Understanding Data Structures in Make: JSON, Arrays, and Objects for HR Data

In the realm of HR, data is the lifeblood of efficient operations, from recruitment and onboarding to payroll and performance management. Yet, this data often arrives in various formats and structures, posing a significant challenge for automation. This is where a deep understanding of how data is structured—specifically JSON, arrays, and objects—becomes indispensable, especially when leveraging powerful automation platforms like Make. For HR professionals looking to streamline their workflows, grasping these foundational concepts is not just a technicality; it’s a strategic advantage.

Make (formerly Integromat) excels at connecting disparate systems and transforming data as it flows between them. At its core, this transformation relies on recognizing patterns within data. The most common pattern you’ll encounter, particularly when dealing with APIs or webhooks, is JSON: JavaScript Object Notation. JSON is a lightweight data-interchange format, designed to be easily readable by humans and easily parsed by machines. It’s essentially how most modern applications “speak” to each other, making it the universal language for integrating your HR systems.

JSON: The Universal Data Language in Make

Think of JSON as the envelope that carries your HR data between different systems. While it provides the overall structure, its content is what truly matters. JSON primarily organizes data using two fundamental structures: objects and arrays. When Make receives data, perhaps from a new applicant tracking system or an employee survey tool, it’s often in a JSON format. Understanding how to navigate this format allows you to extract precisely the information you need, whether it’s an applicant’s name, their submitted resume link, or their response to a specific question.

In Make scenarios, you’ll frequently use modules like “Parse JSON” to take raw JSON text and convert it into a structured format that Make can easily manipulate. This step is crucial because it transforms a blob of text into distinct, addressable pieces of data, allowing you to map fields, filter records, or perform calculations. Without this foundational understanding, your automations would struggle to interpret the very information they are designed to process.

Objects: Key-Value Pairs for Structured HR Records

Within a JSON structure, an “object” is akin to a record or a single entity’s profile. It’s a collection of key-value pairs, enclosed within curly braces `{}`. Each “key” is a unique identifier (a string) and its “value” is the data associated with that key. For instance, an HR object might represent an individual employee:

{
  "employeeId": "EMP001",
  "firstName": "Jane",
  "lastName": "Doe",
  "email": "[email protected]",
  "department": "Marketing",
  "salary": 75000
}

Here, “employeeId”, “firstName”, “lastName”, etc., are the keys, and “EMP001”, “Jane”, “Doe”, etc., are their respective values. In Make, when you map data from one module to another, you’re essentially telling Make which “key” from the source object should populate which field in the destination system. This object-oriented approach is incredibly powerful for representing structured HR data like employee profiles, job applications, or benefit enrollments, ensuring that each piece of information is clearly labeled and easily retrievable.

Arrays: Handling Collections of HR Data

While objects represent single entities, “arrays” are used to store ordered lists or collections of items. They are enclosed within square brackets `[]`. An array can contain numbers, strings, booleans, or, most commonly in HR data, a list of objects. Imagine you’re pulling a list of all applicants for a specific job, or all employees in a particular department, or even a list of skills an applicant possesses.

[
  {
    "applicantId": "APP001",
    "name": "John Smith",
    "status": "Interview Scheduled"
  },
  {
    "applicantId": "APP002",
    "name": "Emily White",
    "status": "New Application"
  },
  {
    "applicantId": "APP003",
    "name": "David Green",
    "status": "Offer Extended"
  }
]

This is an array of applicant objects. In Make, handling arrays is vital for batch processing or iterating through multiple records. Modules like “Iterator” are designed specifically to take an array and process each item within it individually. This capability allows HR teams to automate tasks like sending personalized emails to a list of applicants, updating multiple employee records simultaneously, or aggregating data from numerous performance reviews. Without understanding arrays, the ability to process data at scale within Make would be severely limited.

The Synergy in HR Workflows

The true power lies in the combination of these structures. A single HR record might be an object, but a collection of those records is an array of objects. An employee object might contain an array of their past job experiences or a nested object detailing their emergency contacts. Make provides the tools to navigate these complex structures seamlessly. From filters that examine specific object keys to aggregators that combine data from multiple array items, and routers that direct data flow based on specific values, every module in Make implicitly or explicitly interacts with these data structures.

For HR professionals, mastering these concepts unlocks the full potential of Make. It transitions your use of the platform from simple integrations to sophisticated, resilient automation workflows. You can confidently build scenarios that parse intricate employee benefits data, synchronize applicant information across disparate systems, or automate payroll processes by intelligently structuring and manipulating data at every step. This deeper understanding ensures your automated HR workflows are not just functional, but truly optimized, clean, and scalable.

If you would like to read more, we recommend this article: The Automated Recruiter’s Edge: Clean Data Workflows with Make Filtering & Mapping