sample data object: cassett

What is a data object? Definition, Types, & Examples

If you’ve ever tried to understand data objects from a textbook or formal documentation, you probably ended up confused. Data object are a simple concept, but their meaning usually gets lost in dense wording.

The purpose of this article to to clearly define data objects, explain their various types, and provide examples so you walk away with clean fundamentals on the topic. So let’s start with the basics: what is a data object?

Data Object Definition & Description

A data object is a collection of one or more data points that create meaning as a whole. In other words, “data object” is an alternate way of saying “this group of data should be thought of as standalone.”

The most common example of a data object is a data table, but others include arrays, pointers, records, files, sets, and scalar types.

Values in a data object may have their own unique IDs, data types, and attributes. In this way, data objects vary across database structures and different programming languages.

An easy way to think about the term “data object” is that it reflects the simple need for compartmentalizing information in a database that otherwise looks big and confusing.

In addition to compartmentalizing, data analysts use unique IDs, data types, and attributes to make data even easier to understand. These data objects are almost always represented in data models, which show relationships between data objects.

As a list, data objects consist of:

  • Values. The data itself.
  • Unique IDs. One data point that identifies others related to it.
  • Attributes. Additional data within one Unique ID.
  • Data types. Classifications of data such as text, numeric, and boolean.

NOTE: a subtlety and major source of confusion around data objects concerns “data types.” In some programming languages, “data object” signifies a single value of one “data type” or another language-dependent definition. Analysts depend on context to tell the difference between data objects as tables and other types.

Example of Data Objects in Databases

Imagine you run an e-commerce company that sells watches to retailers. Your company is called Batch Watch, and its key units are vendors, products, and customers. Over time, you’ve built a complex database with a large amount of data.

As a raw database, your data looks like an enormous table. It’s nearly impossible to conceptualize or extract insight from the data in that format. Instead, you hire an external consultant to build a data model that helps you understand. What’s in your data model? You guessed it: data objects.

These data objects are subsets of your huge database. For example, while the huge database contains vendor, product, and customer data, a data object might only contain vendor data. Another contains product data. And still another, customer data.

As you might imagine, these data objects can still contain an inconceivable amount of data. That’s why data objects actually have 2 “views,” or structures: the underlying data table and a box-style view. Let’s explore these briefly:

  1. Underlying data tables. These are data tables made of columns and rows. Here’s a very simple example of a vendor data table with a unique ID and 3 attributes:
Example data table view of a data object

While our example is here is simplified, the data object can still be quite cumbersome to understand. Who can put layers or rows and columns into context easily? In my experience, not many. That’s why there’s the second view: the box-style data object view.

  1. Box-style view. The box style view displays only the column headers in a data table to summarize the object. Here’s a box-style view of the data table above that uses metadata as a summary:
Example box view of data object

The <<PK>> reference indicates “primary key,” which in another term for unique ID. With the box-style view of the data object, it’s easy to understand what’s inside the data object at a glance. This quick-information view is data object’s added value.

Make Sure You Know Your Primary Keys

Experienced and novice analysts alike fall prey to skipping-over primary keys. When you’re working fast, it’s easy to assume you know a primary key based on values in the data object. When you’re wrong, it can create confusion, so make sure you check yourself for each object.

Don’t forget, you can access the 67 data skills and concepts checklist for free to ensure you’ve got your bases covered.

Types of Data Objects in Databases

We’ve talked exclusively about data tables up to this point, but they’re not the only type of data object. Here’s a list of additional data objects that analysts encounter while working in databases:

  • Arrays
  • Pointers
  • Records
  • Files
  • Sets
  • Scalar types


Arrays are data objects that have only one dimension. You can think of them as a single column in a data table. Analysts use arrays when their data is not thorough enough to merit a table.


Records are data objects that contain one data entry for each dimension in the data table. You can think of data records as a single row in a data table. Analysts use records any time they enter an observation, which usually have values for each dimension in the table.


A data object pointer is a special data value that indicates the memory location of another data point or group of data points. In most cases, a pointer is included within a table column as a separate dimension.


Data object files use code to ensure other data objects use the right structure. While they’re not executable like the other data objects, data object files are invaluable. They ensure data integrity by ensuring data entries take on the same format. A easy way to conceptualize files is that unlike other data objects, you can not view them in simple database software like Excel.


Data object sets combine multiple data objects, and in most cases, tables. In this way, data sets occupy a level of hierarchy between data tables and databases. In common speak, analysts use the shortened term “data set” to refer to data object sets. In addition, this term has become a default term for all data objects.


A scalar data object indicates a single value rather than an aggregation like data tables, arrays, and records. That said, the term “scalar data” takes on different meanings depending on the database management system or programming language.

NOTE: In additional to these data object types, another layer of “data object type” exists for programming languages. Programming data object types are more complex and nuanced, and often depend on “data types.” Let’s look at them now.

Visual Representation of Data Object Types and Their Relationships

Here’s a diagram to help you understand the different types of data objects:

Diagram of data object types and their relationships

Data objects in programming languages: the nuance of language-dependent definitions

A point of considerable confusion on the subject of data objects is the use of “data object” and “data type” as synonyms. In formal documentation on programming languages, you will often see data objects defined either as a data type or by a data type.

If you aren’t familiar, data types are categories of data based on their essential characteristics. Data types include “text,” “integer,” “data,” “time,” and “boolean.” (For more details on data types, check out this article.)

For example, in C++ a data object is a memory space within the program that only has one type of data.

In addition, some programming languages have unique definitions for their own internal data objects, as we’ll see below.

Not all programming languages contain nuances on data objects. Programming languages that do focus on data objects are called object oriented programs (OOPs). They stand in opposition to function and logic oriented programs. To provide clarity, let’s look at OOPs:

  • Java. Uses a traditional class method that provides code-driven templates governing the data type and the function of each type of data object.
  • JavaScript. Data objects can be variables (scalar values) or more complex functions. Note: functions do not exist as data objects in databases, only in the language itself.
  • Python. Data objects contain an identity, a type, and a value. Unlike database objects, the “value” in python languages can be a complex function or action.
  • C++. C++ data objects only have one type of data.
  • Visual Basic. Visual Basic is a Microsoft Excel language. Its data objects closely resemble those in databases. The data objects are the representations (in program code) of the physical database, data tables, and fields.
  • .NET. Like Java, uses a traditional class method that provides code-driven templates governing the data type and the function of each type of data object.
  • Ruby. Ruby data objects consist primarily of arrays and a special “ID to value” relationship called a “hash.”
  • Scala. Like Java and .NET, uses a traditional class method that provides code-driven templates governing the data type and the function of each type of data object.
  • PHP. Uses a language-specific extension to access database objects like the ones defined above in this article.


A data object, in most cases, is a data table or some derivative thereof. This is what we refer to in the context of data tables. However, programming languages often have their own unique definitions.

Because programming languages and databases often work in unison, the diverging definitions can be confusing. Analysts depend on context to tell the difference.

In the end, data objects are nothing more than a collection of data. When you’re a data analyst, you constantly need to compartmentalize. This provides structure and makes data easy to work with.

And how can you do so? By using data objects.

About the Author


Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.


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