“When working with PDF documents, before importing your collected information to your database for further analysis, you usually need to convert your documents from PDF to JSON.”

From driving process and cost efficiencies to improving financial performance and monitoring customer retention, more companies are now capitalising on data analytics to provide greater insights and contextual intelligence into their operations.

While the use of advanced analytics was once limited to enterprises with deep pockets, analytics has come a long way in a relatively short period of time. With so many open-source data analytics tools on the market, and the growing availability of affordable big data, cloud tools and automated machine learning pipelines, smaller firms now have the option of building their own custom data analytics models, as well as deploying off-the-shelf prebuild data analytic softwares that have third-party integrations.

“59% of all enterprises globally are using advanced and predictive analytics and more than 12% have adopted embedded analytics and personalised distribution of analytics via email and collaboration tools.”

Source:  MicroStrategy’s 2020 Global State of Enterprise Analytics Report

But, whatever the company size and tech stack in use, data wrangling will always be a crucial step in the early stages of the data analytics process.

To put it simply, data wrangling is the combined task of collecting, cleansing, creating data structures, and storing them for later use. It aims to transform raw data e.g., survey responses, invoices, financial information or any data from PDF documents into a more accessible data format such as JSON – a data format that could be deposited into a database or architecture that allows for further data manipulation. Data analysts, engineers, or data scientists will then use these datasets to build business reports and other insights.

Data wrangling on its own is a time-consuming process. In fact, it can take up to 80% of a data analyst’s time. When it comes to building data structures and prepping them for databases in particular, the time spent at this step could be even more time and labour intensive. This is especially relevant when dealing with different sources of data formats e.g., text heavy documents and tabular data that don’t adhere to a particular structure. Without automation, manual adjustments and input by analysts will be an additional iterative step.

But what is meant by structured data?  And how do you automate unstructured data extraction into structured data and import them into a database?

In the data Analytics sphere, think of data structures as the data scientists’ bread and butter.

Data structures are the methods of organising, processing, retrieving and storing data in ways that machines and humans can understand.

When data is “structured”, it is stored in a specific and organised manner that is conducive to operations or analysis - allowing for easy manipulation and querying. Unstructured data on the other hand, is unprocessed data, stored in its native format. According to projections from IDC, 80% of the world’s data is unstructured, sitting as an untapped resource with substantial potential.

Here’s a summary of the main differences between structured and unstructured data:

So how do you automate unstructured data extraction into structured data and import them into a database?

Taking the example of PDF documents for instance, before importing your collected information to your database, you usually need to convert your documents from PDF to JSON.

This is done through parsing information from a PDF document using a data extraction tool like Parsel, that extracts relevant information and converts it to a JSON file - a standard text-based format for representing structured data based on JavaScript object syntax.

JSON is now believed to be the most used data format due to its speed and ease of use. It’s easy to read, write and manipulate by humans and machines. Its speed makes it the go-to format to exchange information between web clients and web servers. It can also be integrated with most of the popular database management systems like PostgreSQL, TimescaleDB, MongoDB, Snowflake and others.

This is why our clients from all industries - from financial services to public sector contractors to small retail businesses - use Parsel. Benchmarked at 96.6% accuracy for financial documents, Parsel's PDF to JSON output option allows clients to easily automate their data extraction and analysis operations via a simple 3-step process - even with the most challenging documents i.e  PDF documents. Why challenging you might ask?

When dealing with native PDFs (electronically generated), copy and paste will often do a decent job extracting small amounts of information. However, when dealing with large volumes of data and documents, such manual approach becomes time consuming and prone to error.

This is where PDF data capture tools become useful. They automatically extract data in a fraction of the time, with better accuracy, yielding substantial productivity gains to your analysts’ team.

Nevertheless, most PDF data tools struggle with scanned PDF documents - which are still commonly used in corporate workflows despite the growing call for document digitisation.

Dealing with scanned PDF documents is more problematic because data is stored as images or pictures (PNG or JPG), which have embedded texts and tables of different size and quality resolutions. These images carry no markup nor character level data or hierarchy, resulting in unorganised unstructured data.

In this instance, parsing tabular data from such image-based PDF can only be recovered using Optical Character Recognition (OCR) functionality, that identifies characters - letters or numbers - from a source file, so they can be reproduced and assembled in the form of a new editable and structured data.

Now that we have covered how data structure types come into play in document parsing. Let’s uncover how Parsel extracts data from scanned documents and converts it from PDF to JSON format.

At Parsel, we use cognitive data extraction - intelligent data extraction algorithms that use AI and machine learning technology to understand the information it is extracting, and categorise it into key-value pairs, tables, and entities.

At 96.6% financial grade accuracy, Parsel's data inference algorithms capture the relevant unstructured data from different documents formats and layouts e.g. invoices, company reports, and convert data from PDF to JSON structured data format. It’s all done online, and eliminates the need for manual guidance - as discussed in our previous blog on serverless architecture.

Furthermore, with our Enterprise plan, you benefit from an unlimited monthly page allowance and direct API access to send the extracted JSON data files to other software or databases.

In addition to JSON, Parsel also supports PDF to Excel and CSV output files when more compact files are preferred.

Simply drag and drop your PDFs into Parsel and let our extraction technology analyse, identify and convert your PDF tables into JSON documents in minutes. See our Step-by-step guide on converting your PDFs to JSON with Parsel.

1. Sign up for a free account on Parsel

To extract data from PDF documents quickly and easy, sign up for a free account with your email address at Parsel.ai.

2.Drag and drop your company report

Next, upload your PDF documents via our simple drag and drop file uploader.

3- And Voila, Download your JSON file

Once Parsel has analysed, identified and extracted the data in your PDF, it'll convert your data to JSON in minutes.

To start using Parsel to capture and extract important information from receipts, bills, invoices, bank statements and company reports, sign up for a free account today.