In today’s data-driven world, file analytics is one of the most powerful solutions for unlocking the insights contained within an organization’s unstructured data (emails, MS Office, CAD and other formats). But when it comes to analyzing any complex documents, businesses have two options. They can rely on manual indexing where staff re-key and re-file data from every single document, or they can utilize Optical Character Recognition (OCR) software to create fully text-searchable documents that are primed for sorting and analysis.
But when millions of documents are at stake among Energy, Financial Services, Life Sciences, and Insurance businesses, manual indexing is not a feasible or affordable task – nor is it highly accurate. That’s why an automated OCR solution is an essential part of the file analytics solution, ensuring the valuable insights within complex documents can be identified and leveraged for business intelligence
How File Analytics Transforms Data
While more and more companies are prioritizing data collection, not all of the data they take in is accessible or usable for analytics. Only about 25 percent of enterprise data is considered structured – meaning it exists in text-based, machine-ready formats, and can easily be discovered and utilized. The opposite is true for the other 75 percent, which is locked in emails, TIFF files, images, scanned documents and other unstructured formats that cannot be readily used for analysis and decision-making related to market trends, changes in customer behaviour or emerging risks.
With file analytics, businesses can to transform their stores of unstructured data into high-quality structured content by automatically crawling a wide-variety of documents and corresponding metadata to sort, analyze and classify it so that the valuable insights within can be extracted. This is especially important in situations where organizations need to efficiently and accurately classify massive volumes of information, whether to meet compliance reporting requirements, such as those faced by oil and gas companies, or as part of a data migration when undergoing a merger or acquisition, a product or asset swap, or system upgrade.
OCR Software Fuels File Analytics
In the same way you can’t expect to win at poker by seeing only half the cards in your hand, making insightful business decisions requires access to complete data sets. That’s why optical character recognition (OCR) is an essential component in file analytics – giving businesses the power to accurately extract inaccessible data from documents by creating fully-searchable text-based documents for analysis.
When businesses are sitting on hundreds of terabytes of data, the potential insights within these immense content stores are vast. But the task of manually digesting and classifying every scanned, paper and image-based file is simply not feasible —creating a roadblock in gleaning any valuable business insights from their content. With OCR software, businesses can automatically standardize these image-based documents into searchable, accessible data that can then be crawled for analysis. Additionally, the power of automated OCR processes versus manual indexing reduces the staff resources, costs and human error that are associated with manual data capture.
OCR Accuracy is Essential for File Analytics
The level of OCR accuracy that businesses are working with as part of the file analytics process will have a significant impact on results. For example, if an OCR software solution doesn’t support the languages in your documents or frequently introduces random characters into your converted text, these limitations will have a ripple effect on your ability to carry out file analytics to a high degree of accuracy.
For starters, it’s important to weigh your OCR software’s capabilities against your business needs. Some things to look for include:
Consider, for example, the data-intensive research processes a Life Sciences company must undertake to develop life-saving drugs. In order to employ file analytics so that they can glean insights from their deep data stores of compounds and molecular designs, optical character recognition (OCR) must be utilized to ensure that the content in each document is captured with a high level of accuracy. When the goal is to improve patient outcomes, the research data that supports the development of each drug simply cannot be fraught with any errors or omissions caused by poor-quality data capture solutions. As such, OCR accuracy, along with the speed and scalability of your solution to handle growing data volumes are key.
Benefits of OCR Software as Part of File Analytics
The joint power of OCR software and file analytics transforms a business’ ability to:
- Accelerate innovation by being able to access and leverage larger amounts of data (including legacy content) for high-quality insights.
- Identify risk by uncovering sources of PII that were otherwise sitting dark and dormant in scanned paper documents or image-based files.
- Ensure content is readily accessible to fuel RPA because data that has been effectively and accurately classified can be leveraged by bots for process automation.
- Create a competitive edge by being able to analyze more information from customers, which can fuel faster insight into new product/service development.
- Improve the customer experience by ensuring that data from every touch-point – including any handwritten forms or applications – is included in a unified customer record.
With data positioned as the cornerstone of digitization initiatives among today’s modern businesses, the power duo of file analytics and optical character recognition (OCR) cannot be dismissed. When businesses are able to leverage more of their data, they’ll be better positioned to get ahead of trends, make informed decisions and outpace the competition.
Excited to learn more about how OCR software will improve and propel data-based initiatives within your organization? Check out our introductory guide to OCR software and further learn how Adlib's enterprise solution can convert your image-based documents into data that drives essential business advantages.