Data is now considered the new gold for businesses to make better decisions and reach more customers. However, data comes in various forms including unstructured and structured.
In this article, we will explore the process of converting unstructured data to structured data. We will take a look into the importance of structured data in data analysis and decision-making, as well as the benefits of converting unstructured data.
By understanding the significance of structured data and the need to convert unstructured data, organizations can unlock the full potential of their data assets and gain a competitive edge in today's data-driven landscape.
What is unstructured data?
Unstructured data is data that doesn't have any pre-defined model or schema making it challenging to store and process.
The volume of unstructured data is expected to grow to 175 billion terabytes by 2025.
Examples of unstructured data
- Email messages, social media posts and chat conversations
- Images such as digital photographs
- Music and recordings
- Movies, YouTube clips
- Geospatial data
Challenges of unstructured data
Though unstructured data is an important source of information, it poses unique challenges in terms of processing and analysis.
- It cannot be processed or analyzed because of the undefined structure
- There is no standardization because it comes in various formats
- Since there is no metadata, it is difficult to identify characters and categorize
- Data extraction can't be done properly
What is structured data?
Structured data is highly organized and follows a specific data model or schema. Data can be easily searched and analyzed for further processing.
Examples of unstructured data
Structured data is stored in a relational database management system (RDBMS) and contains text and numbers.
- Dates and times
- Customers' names, addresses, phone numbers
- Invoice details (number, date)
- Product details (quantity, description, unit price)
- Discount and grand total
Learn the key differences between unstructured and structured data
Why do you need to convert unstructured data into structured data?
Structured data is a valuable asset in business intelligence and decision-making. Its analysis, consistency, integration capabilities, scalability, and support for data-driven decision-making contribute to improved organizational performance, efficiency, and strategic planning.
By leveraging structured data effectively, businesses can gain valuable insights, make informed decisions, and stay competitive in a data-driven world.
Data accuracy and consistency
Structured data is considered more accurate and trustworthy because it has predefined models. That data can be used to make informed decisions because it is more reliable.
Data analysis and reporting
With its well-defined schema, structured data is easier to access and analyze compared to unstructured data. Using tools and techniques, companies can derive insights and generate reports.
Integration with other applications
Structured data allows for seamless integration and data exchange with other tools. It also enables cross-analysis which helps uncover patterns, and trends from different data sources.
Improves efficiency and streamlines workflows
It enhances searchability making it easier to locate specific data in documents. This reduces manual effort
How to convert unstructured data into structured data?
There are many ways and techniques to perform unstructured data to structured data conversion. One of the easiest (and more affordable) ways is through data parsing.
Steps for converting unstructured text to structured text
Parseur is a powerful document processing tool that automates data extraction for further analysis. It is integrated with a robust OCR engine that provides a high level of data accuracy.
Step 1: Create a free Parseur account
Parseur has a free plan where you can access all the features. Sign up for the plan using the link below.
Step 2: Create a Parseur mailbox to receive the unstructured data
With Parseur, you can create unlimited mailboxes. It has different mailboxes types for different industries such as Google Alerts, food ordering, real estate or general leads. You also have the option to create a custom mailbox.
For this article, let's take the example of converting unstructured text into a receipt.
The mailbox "invoices" is used for invoice and receipt processing.
Drag and drop or forward one of your receipts to this mailbox.
Step 3: The data is converted into structured data automatically
Parseur has built-in templates to process unstructured data instantly. You can also create a custom template for the conversion using our AI-assisted templates and teach Parseur which data to convert and which ones to discard.
Parseur app is integrated with AI OCR, Zonal OCR and Dynamic OCR to ensure accurate data conversion and processing. Parseur also utilizes NLP and computer vision for categorizing unstructured text.
Step 4: Analyze the structured data with other applications
Create a workflow using Zapier, Make or Power Automate to export data to another application for analysis.
You can also download the data or send it to Google Sheets using our default formulas.
As you can see, this is one of the easiest ways to convert data, especially if you're not tech-savvy. Parseur does not require any coding knowledge and is fully template-based.
Are there other tools for data conversion?
Yes, there are many converters for unstructured data such as:
- Python libraries (Pandas, NumPy, NLTK)
- Open Source software (Hadoop)
- Chat GPT-3
- SQL Databases
Ultimately, it will depend on your requirements and what you're trying to do with the unstructured data.
The full of unstructured data
Unstructured data lacks a predefined structure which poses challenges for analysis and integration. On the other hand, structured data is organized, schema-driven data that enables efficient processing, analysis, and integration.
Looking towards the future, AI and machine learning techniques will likely play a significant role in automating the conversion process and extracting valuable insights from unstructured data more effectively.
Last updated on
