Unstructured data is everywhere–in emails, PDFs, and images. Making sense of this data remains a challenge for organizations aiming to stay competitive.
In this post, we’ll explore how to convert unstructured data to structured data using AI, making it ready for processes like decision-making. Plus, we’ll show you how Unstructured AI streamlines this process effortlessly, ideal for organizations dealing with large volumes of data.
Common Ways to Convert Unstructured Data to Structured Data
When dealing with unstructured data, many professionals rely on Python to write custom scripts or use pre-built libraries for parsing, cleaning, and transforming data.
JSON (JavaScript Object Notation) is often the preferred format for structured data due to its simplicity and compatibility with various systems. While Python is powerful, these manual processes are extremely time-consuming, labor-intensive, and prone to error.
Other tools exist for converting unstructured data, such as Apache NiFi, Talend, or Informatica. However, they involve steep learning curves or significant manual intervention.
This is where Unstructured AI stands out as it simplifies the process, transforming unstructured data into structured formats.
By automating what would otherwise require hours or even days of work, Unstructured AI offers a superior alternative to traditional methods, saving businesses time and resources while delivering precise results.
What is Unstructured AI?
Unstructured AI is an AI Agent that simplifies data preparation processes for artificial intelligence applications and RAG architectures. It serves as an Extract, Transform, Load (ETL) layer designed to process complex, unstructured data formats.
It supports a wide range of file types, including PowerPoint, Excel, CSV, PDF, HTML, DOC, and DOCX, and easily converts them into structured outputs ready for advanced AI applications.
Unstructured AI’s key features include:
Intelligent chunking - Breaking down text into semantically relevant components for better data analysis
Table extraction - Converting tables into data-friendly formats to facilitate easier data manipulation
Chart processing - Extracting charts as images and providing semantic text descriptions to enhance data comprehension
Hierarchical text retention - Maintaining nested text relationships while filtering out irrelevant elements like sidebars or footers
After performing data transformation processes, Unstructured AI delivers the following structured output formats:
Structured text — Outputs in JSON format, preserving formatting and hierarchical structure
Tables — Outputs in CSV and Excel formats
Charts — Outputs as PNG files with semantic descriptions
Unstructured AI also outputs rich meta data, including titles, data, and page counts, to enhance data context.
Unstructured AI vs. Python for Data Conversion
In terms of data conversion, Unstructured AI offers significant advantages over Python, especially when dealing with high-volume, complex, and continuous data streams.
Python requires manual coding and custom solutions for each specific data transformation task.
Unstructured AI, on the other hand, leverages advanced machine learning models and automation.
This allows it to significantly reduce labor-intensive work, making it ideal for enterprises that need to quickly process large, real-time data feeds. It also processes data faster and more efficiently than Python scripts, which require custom code for each transformation.
Finally, Unstructured AI can scale seamlessly as data volumes grow. It minimizes the need for constant manual adjustments, ensuring faster turnaround times and less downtime.
How to Convert Unstructured Data to Structured Data with Unstructured AI
Here is how unstructured to structured data conversion works with our specialized ETL layer, Unstructured AI:
Step #1: Select a Document Type and Upload a Document
Select a documenttype from the dropdown menu. You can choose between analyst reports, financial statements, invoices, and many others. We tailor the selection to your specific business needs.
After the selection, upload a document by choosing a file or dropping it in the designated upload area.
Step #2: Run Unstructured AI
Unstructured AI automatically begins transforming the data after you upload your desired documents. It will parse them using OCR, and run them through large language models specialized for text, table, chart extraction, and hierarchical organization.
You can check the status in the status column or press on your file’s name for more insights.
You’ll see a document you uploaded on the left. On the right side, you’ll see a structured version of your document in JSON.
Step #3: Download a ZIP File of Your Structured Data
When Unstructured AI finishes transforming unstructured data, you’ll be able to download structured outputs in .zip files.
The files will include your now-structured data in JSON formats, including supporting tables and charts extracted as CSVs and PNGs.
How to Convert Table Data Into JSON Format Using Unstructured AI
To convert table data into JSON format using Unstructured AI, follow step #1 from the previous section. Upload the documents containing tables, and Unstructured AI will automatically convert the tables into CSV/Excel formats. You’ll be able to download them as .zip files.
Why Should You Convert Data Into JSON Format?
You should convert data into JSON format to benefit from easier and better data storage, sharing, and processing.
JSON format is:
Easy to read and write. JSON's syntax is straightforward and intuitive, making it accessible to both developers and non-technical users. This makes it easier to read and edit.
Universally supported. JSON is widely accepted and supported by nearly all programming languages. This makes it easy to use across systems, applications, and databases and facilitates data exchange.
Lightweight and efficient. Compared to XML and other similar data formats, JSON is lightweight and faster to sendover the internet. It also requires less storage, which makes it a better option for handling large datasets.
Structured and organized. This helps ensure data is consistent and easy to parse, making specific data retrieval easier from large datasets.
Easy to convert to other formats. Data stored in JSON can be effortlessly converted into other formats, like XML or CSV.
To sum up, JSON lets you retrieve and exchange data more easily, no matter how many different applications you use. Thanks to being easy to convert and extremely structured, it also lets your business easily integrate new tools, collaborate with external vendors, and stay future-ready.
We’ll show you how Unstructured AI works live, discuss your needs, and help you effortlessly structure even the most complex data formats.
FAQs
1. What is an example of structured data?
An example of structured data can be a customer database, which contains fields like name, address, phone number, and email, all organized in rows and columns. Structured data is always organized in a predefined format, typically in tables or datasets.
2. What is an example of unstructured data?
Social media posts make a great example of unstructured data as they contain text, images, or videos that are not organized like structured data. Unstructured data is information that doesn’t have a predefined format.
3. What are the key technologies used to transform unstructured into structured data?
Key technologies used to transform unstructured into structured data include NLP for text analysis, a data intelligence tool like OCR for document parsing, machine learning for classification, and/or speech-to-text for audio conversion, and image recognition for visual data processing.
4. What is structured data useful for?
Structured data is useful for training AI models, building workflows like RAG pipelines, and streamlining business decisions. Learn more about training LLMs with custom data.
5. What are the pros of using specialized, AI-powered data conversion tools?
The biggest pro of using specialized, AI-powered data conversion tools is that you don’t need any data science, AI, or similar expertise to convert unstructured data into structured formats successfully.
For example, you don’t need to understand data patterns or data architecture, as the tools handle these complexities using natural language processing and other advanced AI techniques.
Other pros include the speed and ease of conversion, as well as scalability potential.
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