We live in an era where data has become the new currency. The more data you gather and analyze, the more successful you can be.

Data is a set of values that are available for reference or analysis. It is an invaluable asset for any business that wants to succeed in the modern world. Companies use data for every part of their operations: to gather information on the market, customers, and competitors and to communicate internally and externally with all relevant stakeholders.

The amount of data a company has on its operations, its customers, and influencing its decision-making can make the difference between success and failure.

Roughly 20% of commonly available data in a company is structured (clean), but the vast majority of available data remains unstructured. Both data types are helpful to a business, but we do not handle them similarly. 

So what is the difference between structured and unstructured data? And what is semi-structured data?

Let’s dive into understanding the different types of data, how they can be used, and discuss potential solutions for making the most out of all the available data.

In this article, you will learn:

  • The main difference between structured and unstructured data
  • How to structure unstructured data using AI / ML /NLP models and maximize its utility
  • Adaptive natural language processing models
  • How to classify unstructured data using a no-code NLP solution

Let’s start!

Differences Between Structured, Unstructured, and Semi-structured Data

The main difference between structured and unstructured data is that structured data is highly organized and can be quickly processed by computers.

Structured data comes in a predefined data model. It is organized and fits into templates and spreadsheets, making it easy to analyze.

On the other hand, unstructured data is unorganized and difficult to process. 

Unstructured data comes in different forms. It can be text, videos, audio, and images, making it hard to analyze and use.

Then there is semi-structured data that has structured and unstructured data elements. Semi-structured data does not have a fixed schema or data model like structured data but is not entirely unorganized like unstructured data. Instead, the information is loosely organized with self-describing tags.

With everything going online and everyone on the internet, the amount of semi-structured data like web pages and email messages is growing exponentially.

Structured Versus Unstructured Data

What is Structured Data

Structured data is a standardized format for representing data organized into tables – columns and rows- making it easy to analyze manually or using data analytics tools. 

Structured data is a type of data that is organized in a specific way. It doesn’t have to reside in relational databases necessarily. However, historically, we store structured data in a relational database (RDBMS). 

It can consist of numbers and text, and sourcing can happen automatically or manually, as long as it’s within an RDBMS structure. BUT! We can also store it in a spreadsheet, JSON, or another structured data format. 

In terms of storage space, less storage is required for structured data than for unstructured data because it is organized and easily retrieved. 

The information within structured data is formatted and inputted into a set template with a specific design that upholds a particular structure.

It resides in relational databases or data warehouses and is easily recognizable by data analytic tools.

The content is standard. It is easily processable for computers.

Types of Structured Data

Examples of structured data include

  • Boolean data types
  • CHAR
  • TEXT
  • INT
  • etc.  

Structured Data Examples

There are many examples of structures. A typical example is data organized in a tabular format.For example, customer data in a spreadsheet is structured data.

Advantages of Structured Data

The main advantages of structured data include:

  • Easily understood by machine learning algorithms- The organized and detailed format of structured data makes it easy to manipulate and pull specific data.
  • Easily understood and used by business users- Structured data can be understood and used by an average business user who has an understand of the related data topic. Business users can search through structured data sets and analyze the data manually, without the need for analytic tools or technical-heavy labor.
  • Offers more accessibility to tools- Structured data was once the only form of data available and has been used longer by business users, data analysts, computer scientists, and business leaders. Therefore, more tools are available to test, use, and analyze structured data.

Disadvantages of Structured Data

Although structured data has its advantages, it also has its cons: 

  • Increased complexity. When data is structured, it can be more challenging to understand and work with. This complexity arises because there is much more information to process, and it can be harder to see the overall picture.
  • Reduced flexibility – Structured data is often less flexible than unstructured data. This reduced flexibility is because it is more challenging to change structured data format, and it may be less compatible with other systems.  
  • Increased costs – Structuring data can be costly in terms of time and money. This is because it requires specialized software and hardware, and there may be a need for training the staff.

What is Unstructured Data

Unstructured data is data that is not stored in a predefined format. This data type is usually not organized and can be challenging to process.

Although unstructured data makes up more than 80% of digital data, it is often complicated and time-consuming to search and analyze. The potential of unstructured data is a rarely tapped due to its complexity.

Unstructured data is more challenging to store and retrieve because it is not categorized. Generally, there are larger volumes of unstructured data, which is why it uses significantly more storage. 

However, once analyzed, the information can provide invaluable insights. Utilizing the potential of unstructured data can be imperative to a business’s success and competitiveness in the market.

Types of Unstructured Data

Unstructured data comes with no limitations in type, and you can find it in various formats such as:

  • Images
  • Video files
  • Text
  • Audio file formats
  • Emails

Examples of Unstructured Data

There are various examples of unstructured data assets in business usage. Below are some typical examples:

  • Medical records: In health care, machines and humans generate a massive amount of unstructured data.
  • Survey Responses: Survey responses do not only come in a clear-cut format. The free-form answers are indeed unstructured and hard to get the meaning of. 
  • Webpages: Web Pages contain images, videos, and text. To analyze web pages, data analysts need to scrape the data and find a meaning to every piece of the code.

Another good representation of an unstructured data source is email. Emails are unstructured because the information embedded in the main body of the email is free-form text, which contains interesting information, like the topic of the conversation, the writer’s mood, etc.

Advantages of Unstructured Data

The main benefits of unstructured data include: 

  • More flexibility– The undefined unstructured data format comes in various forms, such as images, audio transcripts, videos, web content, etc. You can also use it for different purposes, such as classifying images, text, and sound, inputting that information  into predictive models for sentiment analysis, entity and theme classification, and other text analytics. 
  • Offers more insights– Although the information is harder to detect and analyze, unstructured data can provide otherwise unidentifiable insights into a company’s customer and competitor base. 
  • Quickly accumulated: Unstructured data requires no predefined format, which makes it easy to collect.

Disadvantages of Unstructured Data

The main disadvantages of unstructured data include:

  • Time-consuming and expensive– Unstructured data can take a long time to process. It can also be costly to convert it into useful, practical information, as you will need AI and data scientists to structure it.
  • Difficult to analyze– Business users and data analytics tools will not be able to access unstructured data as it is text-heavy or stored in non-recognizable formats. Data analytics specialists are needed to identify, extract, and process relevant information from the data.
  • Requires specific tools– Due to the complexity of unstructured data, most data tools cannot ingest it. Specialized Data Analytics tools will be needed to manipulate and pull insights from the unstructured content. 
  • Hard to store- Due to its enormous size, we generally store unstructured data in data lakes. Data lakes are storage spaces with huge storage capacity.

Unstructured data includes a broader range of information and can provide more inputs, which can be imperative for a business. This surge of information gives a competitive advantage to companies when used well.

Due to the increased amount of unstructured data, businesses are looking to tools that can efficiently extract information from unstructured data.

What is Semi-Structured Data 

Semi-structured data combines unstructured and structured data because it contains elements of both. Semi-structured data is not as rigidly formatted as structured data but is not as unorganized as unstructured data.

Semi-structured data does not follow a standard relational database schema and yet has a certain level of organization.

In other words, data classification and storage systems are more flexible for semi-structured data sources than structured and unstructured ones.

Types of Semi-Structured Data

There are several types of semi-structured data. We can mention

  • JSON files
  • HTML web pages
  • CSV files

The difference between semi-structured and unstructured data resides at the organizational level. While the latter comes in different forms and types, the former is organized by tags and structures.

Examples of Semi-Structured Data

One good example of semi-structured data is JSON. It does not restrict the amount of information you can collect yet makes you follow a specific hierarchy.

The main advantages of semi-structured data include: 

  • Flexibility- We can easily change the semi-structured data’s schema.
  • Portability- We can use it through different services and languages. We can use it in a structured query language and in an unstructured one.
  • Ease of use- Compared to unstructured data, semi-structured data is effortless. It can easily fit into structured data tools and the tools that manage unstructured data.

Disadvantages of Semi-Structured Data

The main disadvantages of semi-structured data include:

  • More challenging to query. Since semi-structured data is not as organized as structured data, it can be more difficult to query.
  • More challenging to analyze. Semi-structured data can also be more difficult to study than structured data.
  • Less reliable: Semi-structured data can be less reliable than structured data because it is not as well-organized.

How to Structure Unstructured Data Using Adaptive NLP Models

The most logical question is how to transform unstructured data into structured data?

The answer is simple: Artificial intelligence (AI)!

Text analytics, or text mining, is an AI technology that uses natural language processing (NLP) to convert the unstructured text in documents and databases into structured and normalized data.

Once we structure the unstructured data, we analyze it and input it into machine learning (ML) algorithms.

Artificial intelligence platforms can analyze unstructured text by transforming the unstructured data into a structured format.

Unstructured data platforms like the Accern NoCodeNLP platform use ML and natural language processing (NLP) techniques to:

Although humans would take days to structure the unstructured data, the Accern NoCodeNLP Platform enables business users and data teams to categorize and analyze the text in a fraction of the time with complete accuracy.

Adaptive Natural Language Processing Models

The Accern NLP models deliver quick, timely, and accurate results. Typical adaptive NLP models include text analysis techniques: 

  1. Document Classification automatically categorizes and transforms your documents into a general structure format. The insights extracted from document classification include document titles, source, type, URL, content, and time of publishing. 
  2. Entity Recognition automatically understands text data and extracts names of companies, products, and services, addresses, phone numbers, and other entity-specific information.
  3. Theme Classification automatically identifies, classifies, and extracts information on specific themes, topics, or events. 
  4. Sentiment Analysis rates the sentiment or emotions (positive, negative, and neutral) around a given text or document. Once adequately trained with Accern’s sentiment model trainer, sentiment analysis models can tell how customers feel about a specific entity or theme.
  5. Relevance Analysis automatically determines the relevance of entities and themes within your documents. 
  6. Text Summarization takes large quantities of data and extracts the most relevant details of the text. The most critical points within the document are summarized while keeping the meaning of the data.
  7. Foreign Language Translations scan and interpret content in different languages to identify if the text has a positive or negative connotation in various documents.

How to Classify Unstructured Data Using No-Code NLP

Classifying unstructured data with the Accern NoCodeNLP Platform is a fast and easy 4-step process:

  1. Connect pre-assembled data sources
  2. Categorize with pre-built taxonomies
  3. Analyze with pre-trained NLP Models
  4. Deliver results to pre-integrated dashboards

Watch this 3-min video to learn how data teams can quickly enhance AI / ML Models, augment BI dashboards, and enrich products with insights extracted from the 80% of enterprise data contained in web pages, blogs, emails, documents, reports, transcripts, tickets and more.

NLP for Data Teams: Two weeks to ROI with Pre-Built NLP Applications

Schedule a demo to learn more about the platform and how it can drive unstructured insights for your business ROI.

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