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. Show
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:
Let’s start! Differences Between Structured, Unstructured, and Semi-structured DataThe 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. Source: Lawtomated on MediumWhat is Structured DataStructured 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 DataExamples of structured data include
Structured Data ExamplesThere 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 DataThe main advantages of structured data include:
Disadvantages of Structured DataAlthough structured data has its advantages, it also has its cons:
What is Unstructured Datanstructured 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 DataUnstructured data comes with no limitations in type, and you can find it in various formats such as:
Examples of Unstructured DataThere are various examples of unstructured data assets in business usage. Below are some typical examples:
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 DataThe main benefits of unstructured data include:
Disadvantages of Unstructured DataThe main disadvantages of unstructured data include:
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 DataSemi-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 DataThere are several types of semi-structured data. We can mention
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 DataOne 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:
Disadvantages of Semi-Structured DataThe main disadvantages of semi-structured data include:
How to Structure Unstructured Data Using Adaptive NLP ModelsThe 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 tools like Accern’s No-Code NLP platform use machine learning algorithms and natural language processing (NLP) techniques to
Although humans would take days to structure the unstructured data, Accern’s No-Code NLP platform enables end-users to categorize and analyze the text in a fraction of the time with complete accuracy. Adaptive Natural Language Processing ModelsThe Accern NLP models deliver quick, timely, and accurate results. Typical adaptive NLP models include text analysis techniques:
How to Classify Unstructured Data Using No-Code NLPClassifying unstructured data with the Accern NoCodeNLP Platform is a fast and easy 4-step process:
Schedule a demo to learn more about the platform and how it can drive unstructured insights for your business ROI. What is difference structured and unstructured?Non-structural items include things like doors, cabinet sets, flooring, trim, windows and other finishing materials. In contrast, structural deconstruction requires more integral components of a building, like load-bearing walls, to be systematically dismantled.
What is the main difference between structured and unstructured data Accenture?Structured data is clearly defined and searchable types of data, while unstructured data is usually stored in its native format. Structured data is quantitative, while unstructured data is qualitative. Structured data is often stored in data warehouses, while unstructured data is stored in data lakes.
What is structured and unstructured data give one example of each?The examples of unstructured data vary from imagery and text files like PDF documents to video and audio files, to name a few. Structured data is often spoken of as quantitative data, meaning its objective and pre-defined nature allows us to easily count, measure, and express data in numbers.
What is unstructured data example?Unstructured data just happens to be in greater abundance than structured data is. Examples of unstructured data are: Rich media. Media and entertainment data, surveillance data, geo-spatial data, audio, weather data.
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