Data offers businesses a much-needed edge over competitors. A large amount of data is hidden among piles of documents waiting to be extracted for making game-changing decisions. RPA has helped organizations transform digitally and enhance process efficiencies by automating repetitive manual tasks. Unfortunately, businesses hit a roadblock with non-digital data in documents such as invoices, scanned paper forms, statements, claims, and receipts.
How can organizations extract insights, especially from data that is locked away in scanned documents? This is where data digitization comes to the rescue.
Whether big or small, every organization has to deal with bulk documents daily. Each of these documents is a gold mine of valuable company insights. However, most documents are non-digitized; hence, extracting data requires manual effort to scan and enter inputs into the enterprise IT system.
Manual data extraction has its own set of challenges, such as:
Digitization of data seems like the best bet for businesses to end the age-old approach to handling documents internally. The emergence of deep learning techniques in computer vision and NLP has enabled a new breed of data digitization solutions.
Unstructured data can immensely benefit from digitization, a few of which are:
Wherever data is needed, digitization will have tremendous scope and opportunity. Almost every sector needs data; hence, it is safe to say data digitization has one foot in every industry, be it manufacturing to retail, logistics to consumer goods, or banking to insurance.
A few businesses use cases are mentioned below:
Form digitization: Multi-page paper-based forms involving a mix of typed text, handwriting, checkboxes, and other fields and tables will be better handled when digitized.
Dynamic extraction or touch-free zero template extraction: Helps deal with non-standard, non-structured input documents that contain the same information in varying layouts.
Content classification and extraction from mixed-type documents: Digitization solutions cater to extracting data and digitizing them from documents of varying types and formats.
Information consistency checking: Every business has a specific complex use case requiring mature products, which address all the previous use cases and support the definition of consistency verification rules that enforce domain-specific rules for information consistency.
Data Digitization is the future of all industries and quite rightfully so to help businesses achieve process efficiencies and address bottlenecks arising from processing paper-based documents for extracting unstructured data. The above use cases and applications aptly point out why data digitization solutions have become the need of the hour.