Data Action Layer September 6, 2023
Because data powers every decision in financial services today, your business’ performance is directly dependent on the quality and completeness of your data. That data can come from a multitude of sources, in various formats, and is often hidden in content sources that aren’t structured or readily transparent, like emails and slide decks. That’s why investors, wealth and asset managers, fund managers, service providers and financial services firms opt to automate data processing to help generate actionable data from unstructured documents.
There are varying levels of capabilities and outputs when automating document processing, but the goal is always the same—make the data usable. How do you know what type of solution is right for you? Start by identifying the problem you want to solve. For example, do you need simple, straightforward text mining? Do you need to extract specific data from documents and populate it in specific systems? Or, do you need to extract data, reshape and reformat it, and put it to use immediately in other parts of your business?
There are three levels of document processing: data extraction tools, intelligent document processing (IDP) and adopting a core business system to manage unstructured data.
Each level increases in ability to handle complexity, volume and speed. Data extraction is the most basic. Intelligent document processing often adds the ability to export raw data into other systems, and a business system goes one step further by delivering transformed and actionable data to your other systems, as well as maintaining source traceability.
Whether you use data extraction, IDP, or a full business system—your business goals and needs should drive the scope of your solution.
Data extraction is a broad category. Any activity that involves retrieving data and replicating it for later analysis or manipulation, whether from structured or unstructured sources, can be considered data extraction. This can include techniques like web scraping or text mining.
There are several tools, usually running on optical character recognition (OCR) that can turn a PDF into text. However, these solutions don’t do much more than that. A data extraction solution can be useful if you are just looking to get clean copy and paste data. However, if you want more to happen with your data post-extraction, these tools would require you to add-on or build complicated transformations and workflows, or to rely on manual work from your team.
Intelligent document processing is an evolution of basic data extraction, understanding that people want to do more than copy and paste information. Essentially, IDPs leverage machine learning to broaden the scope of potential files. Not only can IDP platforms extract data from documents like PDFs, word docs, emails, or scanned images, but they can also export this raw data at scale and classify it based on predetermined fields.
IDPs employ various technologies to unlock and extract unstructured data from documents. There is a lot of variation of capabilities in this category that are dependent on where the vendor has invested for product development. Some systems are more basic and use things like templates built for each document type to process documents. Others employ more sophisticated technologies to understand and read documents without the need for templates. There are also varying levels of integrations and export methods.
Because IDP can generate accurate data from an immense breadth of document types, it eliminates edge cases and automates much of the extraction process. These benefits, in turn, drastically decrease the human oversight required to power your organization’s processes at scale.
The next generation of managing unstructured data comes in the form of a complete business system. Similar to an IDP, the business system merely starts with the process of data extraction. The two things that make it distinct are 1) the ability to continuously interact with the data extracted from documents and 2) the flexibility of manipulating the data to fit into your workflows.
Financial services firms need to onboard thousands of documents annually, and while the name of the document might be similar (i.e. quarterly report) there is little consistency otherwise beyond measurement of pure financials.
Business systems for unstructured data help you build a structured, searchable database of actionable data points that can be implemented across workflows and other technologies. It is a way to get insights, deadlines and other important information often hidden in the context of these documents. Because it is a database in its own right, it provides features like traceability and auditing.
Overall, as you consider how to manage your unstructured data, think beyond the features of different platforms and look at how each solution can help you meet your business goals. The solution you choose could also have an effect on your whole workflow, from the human resources that you need or can free up, to the ease of scaling your business if you add new document types or data sources. Start your evaluation with a clear picture of the problems you want to solve, and don’t be afraid to dream big—the right solution for your business will do more for you than just extracting data.
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