Structured data defined

Structured data, also referred to as quantitative data, is data that follows a predefined structure or model. Because structured data is highly organized, it is easily processed by machine-learning algorithms and humans. Structured data is stored in databases and data warehouses.

Examples of structured data include metrics, dates, names, zip codes, and credit card numbers. This type of data fits neatly into spreadsheets or relational databases like SQL, MySQL, and PostgreSQL, providing businesses with information that can easily be accessed and interpreted.

Companies can use structured data to interpret their customers’ behaviors with data points like their names, purchase histories, and geolocation. This enables customer relationship management (CRM), in which businesses manage customer relationships with relational databases that can analyze customer behavior.

Types of structured data

Think of structured data as numbers and values. It is quantitative data and exists in the form of Excel files, web form results, reservation systems, and SQL databases. Additional types of structured data include point-of-sale data, product directories, and financial transactions. Structured data can be used in several contexts and industries, including:

  • Financial services: Structured data is used by banks, accountants, and financial bodies to record, process, manage, and analyze financial data such as transactions, account numbers, and names of account holders.
  • Travel industry: Booking sites, hotels, airlines, and other transportation companies use structured data that includes client and passenger data, hotel or flight prices, bus, train, or flight itineraries, and transactions.
  • Healthcare: The healthcare industry uses structured data for patient records, insurance records, and medical equipment inventory.
  • Retail and ecommerce: Structured data is used in retail and ecommerce to record and store product inventory, prices, transactions, and user account information.
  • Public sector: Governments use structured data in many ways. One way is through census data, to collect information about the population at one specific time. This structured data consists of things like geolocation, sex, race, and number of members of a household.

What is the difference between structured, semi-structured, and unstructured data?

Structured data is quantitative, consists of values and numbers, and is highly organized data that is easy to access and interpret. Examples of structured data include dates, times, and customer IDs.

Unstructured data is qualitative data that does not have an internal structure, consists of text, video, and images, and requires dedicated tools to manage and interpret it. Examples of unstructured data include customer reviews, video or satellite surveillance data, and product photos or demonstration videos.

Semi-structured data is between structured and unstructured data. It doesn’t have a predetermined structure like structured data but is more easily managed and interpreted than unstructured data. Semi-structured data uses metadata to define data points, which enables more organized and standard storage of said data. Examples of semi-structured data include JSON, XML, web, and zipped files.

How to manage structured data

Structured data is managed by using a relational database, such as an Excel sheet or a structured query language (SQL) database. A relational database is based on the relational model, which represents data in tabular form. It enables businesses to establish relationships between various data points, and to input, search, and manipulate structured data.

Structured data is schema-on-write, so before it can be placed in a database, it must be structured into a data model. The data model is established by defining a schema based on the data. This produces tables or entities. Next, you establish the relationship between these entities. Finally, you write the SQL script to produce the relational database that stores your structured data.

From there, it can be accessed and manipulated to suit your needs. In order to ingest the data of a restaurant menu item, we first create the different tables:

  • The items
  • The ingredients
  • The nutritional values

Then, we establish the relationships between the data points. And finally, we write the SQL script. Structured data can be sourced from online forms, network logs, sensor data, and points-of-sale. Once it has been stored, it can be used in the algorithms that drive machine learning (ML) to search and analyze data and generate reports and projections.

Manage your structured data with Elastic

Benefits of structured data

Structured data has several benefits since it is easy to use, store, scale, and analyze by people and machines alike.

Structured data is easily used
Structured data is highly organized, which enables easy manipulation and querying by machine learning technology.

For business users, structured data is easy to use because it does not require vast data science knowledge. Users can access the data and analyze it if they have an understanding of the topic the data is related to.

Additionally, a multitude of tools are available to analyze and interpret structured data. This is in part because structured data predates unstructured data, and because it provides more accurate outcomes.

Structured data is easily stored
Structured data can be stored in relational databases, NoSQL databases, data warehouses, data lakes, in-memory databases, and more, and takes up less space than unstructured data. As a result, structured data storage is efficient.

Structured data is easily scalable
Because structured data can be stored in data warehouses, it is easily scalable. Data warehouses serve as a repository for all the structured data produced by a business or enterprise. As the volume of structured data increases, businesses can easily add storage space and processing power.

Structured data simplifies data mining
Structured data is the foundation of big data analytics. As quantitative data, it lends itself more easily to forecasts, predictions, and studies. Structured data enables easy querying and report generation because it can be stored in relational databases. Machine learning algorithms have an easier time crawling the data. As a result, structured data also produces better, more accurate business intelligence, due to its structured nature.

Structured data can improve your discoverability
You can use structured data in your website code through schema markup to create rich snippets, or rich results, which are proven to improve customer interaction. By adding structured data to their site pages, businesses can increase click-through rates, conversion rates, and organic traffic.

Limitations of structured data

Though structured data has many advantages for businesses, some of its benefits can also present limitations.

Structured data can have limited use
Structured data's predefined structure is both a benefit and a limitation because structured data can only be used for its intended purpose.

Structured data can be low quality
Data quality can decrease when there is missing or incomplete data. Data that does not neatly fit into the schema can also negatively affect the data quality. This leads to inaccurate search results or reports if unaddressed.

As companies grow, so too does their data footprint, which is often synonymous with data duplicates or data that is no longer relevant. This diminishes the overall quality of an enterprise's structured data.

Best practices managing structured data

To make the most of your structured data, consider applying these best practices.

Take a future-proof approach to data management
You should build your file naming and cataloging conventions with future and long-term access in mind. Make sure your file names are descriptive and standard so they are easy to find.

Record data lineage with metadata
Metadata describes your data's content, structure, authors, and permissions. Carefully recording your metadata allows your site to be discoverable, enables you to track data from origin to destination, map data relationships, and ultimately build an effective data governance system.

Secure your structured data
Structured data can often be extremely sensitive information: credit card numbers, account numbers, medical information, and so on. Securing your structured data is a crucial step in managing it. Securing structured data includes backing up your data, and considering a storage plan that provides security and observability tools that mitigate cybersecurity threats.

Pick the storage plan for your needs
While keeping a future-proof approach and considering the importance of securing your data from breaches, choose a storage plan that suits your business' size and requirements. If you're a small business, your data footprint is smaller than that of a larger business. A plan intended for a large enterprise will likely not suit your needs.

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Managing and processing structured data with Elastic

The Elastic Stack is a search platform that enables you to search, analyze, and visualize data taken from any source and in any format. The Elastic Stack is comprised of Elasticsearch, Kibana, Beats, and Logstash, which together, empower you to better manage and process your structured and unstructured data.