Converting Unstructured Data in Documents to Structured Data

Mitchell Sloan
Post by Mitchell Sloan
April 7, 2021
Converting Unstructured Data in Documents to Structured Data

While valuable, unstructured data can be a hassle - it’s messy, and it isn’t easy to extract vital data points manually. If you’re working with significant sources of information like invoices, emails or detailed financial reports, deciphering what information is essential can be unnecessarily time-consuming. Let’s dive into how you can make the most of your unstructured data by converting it to useful information.

There are many examples of unstructured data, including: 

  1. Emails
  2. Images
  3. Reports
  4. Invoices
  5. Ticker Data 
  6. Sensor Data
  7. Presentations
  8. Medical records
  9. Survey responses
    10. Social media posts
    11. Video and audio content

While the data from these sources is extremely valuable, it’s only usable once it’s converted to information relevant to your current problem. Once the data has been extracted, the unstructured data must be cleaned and converted into practical information. Here’s how you can convert unstructured data into useful information:

Identify your problem

Before you can analyse the data you’ve collected, you need to know what problem you want to solve. Narrowing down your main point allows you to trim away unnecessary data points and get right to essential facts. Understanding your pain-point will also allow you to narrow down the sources of your data.

Utilise Optical Character Recognition (OCR)

OCR software recognises texts inside images (like scanned documents or pictures) and converts them into readable data. Furthermore, this technology was first used for digitising old newspapers and books. 

Today, OCR is used to import printed documents into readable text documents, which can then be edited in a word processor. This software eliminates the need for manually retyping text from lengthy documents. 

If you need to extrapolate data from printed sources (like receipts or forms), OCR is a valuable tool. However, if you want to categorise your data automatically, you'll need an automated document processing system like Acodis. 

You can structure the data from the OCR system to fit your needs. For example, you can use OCR data and Acodis to understand contents from a complex financial report. In fact, Acodis is a world leader in table recognition

The data extraction process includes:

  1. Inspection
  2. Classify
  3. Extract
  4. Analysis

OCR helps read a text and pre-processes the information, meaning that once you've completed the above steps, the information you've obtained is usable.

The generated text created from the OCR system is then structured into machine-readable data, ready for analysis and interpretation. Data extraction takes the data from the OCR and turns it into usable information. This type of extraction can be used for payroll and invoice, among other processes.

Unstructured data is available in nearly all areas of your business. It is up to your company to understand what data is essential and convert the raw numbers/facts into analysable information.

How much unstructured data do you deal with? Do you find that your unstructured data can be a hassle?

Let’s discuss how we can streamline your process. 
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Mitchell Sloan
Post by Mitchell Sloan
April 7, 2021
Content Marketer