AI used for data capture


Welcome to our blog post, where we embark on an exciting journey into the realm of AI and its incredible role in automating data capture. Throughout this article, we will explore what AI means for me, the importance of data capture in my day-to-day life, and how AI can personally assist me in automating the process. So, let’s dive in together and discover how AI can revolutionize the way I capture and utilize data, making my life easier and more efficient. Join me on this exploration as we unravel the immense potential of AI in transforming my data management experiences.

What is AI?

Artificial Intelligence (AI) is an ever-evolving field of computer science that seeks to develop intelligent systems capable of emulating human-like interactions and cognitive abilities. You may think this is a new technology but the truth is that it is very old. In fact, when I was going to the University of Minnesota IT, it was a hot topic and was said to be the next big thing in technology. Well, here it is nearly 50 years later and now it is again the hot topic and said to be the next big thing.

Well, this time they may be right.  Among the exciting advancements in AI, one prominent example is ChatGPT, a state-of-the-art language model. ChatGPT, short for “Chat-based Language Model,” is designed to engage in dynamic and natural conversations with users, offering responses and generating text that simulates human-like conversation. By leveraging cutting-edge techniques such as deep learning and neural networks, ChatGPT learns from vast amounts of data to understand context, infer meaning, and provide relevant and coherent responses. With ChatGPT, AI continues to push the boundaries of what is possible in human-computer interaction, opening up new horizons for communication, problem-solving, and personalized experiences.

What Is The Importance of Data Capture?

The importance of data capture cannot be overstated in today’s data-driven world. Data capture involves the process of collecting and recording information from various sources, providing valuable insights and opportunities for businesses. By effectively capturing data, organizations can make informed decisions, improve operational efficiency, enhance customer experiences, and gain a competitive edge. In practical terms, data capture is widely utilized across industries. For instance, in retail, point-of-sale systems capture transactional data, enabling businesses to analyze sales patterns, inventory management, and customer preferences. Similarly, in healthcare, electronic health records capture patient data, facilitating accurate diagnoses, personalized treatment plans, and medical research. Moreover, in marketing, data capture through customer surveys, website analytics, and social media monitoring empowers companies to understand consumer behavior, tailor marketing campaigns, and drive targeted advertising strategies. These examples illustrate the pivotal role that data capture plays in driving business growth, innovation, and strategic decision-making.

Examples Of Practical Uses Of Data Capture

AI’s capabilities in automating data capture extend beyond structured data to include unstructured data as well. Unstructured data, such as emails, social media posts, audio, and video content, poses unique challenges due to its lack of predefined formats. However, AI techniques like natural language processing and sentiment analysis enable the extraction of valuable insights from unstructured data sources. AI-powered systems can analyze text, identify key entities, sentiments, and themes, and organize unstructured data into meaningful categories. By automating the capture and analysis of unstructured data, businesses can unlock hidden patterns, sentiment trends, and customer feedback, leading to enhanced decision-making and targeted strategies. AI empowers organizations to harness the untapped potential of unstructured data, transforming it into valuable knowledge that drives innovation, improves customer experiences, and fuels business growth.

I have a customer who has a team of people that manually extract information from handwritten service records. It is time-consuming and frustrating for the team. The key information that they need to find can be any place on the page. And some people’s handwriting can be hard to read. Now they know how the people who work for the US Postal Service feel.

How great when we say that we can use AI to capture over 90% of the data accurately and even show them which capture need to be looked at manually. With the shortage of qualified workers these days, AI can help reduce the number of staff members needed for data capture and data entry.

How AI Automates Data Capture

In the realm of data capture, AI emerges as a game-changer, automating processes that were once time-consuming and prone to human error. In this next section, I will explore how AI revolutionizes data capture by automating essential tasks. We’ll delve into topics like data extraction, where techniques such as Optical Character Recognition (OCR) and Natural Language Processing (NLP) enable AI systems to extract valuable information from various sources. Additionally, I’ll uncover how AI facilitates data validation and verification, ensuring the accuracy and integrity of captured data. Finally, we’ll discuss how AI excels in error handling and exception management, mitigating potential issues and ensuring seamless data capture processes.

Using Optical Character Recognition (OCR) in Data Capture

ai invoice capture
ai invoice capture data

OCR, short for Optical Character Recognition, is a transformative AI technology that plays a vital role in automating data capture. Using advanced image processing algorithms, OCR enables machines to recognize and extract text from scanned documents, images, or even handwritten notes. (Note recognizing handwritten is often called Intelligent Character Recognition (ICR). By analyzing the visual patterns, shapes, and patterns of characters, OCR algorithms accurately convert images into editable and searchable text. This powerful capability eliminates the need for manual data entry, saving time and reducing errors. OCR has applications in various industries, from digitizing paper-based documents to automating invoice processing and data extraction from forms. With OCR, businesses can efficiently extract valuable data from a range of sources, improving productivity, and data accuracy, and streamlining their overall data capture processes.

I was testing this on a series of service cards for a public utility company. I was amazed at how accurately the AI was able to capture the handwritten information on the pages. It was able to capture information that was written vertically on the page.

Natural Language Processing (NLP) Is Used in Data Capture

Natural Language Processing (NLP) is an innovative AI technology that empowers machines to understand and interpret human language. By combining linguistics, machine learning, and computational algorithms, NLP enables computers to comprehend, analyze, and generate human-like text. In the context of data capture, NLP plays a crucial role in extracting valuable insights from unstructured text sources, such as emails, social media posts, or customer feedback. NLP algorithms can identify key entities, sentiments, and themes within the text, allowing businesses to derive meaningful information from large volumes of unstructured data. This capability is invaluable for automating data capture, as NLP helps categorize, summarize, and extract relevant information with remarkable accuracy and efficiency. With NLP, organizations can unlock the potential of unstructured text data, gain valuable insights, and make data-driven decisions with confidence and precision.

NLP would be a great help in capturing data in legal discovery. I found that it was helpful in capturing data from easement documents for a public utilities company. Since these types of documents are very unstructured it is very difficult for traditional capture technologies to have any success.

Data Validation and Verification

Data validation and verification are critical steps in ensuring the accuracy, integrity, and reliability of captured data. With the power of AI, organizations can automate these processes, saving time and reducing human error. AI systems can analyze captured data, comparing it against predefined rules, standards, or algorithms to validate its consistency, completeness, and adherence to quality criteria. Through machine learning algorithms, AI can identify patterns, anomalies, and potential errors in the data, flagging them for further investigation or correction. Furthermore, AI can perform data verification by cross-referencing it with external sources or reference data to ensure its accuracy. By automating data validation and verification, organizations can enhance data quality, maintain compliance with regulations, and make informed decisions based on reliable and trustworthy data.

In the case where I was using AI for the service cards, I could compare the street names captured and compare them to a database of valid street names in their service area. Or in the case of invoice capture, I could compare the PO number captured to the list of POs in the ERP and validate that the data matches.

Error Handling and Exception Management

For me, this is the fun part of automating data capture. Rather than just flagging a document captured that has errors we use Laserfiche Workflow to correct the data.

For example, when I was using AI to analyze service cards coming in from field staff. They were not consistent in the addresses. There were abbreviations for streets such as “St”, “ST”, “st”,  “St.”. And they could spell out the numbers. The Laserfiche Workflow could make sure that each address was stored in the preferred format, maintaining the consistency that the client wanted.

The Future Of AI For Data Capture

Ai is currently revolutionizing data capture. But we are just getting started using artificial intelligence for data extraction. The future of AI for data capture holds immense potential for further advancements and innovations. As technology continues to evolve, several exciting trends are shaping the landscape. Firstly, we can expect even more sophisticated techniques in data extraction, leveraging AI advancements to extract information from diverse sources with higher accuracy and efficiency. One such technology is Computer Vision, where AI will derive information from images. For example, it could recognize the logo and associate it with the company name.

Additionally, AI will continue to enhance data understanding and context awareness, enabling systems to capture and interpret data in a more nuanced and intelligent manner. Integration with emerging technologies such as computer vision and speech recognition will expand the capabilities of AI for data capture, allowing for the extraction of information from visual and audio sources. Furthermore, we anticipate an increased focus on data privacy and security, with AI playing a crucial role in ensuring compliance and protecting sensitive data during the capture process. Overall, the future of AI for data capture promises exciting possibilities, empowering organizations with smarter, more efficient, and secure data management practices.

I believe in the near future data capture systems using new artificial intelligence can process complex data and do better data validation. And as we see these data-capture software systems get integrated into customer relationship management and ERP systems, the benefits of automated data capture will be realized. This ai software will reduce inaccurate data, reduce data entry errors, and improve data protection and data classification.  I encourage you to watch the AI trends but don’t wait, look at ways you can use ai data capture today to reduce the strain on your staff.

I would love to show you how these ai capabilities would help you capture key data from for documents.