The Fundamentals of Data Structuring

Illustration of a data structure type with multiple data nodes. Introduction to fundamentals of data structure blog
Illustration of a data structure type with multiple data nodes. Introduction to fundamentals of data structure blog

Data quality matters in every organization's data analytics. But quality isn’t just limited to what the data can tell you. It also extends to its structure because, without a good data structure, you could fail to get the full value from your data.

In simple terms, data structuring is another word for organization. Think about how you organize your own home – you hang your keys in the same location when you come home from work. You have your plates and forks and knives stored in the same place each day – a place that makes sense for those items. Your sheets are on top of the bed, not underneath it. Data structuring works in a similar way. You organize your data in a way that makes sense and is predictable and repeatable.

With this idea in mind, let’s explore the question “What are data structures?”, as well as various types of data structure and how to do it like a pro!

Table of Contents:

What Are Data Structures?

Looking at a data structure.

A data structure is a system for organizing or structuring data. Data structures work to collect various types of data (both structured and unstructured), then convert it into usable, meaningful information. The goal is to organize data so that you can do something with it.

There are many types of data structures, ranging from simple to complex. When you take the time to structure your data, you end up with reliable, actionable insights you can use to empower business strategies.

Why Is Data Structuring Important?

Structuring data.

When you’re looking at a single contact form or a paragraph-long social media comment, structuring data isn’t a priority. But over time, the data you create and collect grows – quickly.

Companies aren’t just dealing with forms and paragraphs. They’re reviewing millions of transactions, customers, social media interactions, marketing campaigns, and tons of other activities. It’s simply impossible to find the exact needle in the haystack when you have this much data to manually sort through.

That’s where good data structuring comes in. Data structures systematize the way you input, process, retrieve, and maintain information. It enables you to do something with the data you have.

What’s more, it enables you to take action on your data with greater speed and in an efficient manner. You can self-surface insights without a background in data science.

Once you find what you’re looking for, you can start answering questions and deriving insights.

What Are the Types of Data Structure?

There are several ways that companies can turn their Big Data into a more organized format. Each of the below basic data structure types has a time and a place. Organizations should know the use cases for each one to achieve the desired outcome.

Data structure is typically classified into two main buckets: linear data structure and non-linear data structure. Linear data means the information is sequential. Non-linear data means the data types are not dependent on a sequence — rather the data is hierarchical, often tree or graph-based.

Some examples of these data structures are:

Arrays

An array is a common type of data structure. The array relies on a fixed-length list of associative data items or objects. You can determine the order of each value or object based on mathematical formulas. An example of this is if you are ranking a list of runners based on their race times or a list of students based on their birthdays. Arrays are static linear data types.

Trees

hierarchical data

A tree is a non-linear data type relying on hierarchical data, where information is stored in data nodes. The first data node is the tree “root.” This node may branch off into one or more child data nodes. Think of it like a family tree, where you have a principal piece of data at the top (in a family tree, this would be the grandparents), which is broken down into smaller pieces of data that relate back to the “root” data (such as children, grandchildren, cousins, etc.).

Another example is a binary tree, where each record is linked to two successor records. Each parent node has two children nodes at most. These are usually called the left child and the right child.

Data trees are commonly used when data types have a natural hierarchy, such as an organizational chart. The data tree format is also a critical piece in creating more advanced data structures.  

Queues

Similar to a line (or queue), Queues use a first-in, first-out order. The first person or data object in the line is also the first one to leave.

An example of this is a call queue, where calls are handled in the order in which they are received. Or, it might be a shared printer that prints documents based on when users hit the Print button on their own computers. Queues are a linear data type.

Stacks

The Stacks structure is also a linear data type, but going the opposite direction of a queue data structure. It uses a last-in, first-out order.

For example, if you are creating a graphic in an editing program, the Ctrl + Z function will undo your last move. Or, you might click the Back button in your web browser to go back to the previous web page.

What Are Examples of Data Structures?

As we explained above, data can be structured in many different ways. It depends on how you want the information displayed, or how it's required to be structured for a particular software or desired outcome.

Here's a summary of data structure examples:

  • Ranked list: arranging data in a linear way, such as people by birthday, movies by run-time, or cities by population size.
  • Charts & Graphs: displaying data that is non-linear, and therefore not tied to a specific number that dictates the specific datapoints position. Examples include genealogy maps or org charts.
  • Process progression or degression: the queue or stacks type of data structure indicates a grouping of items that need to happen sequentially, such as a printer queue. Alternatively, it could involve a function that precedes an action like hitting an "undo" keyboard code.

Unstructured Data vs. Structured Data

Structuring data.

While there are linear and non-linear data structure types, structured and unstructured are other definitions you may also hear when it comes to data types. Structured data is a very common use case for SEOs — when designed well, it helps your content appear in search engine results as cards or snippets that help encourage clicks to your site!

  1. Structured data:
    Structured data refers to data that’s already been organized. It falls under pre-defined categories or fields and is highly specific. For example, if you’re using a contact form on your website and have specific fields for names, phone numbers, and email addresses, those elements would be considered structured data. Users are able to search for those specific items via database queries without extensive knowledge about data. They’re in usable formats and are typically stored in data warehouses.
  2. Unstructured data
    Unstructured data is exactly what it sounds like – Big Data that doesn’t follow any pre-defined format. This type of data requires some data science experience to understand and use. It typically lives in data lakes, where you have to fish for insights and understanding.

Think of structured and unstructured data as a book. A book that resembles structured data would have a cover, a title page, a table of contents, and so on. This is a fairly consistent format among all books. Users can go to the table of contents and see the book broken down by chapters. It has a clear beginning, middle, and end.

If the book were unstructured, we might see all of the same words that are in the structured book but not in any sensical order. The words might not even make full sentences. Chapters might be out of chronological order.

Structured data is often the byproduct of unstructured data and hard work. Data scientists review the unstructured data, then find ways to organize it and make it more useful to others.

How to Structure Data: The Basics

Basics of data structure.

Now that you know the answers to questions like “What is data structure?” and “Why does data structure matter?”, let’s take a high-level look at how to structure data in your organization.

Choose which data to structure and how it should look

The most fundamental step of structuring your data is to choose what data you’d like to structure and how it should look. Structuring data is all about standardizing the way data is collected and accessed by the user. Having an idea of what you want to do with your data can inform the rest of the data structuring process, as well as the software you use.

Write an algorithm to process the data

An algorithm is responsible for analyzing, classifying, and organizing data. Machine learning algorithms try to match data to known data types based on the format and nature of the data. They pull data from disparate sources into a single, organized system.

Algorithms are usually written based on the unique requirements of the organization and use case. They automate the process of data classification, in whole or in part. This helps to save time when working with large volumes of data and eliminates some of the need for deeper human expertise.

Store your structured data

Besides choosing a data structure type, you’ll also need a place where you can standardize data into a structured form. This might be a database, such as a relational database or SQL database. Both have earned their spot as a backbone in common data structures.

For years, SQL databases have been the gold standard for data structuring. It works with a range of programming languages and supports many data formats. When you’re just starting to learn data structures and options, SQL is a popular choice. This works well for users querying multiple questions of the same set of data. SQL is scalable and works across multiple systems and data sources.

A schema-less database model can also help a business to scale, a critical factor in the era of Big Data. These are known as NoSQL databases, where data nodes can be added quickly. The infrastructure is highly flexible in terms of modeling the data it collects.

No matter how you decide to structure your data, there’s no substitute for intuitive software that helps you get results. Meltwater Display takes the guesswork out of structuring your brand’s online data. From media mentions to social media and customer service, get a 360-degree snapshot of your brand in a centralized source. Get a demo to learn more!

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