April 23, 2021
There are four main data structures: structured data, unstructured data, semi-structured data, and metadata. Structured data is information that is formatted and organized for readability within relational databases. On the other hand, unstructured data’s format is undefined, not well-organized, and not usable by relational databases. This article will explore structured vs. unstructured data, semi-structured data, how to convert unstructured data, and AI’s impact on data management solutions.
Structured data is defined as highly organized data formatted to integrate with relational databases easily. As previously mentioned, structured data is typically collected from spreadsheets and DBMS systems and is primarily, but not always, quantitative in nature. Because structured data complies with relational databases, it also easily integrates with AI technology, specifically machine learning.
While structured data is widely regarded as the most sought after and user-friendly data type, structured data is the least common data structure type. This is because structured data is collected from spreadsheets, database management systems (DBMS), etc. If we were to think about where most of our data is collected, we would notice that public web sources and other more informal collection methods are the most common, even in the world of finance. However, while structured data accounts for only about 20% of total global data, it is still extremely valuable, particularly to companies and investors. Let’s take a closer look at structured data.
The management of structured data was created by IBM in the 1970s. IBM needed to develop a language that defined how each data point within a larger database relates to one another, creating the Structured Query Language, known more widely as SQL. This language helped companies replace outdated paper-based business intelligence processes with digital processes. This digitalization has since enhanced data analysis and improved business operations by reducing costs and improving efficiency, among other benefits. Today we are seeing companies leverage the digitalization of such structured data for building AI-based tools and data-driven decision making.
Structured data is well-organized and formatted, making it easy to find in relational databases. Unstructured data is difficult to capture, process, and evaluate since it lacks a predefined format or organization.
- Jason Mitchell, CTO of Smart Billions
Unlike structured data, unstructured data is defined as data that is not organized or formatted in a predefined way. Typically qualitative in nature, unstructured data includes a variety of data types such as text, numbers, booleans, and enumerations. Because unstructured data is collected from a variety of sources, raw unstructured data in its unfiltered form is disorganized and complex. Despite its complexity, unstructured data, when integrated properly, provides companies with high-quality business insights. This then raises the question, if unstructured data is so complicated, why is it so sought after? The answer is simple unstructured data accounts for approximately 80% of data worldwide.
More specifically, unstructured data is sourced from a variety of sources, including social media, documents, PDFs, audio files, video files, and more. The variety of source types and loose formatting definitions require unstructured data to be processed and transformed for further analysis or integration.
As previously mentioned, unstructured data accounts for the majority of data while also being the most complex, unorganized, and typically largest in size. However, because it is so valuable, data scientists have been looking for unstructured data management and storage solutions. Today, AI technology, automation, and cloud technology have been the key to such solutions. More specifically, unstructured data is stored using servers and cloud-based technology and is managed with natural language processing and text mining.
Semi-structured data is a combination of structured and unstructured data. Semi-structured data has some organization and formatting but not enough to integrate into relational databases smoothly. Semi-structured data’s organization is minimal and is organized with tags, attributes, and other semantic markers. However, the resulting organization does not meet the standard of relational databases but can be altered in a way that can fit into easy-to-read tables and spreadsheets, making it a subset of unstructured data.
Similar to how unstructured data is managed and stored, semi-structured data is managed using servers, cloud-based technology, natural language processing, and text mining. Ultimately, as we create more and more unstructured and semi-structured data, we will begin to see new trends in data management solutions.
Let’s take a closer look at the key differences and similarities between structured, unstructured, and semi-structured data.
|Properties||Structured data||Unstructured data||Semi-structured data|
|Data types||Defined, relational||Undefined, non-relational||Semi-defined, tagged, semi-relational|
|Uses||Machine learning||Natural language processing and text mining||Natural language processing and text mining|
|Source location||Sourced from online relational and tabular forms||Sourced from videos, emails, documents, social media, etc.||Sourced from web documents, JSON, and XML files|
|Storage location||Stored in data warehouses||Stored in data lakes||Stored in data warehouses and data lakes|
|Flexibility||Not flexible||Flexible||Somewhat flexible|
|Storage size||Requires less storage||Requires a lot of storage||Requires a medium amount of storage|
|Examples||SQL||JPEG, DOC, PDFs, MOV, etc||JSON, XML, emails|
As our global data sphere continues to grow, businesses will look for solutions to unlocking the full potential of all data structures. Currently, data scientists can extract insights from unstructured and semi-structured data using a conversion process. Let’s take a quick look at this process.
Before you begin the conversion process, you must identify and analyze what data you want to convert. This will require that you access a data lake, where raw unstructured data is stored, and decide what datasets to pull from.
This step can be done in tandem with the first step. Setting clear goals and objectives about what you want to extract from your data and how you might use and access it in the future will provide guidance for the rest of the process.
Once there are clear objectives, and your data sources have been analyzed, you can select which processing tools will work best. These processing tools include text mining, data extraction tools, and natural language processing tools.
Now that you have chosen the processing tool, you must clean and formulate the data according to the chosen tool’s guidelines. This might involve deleting extraneous symbols, whitespaces, and duplicate data. This step will help your processing tools understand the basic organization and entities within your data.
The final step involves running your data through the selected processing tool: text mining or NLP. Text mining involves sifting through textual data for significant words and phrases and extracting key features of each document or data file. NLP involves utilizing AI to sift through and decipher the natural languages (human languages) within textual data.
Our global data footprint is expanding at an exponential rate. According to a study by Seagate, the global data sphere is expected to reach 175 zettabytes by 2025, a massive growth from 45 zettabytes reported in 2019. As our data footprint grows, companies are finding more ways to leverage this data for crucial business practices such as lead generation, market analysis, business and investment intelligence, just to name a few.
Likewise, as our data grows, the challenges surrounding the usability and hidden potential of data have become apparent. This is primarily due to the varying structures of data that are created and consequently collected. Ultimately, data scientists have turned to AI and machine learning for these solutions.
Further, artificial intelligence utilizes both structured data and unstructured data. More specifically, structured data utilizes machine learning, while unstructured data utilizes text mining and natural language processing (NLP), all of which are AI-based processes. Scientists have created AI-based tools that can extract valuable business insights from nearly all data structures. For example, companies can purchase semi-structured data from data providers, prepare said data for analysis or enrich their current datasets, and then extract valuable insights from the data with AI, analysis, or other automated data processing tools.
Structured data is utilized in machine learning and drives machine learning algorithms. Unstructured data is used in text mining and natural language processing.
Veronica Miller, Cybersecurity Expert at VPN overview
In all, understanding the various data structures is beneficial in informing companies about the importance of data management. In order to unlock the full potential of data, companies must maximize their data management and storage processes of both structured and unstructured data. As our global data sphere continues to grow at exponential rates, data scientists and companies will continue to develop management and storage for all types of data structures.
Structured data, unstructured data, and semi-structured data are the three structure types.
A CSV file is semi-structured. In their raw form, CSV files are text files, meaning they lack relational and hierarchical capabilities. However, CSV files do contain tags and entities that help maintain some organization, making them semi-structured.
Email is considered semi-structured data because it contains some organization; however, the body of the email text is unstructured.
HTML is considered semi-structured data because it contains tags and lightweight hierarchies, but its organization varies from document to document.
XML is considered semi-structured data. The markup qualities of XML contain tags and predefined values; however, the content within XML varies from document to document.