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Data Capture
Jun 28, 2022
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What Is Data Capture (Now and in the Future)?

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Chief AI for Everyone Officer

According to Infosource, the Capture Software market exceeded $5B in 2021. Companies seeking to drive down costs and improve customer experience are increasingly turning to electronic data capture software. This article covers various approaches to data capture as well as what data capture software is, how it works, its benefits, and how recent advances in AI are disrupting this massive market.

What is data capture?

Data capture, or electronic data capture, is the process of extracting information in a structured or machine-readable format from any structured, semi-structured, or unstructured data source—including documents (paper or electronic), emails, images, video, audio, and text.

Although most applications of data capture focus on documents, recent advancements in artificial intelligence (AI) and machine learning (ML) have enabled modern systems to recognize and capture data from any unstructured data type.

Some common real-world examples of data capture include:

  • Travel: Automated hotel check-in and self-service passport control leverage OCR, a form of data capture, to enable travelers to verify their identify simply by scanning a document.
  • Healthcare: From digitizing complex reports to protecting the sensitive information within them, data capture has broad applicability in the healthcare industry.
  • Legal: The legal industry is known for its stubborn use of paper, making digitization a constant struggle for firms seeking to future proof.
  • Insurance: Accurate data capture is the first component of fully automated claims processing, which reduces turnaround time and improves customer experience.

How does data capture work?

Data capture software has evolved over the decades and now involves a number of steps:

  1. Data digitization: If the data is on paper, tape, cassette, DVD, or another legacy format, it needs to be converted to a standard digital format such as a PDF, CSV, TXT, GIF, or MPEG  for processing.
  2. Data upload: Data is sent to the capture software manually or via API calls.
    Data classification: Next the data is classified by the data type and a sub-category within it.  For example, a scanned document may need to be further classified as an invoice, PO, bill of lading, contract, etc.
  3. Data processing or extraction: Next, the data is transcribed and the desired information is extracted. In the case of invoices, the information includes things like vendor name, shipping address, receiving address, line items, payment terms, balance due, and more.
  4. Data enrichment: The extracted data is often enriched based on business rules.  Again using the example of invoices, the PO number is often validated against the customer PO format, the addresses validated using web APIs, and the line item costs added to see if they match the total due.
  5. Learning: Newer data capture tools have the ability to route data that could not be processed by software or AI to humans for manual extraction.  The platform then uses AI to learn from humans to improve extraction accuracy and automation rates.
    Data download: Finally the processed or extracted data is downloaded either manually or via API calls for further action or storage.

Types of data capture

Manual data capture

Manual data capture involves humans typing information from documents, emails, and other sources manually into structured machine-readable formats. This approach is expensive, error-prone, and is increasingly being replaced with automated solutions.

Automated data capture

To reduce the cost of manual capture, enterprises have increasingly turned to varied automated approaches for data capture. Below is a list of some common automated data capture techniques.

Optical Character Recognition (OCR)

OCR technology has been widely used for over three decades to turn scanned or photographed text into machine-readable text.  The usage started in mailrooms where paper documents were first scanned and then digitized using OCR.

Intelligent Character Recognition (ICR)

ICR is next-generation OCR technology that is capable of understanding and extracting both typed and handwritten text in scanned or photographed documents.

Optical Mark Reading (OMR)

OMR technology determines the presence or absence of marks at a specific location in a document. This technology is widely used to process hand-filled forms.

Barcodes and QR Codes

Barcodes were designed to automate information capture using scanners.  They are widely used in retail and supply chains to streamline the movement and sale of goods.

Digital Signatures

Digital signature adoption has accelerated in recent years, especially during the pandemic.  Digital signatures streamline processes such as on- and off-boarding, purchasing, and order processing to automate signature collection move documents through chains of custody faster.

Online Forms

Online, mobile, or digital forms capture data at the source and eliminate the need for manual data entry.

Intelligent Document Processing

With the rise of robotic process automation (RPA), the decision-making for data capture solutions started shifting from mailroom to line of business and shared services or global business services (GBS) organizations. Business users found OCR and ICR technology hard to set up and use. This led to the rise of intelligent document processing (IDP) solutions that greatly simplified the user interface and allowed users to pick between a number of OCR and document AI solutions to deliver better results, faster.

Web Scraping

Bots or web crawlers find and capture information from one or more online sources.

Magnetic Swipe Cards

The information encoded in magnetic strips of magnetic swipe cards is captured using readers.

Smart Cards

Increasingly the information that used to be encoded in magnetic swipe cards is encoded in microchips on smart cards for greater convenience as well as a higher level of security and privacy.  The readers typically use near-field communication (NFC) technology to capture information securely from smart cards.

Magnetic Ink Character Recognition (MICR)

MICR readers recognize data encoded in magnetic ink-printed machine characters using. This technology is widely used by banks for check processing.

Text Capture

Text capture is an AI solution that classifies and/or extracts the intent or sentiment from text such as instant messages, chatbots, and unstructured documents.

Email Capture

AI solutions are increasingly used to classify and extract information from business emails such to facilitate customer support, help desk cases, and inter-bank settlements.

Image Capture

AI-powered image capture solutions are increasingly being used to validate identity documents (e.g, employee onboarding and Know Your Customer compliance), redact or anonymize personally identifiable information (PII) information, extract nameplate data, and detect damage in images.  

Video Capture

Businesses are increasingly relying on CCTV and drone footage for security and visual inspection. AI solutions can automatically extract license plate information, detect crop damage and estimate yield, assess property values and damage, and more.

Audio Capture

Businesses are increasingly auditing their customer-facing resources, including sales, customer success, and customer support.  Emerging AI solutions analyze the captured recordings to understand customer sentiments and coach employees to improve customer interactions.

Unstructured Data Processing (UDP)

Until recently customers have had to rely on multiple point solutions for data capture. This could be IDP or OCR for documents, and separate tools for email, images, video, audio, and text.  An emerging category of [unstructured data processing (UDP)](https://super.ai/unstructured-data-processing) platforms allows users to classify and extract information for any unstructured data type.

Benefits of automated data capture

  1. Lower costs by 85% or more by automating previously manual data entry tasks.
  2. Reduce errors compared to humans who are not really suited for repetitive robotic tasks.
  3. Improve customer experience by speeding up issue resolution, personalizing the online experience, and offering online self-service options.
  4. Reduce risks by automatically anonymizing PII information.

Advantages of unstructured data processing (UDP) platforms for data capture

Emerging UDP platforms have several advantages over OCR, ICR, IDP, and other points electronic data capture software including:

  1. Any data type: UDP platforms are designed to process any unstructured data type - documents, emails, images, video, and audio, providing you a one-stop-shop for all your unstructured data processing needs.
  2. Outcome guarantee: These solutions have moved beyond offering just a confidence level for their AI models.  They allow users to define the trade-offs between quality, cost, and speed and automatically allocate resources between AI, humans, and bots to guarantee the outcome.
  3. Low touch setup: They provide the ability to break processing into simpler tasks and use the best human, AI, or bot workers to deliver better results faster and with higher automation rates.  They take care of the setup, model selection, training, ongoing maintenance, and creating and deployment of new AI workers to continuously increase automation rates.
  4. AI model agnostic: AI models are evolving and getting commoditized quickly.  Rather than investing in proprietary AI models and competing with Googles of the world, they are building platforms that select the available AI model for a given sub-tasks to offer the highest quality results at all times.
  5. Comprehensive human resource management: Humans are critical for the success of data capture automation.  But human resource management is often an afterthought.  These companies are creating a curated workforce of crowdsources workers adept at training AI models during deployment and validating results during production.  These platforms include gamification to keep workers engaged and sophisticated escalation rules to make sure a given validation task is completed in time to meet SLAs.

Additional unstructured data processing resources

Electronic data capture software has been around for decades.  There are numerous ways of capturing data.  Recently, data digitization has become one of the top enterprise initiatives.  Emerging unstructured data processing (UDP) platforms can greatly simplify data capture by allowing you to extract information from any unstructured data type, quickly and with guaranteed quality. For more information about UDP, check out the following resources:

Other Tags:
Data Capture
Data Digitization
Digital Transformation
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