What is structured data? How does it work?
Structured data is data that has been put into a formatted repository, most often a database. This is so the data’s components can be made addressable for more efficient processing and analysis. The data is stored in a fixed field in a record or file.
It is opposite to unstructured and semi-structured data. The three data categories lie on a continuum where unstructured data is the least formatted and structured data is the most formatted. The more structured a group of data is, the easier it is to process and analyze.
How does structured data work?
Structured data requires a data repository and a data model, often a database. A data model is the mechanism that keeps items of data together and dictates the way in which they interconnect. An illustration of a data model could state that the data element to store a customer within a database consists of some lesser elements or attributes that provide some particular information of a customer. Some instances of structured data within this example would be the customer’s name, phone number, address and ZIP code.
The data structure may be imposed. For instance, the ZIP code field can only allow numeric data that is five digits in length. This ensures the integrity of the data, yet avoids data that is not this description from being placed into the schema. The form of structured data is that it can be logically partitioned based on similar values and constraints.
Data is narrowly defined by these limitations and scripted to specified slots in a data store. For example, in a database, every field of a record is distinct, and its data can be accessed either independently or with data from other fields, in a different combination.
Databases add context to data so that it provides meaningful information. Organized data can also be stored in rows and columns in a relational database, which connects tables of data together so they can be accessed by a wider range of search parameters to provide more extensive information.
A database query language, like structured query language (SQL), allows a database administrator to interact with and manipulate data in the database. Extract, transform and load (ETL) processes are occasionally employed to merge various structured databases into a data warehouse.
H2 Advantages and disadvantages of structured data
There are numerous advantages to working with structured data, including:
Storage. Structured data is easier to organize and store because it is defined in a set of constraints beforehand, particularly compared to unstructured data.
Security. Restricted constraints make structured data easier to secure. Proper data security depends on data classification, which is easier with data fitting into an existing data structure.
Data analysis. More descriptive information to data makes it simpler to process, analyze, and make inferences from.
Easy to understand. Tight constraints enable data to be easily comprehended and utilized. Business users can read and comprehend structured data without having to know much about data formatting and databases.
Structured data also has its disadvantages, including:
Rigid schema. Data must fit into a schema that defines a specific purpose. This can restrict the use of the data. Data that does not fit into a specific schema may not be processable in a structured database.
Missing data. Since data has to fit into some data format, companies may lose access to unstructured data that can enhance decision-making.
Underrepresented. New devices create a tremendous amount of unstructured and semi-structured data. Companies need to figure out how to utilize this data, which may not neatly fit into a predefined schema. If companies are only looking to acquire and process structured data, they will not be able to take advantage of the entire scope of opportunity offered by internet of things devices and big data.
H2 Use cases for structured data
Some typical instances of how structured data is applied are:
Search engine optimization tool. To website owners, search engines provide an option to edit their website’s HTML in order to describe their page with a list of HTML tags, known as microdata. Tagging up a site using microdata tags allows search engines to comprehend the site more clearly and to list it more probably in search results. Schema.org is an organization that develops, maintains and advocates for vocabularies of microdata that can be utilized to mark up webpages. Structured data in this case is acting similar to metadata.
Training machine learning algorithms. Developers employ organized data to author and extend machine learning algorithms that employ supervised learning. In supervised learning, machines are trained on well-labeled training data; organized data is more likely to be suited to the machine’s rules.
Data management. Business intelligence tools may employ SQL databases or Excel spreadsheets to monitor simple data like customer contact details, account login credentials and financial transactions. Online analytical processing, MySQL and PostgreSQL are some of the tools employed in storing structured data.
ETL. This is a process of drawing data from source data stores, cleaning up and then transforming it, and loading it into a huge data repository like a data warehouse.
H3 Structured vs. unstructured data
There are a number of fundamental differences between structured and unstructured data. While structured data is very specific and adheres to a pre-defined data model, unstructured data does not. Unstructured data tends to be kept in its raw form. It is more abundant and flexible than structured data, but it is more difficult to manipulate and usually takes higher-level techniques and data science practices to manipulate.
Examples of unstructured data are social media posts, IoT remote sensor data, rich media like images, video and audio, and webpages. Unstructured data tends to get stored in data lakes.
When the HTML microdata function is employed as a tool for SEO, it assists in giving structure to the otherwise unstructured data of web pages.
