Since many platforms are not end-to-end solutions, evaluating data extraction software is like comparing apples to oranges. Some attributes sound interesting but aren’t useful for efficiently (and consistently) converting and extracting insights from unstructured data.
Enterprises are more reliant on data than ever. And they also have more data than they know what to do with. Hordes of data is generated in digital and paper-based formats—from emails to contracts to invoices, and everything in between. Some of this data can be utilized to fuel critical business functions, but the vast majority can neither be found nor leveraged. There are various data extraction tools that can help manage this ever-growing problem, but many of these solutions aren’t suited for enterprise-level needs.
To find the data extraction software that will best meet your enterprise’s needs, ask these four questions during your evaluation:
1. Does It Ingest Various Types of Data?
Whether it’s numbers, text, or images, data exists in many different types across documents or within a single document. For example, take a document that contains charts and images. If the data extraction software only finds and ingests the text within that document and misses the embedded images, this will result in the business being unable to discover and access complete information. This could lead to errant results from data-reliant processes and/or the need to manually convert the data in question—skyrocketing the time and cost of data extraction and delaying (or curtailing) the results of data-based initiatives.
Look for a data extraction tool that can ingest various document types, including those from multiple sources or ones that contain various types of data within. The end result should be a single, standardized format that’s text-based, searchable, and machine-readable.
2. Does It Have Robust Optical Character Recognition (OCR)?
Optical Character Recognition (OCR) software converts image files (such as paper forms or embedded charts) into searchable data files. OCR is a crucial first step for data that’s not already machine-readable and is an important capability to evaluate. However, not all OCR tools were created equally. Some OCR tools can’t convert special characters, tend to skip pages or words, have high error rates, and can’t handle high volumes of data. These gaps translate to missing information within the converted document. When manual fixes and workarounds are required, the software in question erodes the results of data-centric processes.
Look for an end-to-end data extraction solution that’s highly accurate, can convert documents in all major languages you need (or may need in the future), and can handle a variety of data types and file formats.
3. Is It Capable of Classification?
Beyond ingestion and capture, there are more key actions that must be performed to fully leverage your enterprise’s data. If your software is not capable of extracting and classifying data, then you’ll need to invest in additional tools to ensure that your data is fully searchable and process-ready.
Even within solutions that do have classification capabilities, some require more manual lift or are more limited than others. Some tools require the user to define the metadata upon which to base classification, or to tell the software where within a document to look for certain information. Some may even require the user to create boxes around relevant sections, known as templated extraction. The latter scenario requires an employee to read each document, which is a far cry from the end-to-end automation that enterprises require.
A data extraction solution should be able to crawl documents and extract and classify all the information they contain—without instruction or intervention. This saves time, cuts down on operational costs, and reduces the risk that something important is overlooked because the software wasn’t instructed to look there.
4. Is It Easy to Use & Deploy?
Adopting a data extraction tool that meets the needs of the entire organization is an important way to reduce organizational silos and improve the volume, availability, and accessibility of clean process-ready data. If a data extraction platform is not truly an end-to-end solution, it’s important to consider the impact and added complexities of additional tools that will be needed to convert and organize data, and any steps required to integrate the various solutions. In either scenario, greater IT involvement may be required, both in configuring and maintaining the various solutions.
Leveraging the right data extraction software gives organizations the tools and technology needed to get a handle on their unstructured data and transform it into actionable business insights. But not every solution can convert data into the formats that enterprises need on an automated, consistent, and scalable basis. To find a solution that can handle the vast volumes of historical and net-new data your enterprise grapples with, pay close attention to the key questions outlined in this article.