New technologies streamline business data capture

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Intelligent Document Processing (IDP)
Intelligent document processing (IDP) uses AI and ML to read documents such as applications, forms, invoices, business cards, etc. It can identify specific information on a document, such as a mailing address. It can then act on that information to fill out forms, prepare mailings, and populate databases or customer relationship management systems.

IDP helps organizations harness the 97% of data that is disorganized, uncontrolled, and unused. Better yet, IDP and its related technologies do much of the data cleaning for you. It’s an ace at handling both structured and unstructured data, such as:

Structured Data and Unstructured Data
Bank transactions, receipts, serial numbers

Social media messages, emails, IoT sensor data

Strictly organized, such as the format of rows and columns in a spreadsheet or table.

Each document follows its own rules - there is no strictly followed format

The content follows a pattern. Data points appear in identical locations across all document types (e.g., an IRS W-2).

Information appears in different places (for example, a discussion group transcript or an exchange with a chatbot).

Optical Character Recognition (OCR)
This technology allows machines to perceive letters, numbers and characters in a document. Using their digital eyes, they can scan an application and pinpoint the location of a phone number, the letter "Q" or a passage of text, with an accuracy of more than 99%.

Natural Language Processing (NLP)
illustration of natural language processing (NLP)



With natural language processing (NLP), machines can do more than extract information from a document: they can understand what they read. NLP scans documents at record speed to determine whether it’s a sales contract or an invoice. With sentiment analysis, it can also judge the attitude of the information, such as a positive or negative review of a product. The results can trigger business processes or other tasks.

Computer Vision
Self-driving cars can measure the distance to the next vehicle using computer vision, and robots can remove defective products from the assembly line. Computer vision can also detect errors in documents, such as smudges or typos, to aid in data cleaning efforts. It can then use image processing to intelligently correct the error.

Artificial intelligence
You may have played with large language models like ChatGPT to craft a sales campaign or discover the meaning of the universe. AI’s unmatched ability to comb through and interpret large volumes of information can also benefit your data, distilling numbers into more digestible narratives.

Semantic search, another area of ​​AI, helps solve the searchability problem by disrupting the traditional “search” process. (If you remember back to the beginning, 9 out of 10 employees waste an entire workday each week digging through company data to find what they need.)

Instead of a computer querying its content based on your pakistan mobile number list for whatsapp very specific search terms, it can browse in a more contextual manner.

For example, a medical researcher searches for “PharmX side effects” and gets exactly that. Using semantic search, he will get useful information that might be tangentially related, such as complications, adverse effects, and side effects of similar drugs.

What Accurate Data Means for the Future of Business Intelligence
Gartner lists “applied observability” as a top strategic technology trend for 2023. What does this mean for your data? The gap between customer behavior and business action is shrinking. Organizations must position themselves to act quickly on real-time data.

This means that more and more internal processes will carry data points from PDFs, apps, sensors, and user analytics. The days of scanning a document and walking away are over. Data needs to flow throughout your operations to power everything from back-office management to customer-facing experiences. Process automation is the shepherd of data, but only if it’s clean.