Organizations across all industries rely on data analytics to generate business insights in order to improve operational efficiencies, mitigate risks and deliver superior customer experiences. For their analytics projects to deliver maximum value, companies need to be able to access all of the required data—and that can present a challenge when so much of their content is dark and unstructured. The solution is for organizations to implement a robust, automated data extraction process that will find and convert their data into clean, usable fuel for analytics engines.
Read on for a three-step checklist for using data extraction to maximize the value of an enterprise’s unstructured data.
Step One: Exploration
The more data that is available to an analytics engine, the better and more accurate the results. So, finding all of the data within an enterprise’s repositories is a crucial first step in any data extraction process.
The challenge for all companies is that 80 percent of their data is unstructured. It’s trapped inside documents that are not searchable or machine readable—like paper, emails, image files, CAD drawings, etc. And, the content is distributed throughout the organization in repositories, file shares and ECMs—meaning enterprises often don’t know what data they have or where it is.
For a company to understand the data landscape they are dealing with, they must automatically crawl all of their repositories; identify each piece of data; and then remove the redundant, trivial and obsolete (ROT) data.
Step Two: Enrich
The next step in the data extraction checklist is to cluster enterprise data according to similarities. This begins with taking all of the files (in their many and varied formats) and converting them into high-quality PDFs. A detailed data extraction can only be carried out most efficiently when documents are grouped together by similarity and are available in a universal format.
For instance, if a company clustered all of its invoices together, they could perform a detailed analysis and extract values unique to that particular content type, such as the invoice number, amount due and deadline. This process is more efficient than writing one large set of analytics rules that have to take into account the different extractions required for invoices, contracts, claims forms and every other type of document that an enterprise has.
Once the data has been clustered around similarities, it is ready for an automated Optical Character Recognition (OCR) process. In this step, OCR software processes the pixels within a digital document and turns them into machine-consumable data.
Many companies use zonal or templated OCR software methods in which zones are defined on each page that identify where certain values will be found (i.e. an invoice number is in the upper right corner). While this approach has benefits, it can falter when the system encounters documents where the data is not in its expected location. Hence, it’s optimal to use a freeform method in which a document goes through an OCR software process and is made searchable, so that no matter where on the page the words “invoice number” appear, they can be located.
The value in using OCR software to create machine-readable documents is that it creates document transparency—making the documents searchable so you can index them (creating better access to your data) or enabling you to perform analytics.
Step Three: Automation and Integration
The final step in the data extraction process involves putting the newly enriched data to use by linking its source and destination locations so that it’s seamlessly delivered to the next stage of a business’ downstream process, whether it be an analytics engine, a people-powered task or another other tool.
The companies that do the best job of extracting value from their unstructured data leverage the power of automation. They integrate their data extraction process into their existing workflows, thereby creating an ongoing opportunity to harvest machine-consumable data for their analytics projects, rather than just a one-time project. This is important because many legacy workflows consist of manual steps that are slow and risk prone. Automating and integrating the data extraction process can accelerate workflows across the organization, leading to improved customer experiences, accelerated product and service development, and simplified compliance on an ongoing basis.
The value of data extraction
Following this three-step checklist can provide significant benefits for companies across industries.
Implementing a robust data extraction process that includes finding and identifying all data, dealing with the ROT, utilizing OCR software, creating high-fidelity PDFs and classifying the data is the best way to maximize the value of enterprise data. Once this information is extracted, businesses will be able to conceptualize and analyze their data, and also gain intelligence from it, leading to accelerated decision-making, reduced costs and an improved customer experience.
About the Author
As a senior executive, Scott has spent the last 20 years building Adlib into the thriving organization it is today. Scott has held customer-focused leadership roles spanning success, professional services, marketing, and support. He is passionate about business growth, the human impact of technology, and the pursuit of an ideal customer experience measured in the customers’ terms.