Some data consists of customers’ numbers; others show when a bill is due. In this article, we’ll specifically dive into «hazardous» data and how companies are retrieving this type of information without incurring errors.
Article content:
Safety data sheets, aka SDSs, typically contain chemical properties, health & environmental hazards, and safety precautions for storing, handling, and transporting chemicals.
They are standardised documents by which chemical manufacturers communicate chemical hazard information to chemical handlers.
Typically, the hazardous information stored within SDSs includes:
But these are merely a handful of the many more data types stored within SDSs.
Before elaborating on how companies extract data from these complex documents, it’s crucial to identify the urgency behind the data. The Occupational Safety and Health Administration (OSHA) states that employers must ensure that SDSs, and the information within them, are readily available to anybody who needs them.
And when we consider that many SDSs are documents that contain complex structures spanning hundreds of pages in length, there’s no wonder why companies seek alternative solutions to extract data from them.
Here is one way companies are doing so...
Humans will look at a document, study where relevant data is located on each page, and then will manually enter each piece of information into an application.
However…
While this seems like the simplest method to extract data from SDSs, there’s always a risk of making errors, especially if someone struggles with fatigue.
Particularly when processing hazardous data from SDSs, some consequences of making errors can be:
The main idea is that typos from SDSs are significantly more catastrophic than your usual email typo to your boss.
So this begs the question: how do companies avoid these outcomes?
As the name suggests, automated data extraction is a faster, more efficient way to process data. Autonomy is often derived from platforms that use machine learning (ML) and artificial intelligence (AI) to learn how to process document data effectively.
Automated data extraction platforms are typically «intelligent» because they can understand data like a human rather than simply reading it into a system.
Example: non-intelligent vs intelligent data extraction
A non-intelligent system, such as OCR (optical character recognition) will read that there are five icons on a given page, but it will not understand what those icons mean.
Whereas an intelligent system, like Intelligent Document Processing (IDP) would identify the meaning of each icon.
Furthermore, intelligent solutions can also identify specific data points contained in complex structures (e.g., figures, tables, etc.), which is particularly useful for SDSs that are often full of them.
SDSs have high priority when retrieving accurate information from them, particularly in a time-efficient way.
And ultimately, automating data extraction, particularly hazardous pictograms, speeds up the entire processing time of SDSs and significantly increases how accurately we can extract their data.