Structured Data and Unstructured Data - Examples, Benefits, and Methods of Transformation

This blog will discuss the distinctions between structured and unstructured data and strategies for organizing unstructured data to improve its usability.

What is Structured Data? Data organized into a precise format, such as a database table or a spreadsheet with rows and columns, is structured data. Because it is in a precise way, structured data is simple to search, sort, and analyze. .

Examples of Structured Data:

A spreadsheet with rows and columns of data, such as a list of customer names, addresses, and purchase histories. A set of financial transactions in an accounting system, such as a list of invoices and payment amounts. A set of weather data, such as temperature, humidity, and air pressure readings, recorded at regular intervals over some time. A set of records in a customer relationship management (CRM) system, such as a list of contacts and their information.

Structured data is easy to search, sort, and analyze because it is organized or arranged in a specific manner. It is often stored in a database or a spreadsheet program and can be queried using SQL(Structured Query Language) or a similar language.

What is Unstructured Data?

Unstructured data does not have a specific format or structure and is often more difficult to analyze and extract insights from. Therefore, it may require specialized tools and techniques to process and extract meaning from it. The tools to process unstructured data include natural language processing (NLP) algorithms, image, and video analysis algorithms, and audio analysis algorithms.

Examples of Unstructured Data: A text document, such as a novel or an essay An email message A social media post An image or a video An audio file, such as a recording of a conversation or a lecture

Difference Between Structured and Unstructured Data

Structured Data Unstructured Data Machine learning (ML) algorithms can more easily use the structured databases’ unique and organized architecture to manipulate and query ML data.

Unstructured data is stored in an indeterminate state until it is needed. Data scientists may prepare and analyze only the data they need from its versatile nature, which expands the database’s file formats and widens the data pool.

Business users can use structured data without having to deeply understand the various forms of data and how they work. Users can easily access and analyze the data if they have a fundamental comprehension of the subject matter of the data.

Requires expertise: Data Science skills are necessary to prepare and analyze unstructured data because of its ambiguous/incomplete nature. Data Analysts benefit from this, while non-specialist business users who may not fully comprehend specialized data issues or how to use their data are alienated.

Limited storage options: Data storage systems with rigid schemas are often where structured data is stored (e.g., data warehouses). Because of this, updating all structured data is necessary when data requirements change, which results in a significant drain on time and resources.

Data lake storage: Offers pay-per-use pricing and large storage, which lowers costs and facilitates scaling. .

Accessible by more tools: More tools are available to use and analyze structured data because it is older than unstructured data and hence more accessible.

Specialized tools: Specialized tools are necessary to manipulate unstructured data, restricting the range of products available to data managers.

Data having a preset structure has a more constrained range of applications because it can only be utilized for that purpose.

Adaptable use: Data collection is quick and simple because it doesn’t need to be predefined, and its application is also quite flexible.

Transforming Unstructured Data to Structured Data:

Determine the unstructured data that need transformation. A text file, email, social media post, or any other unstructured data format could qualify as this.

Choose the structure you want to use to present the data. This could be a database table, a spreadsheet with rows and columns, or some other kind of structured data format.

Take the information out of the unstructured source. Using specialized tools to extract text, photos, or other kinds of data from the unstructured source may be necessary for this.

Make the data clean and consistent. This could entail eliminating extraneous text, fixing typos, and standardizing data formats.

Data should be loaded into the preferred structured format. The data may need to be loaded into a database or imported into a spreadsheet tool.

Verify the data’s accuracy and proper transformation by validating it.

Use data analysis and visualization to uncover trends and reach judgements. Using tools like pivot tables, charts, and graphs may be necessary in this case to recognise patterns and trends in the data.

Benefits of transforming Unstructured to Structured Data

Determine the unstructured data that need transformation. A text file, email, social media post, or any other unstructured data format could qualify as this. Choose the structure you want to use to present the data. This could be a database table, a spreadsheet with rows and columns, or some other kind of structured data format. Take the information out of the unstructured source. Using specialized tools to extract text, photos, or other kinds of data from unstructured sources may be necessary for this. Make the data clean and consistent. This could entail eliminating extraneous text, fixing typos, and standardizing data formats. Data should be loaded into the preferred structured format. The data may need to be loaded into a database or imported into a spreadsheet tool. Verify the data’s accuracy and proper transformation by validating it. Use data analysis and visualization to uncover trends and reach judgments. Using tools like pivot tables, charts, and graphs may be necessary in this case to recognize patterns and trends in the data.

Benefits of Transforming Unstructured to Structured Data

Improved accuracy: Structured data is more accurate and reliable because it is organized in a specific format and follows a set of rules. This makes it easier to validate and verify the data, which can be important for decision-making and analysis. Increased efficiency: Structured data is easier to search, sort, and analyze because it is organized in a specific way. This can save time and effort when working with large datasets. Enhanced insights: Structured data is easier to analyze and extract insights from because it is organized in a specific way. This can help organizations make better decisions and improve their operations. Greater interoperability: Structured data is easier to share and integrate with other systems because it is organized in a specific way. It can help organizations work more effectively with partners and customers. Improved compliance: Structured data is easier to audit and track, which can be important for meeting regulatory requirements and ensuring compliance.

FAQ:

What is semi-structured data? Semi-structured data is data that is neither captured nor formatted conventionally. Semi-structured data, which comprises JSON, CSV, and XML, connect structured and unstructured data. It is easier to store than unstructured data, more complex than structured data, and does not have a defined data model. As a result, semi-structured data has the benefits of being more versatile and easier to scale than structured data.

An example of semi-structured versus structured data is a tab-delimited file containing customer information instead of a database with CRM tables. For instance, when contrasting semi-structured and unstructured data, a tab-delimited file is used instead of a client’s collection of Instagram comments.

Are SQL databases and excel structured data? Each of them has rows and columns that may be sorted. Structured data cannot exist without a data model, a visual depiction of how data can be processed, stored, and accessed. Consequently, they have a structured nature.

Are Facebook and Twitter unstructured data? These data are neither organized nor specified, so one needs various tools to process the information and create structured data. Since it contains text, music, video, and GIFs, most of the data that Facebook and Twitter produce daily is unstructured. As an illustration, Facebook uses AI and deep learning algorithms to structure these unstructured data sets and understand how people interact on the site.