attributes of that entity (e.g., Candidate Name, Email, Phone Number), while rows contain the actual data for each individual entry (e.g., John Doe’s details). For HR and recruiting, tables are fundamental for segmenting and managing different types of data. Effective table design prevents data redundancy and ensures that all information for a specific candidate or role is consistently stored, making it easier to query, report on, and integrate with automation platforms like Make.com for seamless data synchronization.
Field (Column)
A field, often referred to as a column in a database table, represents a specific attribute or characteristic for each record. For example, in a “Candidates” table, fields might include “First Name,” “Last Name,” “Email,” “Phone Number,” “Skills,” or “Application Date.” Each field is designed to hold a particular type of data. In HR and recruiting, the choice and definition of fields are critical for capturing relevant information and ensuring data quality. Properly defined fields facilitate accurate data entry, enable precise filtering and sorting for candidate searches, and are essential for mapping data correctly when integrating different systems through automation platforms, preventing mismatches and ensuring data integrity across your tech stack.
Record (Row)
A record, also known as a row or tuple, represents a single, complete entry within a database table. It contains all the specific data for each field pertaining to a particular item. For instance, in a “Candidates” table, one record would contain all the information (first name, last name, email, skills, application date) for a single candidate. For HR and recruiting, each record is essentially a unique candidate profile, employee file, or job application. Managing records efficiently is crucial for individual candidate tracking, personalized communication, and ensuring that all data related to a specific person or event is aggregated and accessible. Automation workflows often operate at the record level, pushing or pulling specific candidate details between systems.
Primary Key
A primary key is a special relational database column (or combination of columns) that uniquely identifies each record in a table. It’s like a social security number for each data entry within its table, ensuring that no two records are identical and that each can be referenced distinctly. For HR and recruiting databases, a primary key might be an auto-generated candidate ID, an employee ID, or an application ID. Its uniqueness is fundamental for maintaining data integrity, establishing reliable relationships between different tables (e.g., linking a candidate’s record to their applications), and ensuring that automation processes can accurately target and update specific records without ambiguity or data corruption.
Foreign Key
A foreign key is a column or a set of columns in one relational database table that refers to the primary key in another table. It acts as a link, establishing a relationship between two tables. For example, in an HR database, a “Job Applications” table might have a foreign key called “CandidateID” that links back to the “CandidateID” primary key in the “Candidates” table. This allows recruiters to easily see all applications submitted by a specific candidate. Foreign keys are essential for building robust, interconnected HR and recruiting systems, enabling complex data queries, ensuring referential integrity, and facilitating comprehensive reporting across related data sets, making automation workflows that span multiple data entities highly reliable.
Data Backup
Data backup is the process of creating copies of data so that these copies can be used to restore the original data after a data loss event. In the context of HR and recruiting, where sensitive personal information and critical operational data are stored, regular and reliable data backup is not just good practice—it’s essential for business continuity and compliance. Losing candidate pipelines, employee records, or historical hiring data can be catastrophic. Automation can play a key role here, scheduling automatic backups of your CRM, ATS, and HRIS systems to secure cloud storage, ensuring that even if your primary system fails, your vital data can be recovered swiftly, minimizing downtime and protecting your organization from significant operational and reputational damage.
Data Recovery
Data recovery is the process of salvaging inaccessible, lost, corrupted, or formatted data from secondary storage, removable media, or files when the data cannot be accessed in a normal way. This typically involves restoring data from a backup. For HR and recruiting, the ability to perform efficient data recovery is paramount. Whether due to system crashes, accidental deletion, cyber-attacks, or human error, the loss of candidate profiles, offer letters, or employee performance data can lead to legal issues, compliance breaches, and significant operational setbacks. A well-defined data recovery plan, often leveraging automated backup solutions, ensures that your critical HR and recruiting data can be restored quickly and accurately, safeguarding your operations and reputation.
Data Integrity
Data integrity refers to the overall completeness, accuracy, and consistency of data throughout its entire lifecycle. It means that data remains uncorrupted and unchanged from its initial creation until it is no longer needed. In HR and recruiting, data integrity is crucial for making informed decisions, ensuring legal compliance, and maintaining trust. Inaccurate candidate contact information, inconsistent job application statuses, or corrupted employee records can lead to inefficient processes, poor candidate experiences, and compliance risks. Implementing robust database schemas, validation rules, and automated data synchronization processes are key to preserving data integrity, ensuring that all data used for hiring, onboarding, and talent management is reliable and trustworthy.
API (Application Programming Interface)
An API, or Application Programming Interface, is a set of rules and protocols that allows different software applications to communicate with each other. It defines the methods and data formats that applications can use to request and exchange information. For HR and recruiting, APIs are the backbone of modern tech stacks and automation. They enable your ATS to integrate with job boards, your CRM to connect with communication tools, or your HRIS to sync with payroll systems. Through APIs, platforms like Make.com can orchestrate complex workflows, pulling data from one system (e.g., new candidate details from a form), transforming it, and pushing it into another (e.g., creating a record in your CRM), unlocking significant efficiency gains and eliminating manual data entry.
If you would like to read more, we recommend this article: Selective Field Restore in Keap: Essential Data Protection for HR & Recruiting with CRM-Backup
