The 3% OCR Accuracy Gap

OCR technology has the potential to revolutionize the way enterprises process documents. However, without data validation technology, the full benefits of OCR cannot be realized. Data validation enhances the accuracy, reliability, and efficiency of OCR systems, making them truly effective for enterprise use. By investing in robust data validation solutions, enterprises can unlock the full potential of their OCR systems, ensuring accurate data extraction, compliance with regulations, and improved operational efficiency.
What is Fuzzy Matching?

Fuzzy matching is an advanced technique to link data points that are connected in real life. Enterprises use fuzzy matching to speed up data comparison and standardization. When combined with a robust data validation process, fuzzy matching helps improve overall data health.
Generating Value from Generative AI

Generative AI will heavily influence the value of information services companies in the next half decade, according to a report by Morgan Stanley.
Why Data Quality Can Make or Break Mergers & Acquisitions

Improving data quality delivers value before, during, and after a major merger or acquisition. Find out how to increase your org’s valuation.
Document Validation for FinServ Risk Management

The financial services sector is increasingly recognizing the importance of data quality for risk identification and compliance management. Deloitte listed the major challenges facing QA teams in the financial sector, recommending that using automation to assist in quality control processes “can enable better controls over completeness and timely resolution.”
Data Observability for the C-Suite

Leaders eying artificial intelligence must improve their data health first. For the CXOs, the priority is running a tight operation that can pivot faster and outmaneuver the competition.
Data Observability for Financial Services

Exabytes of data flow through financial services systems every day. The rise in mobile banking, open financial data, and artificial intelligence has created increasingly complex webs of pipelines and other tools. And as the volume of data in the industry grows, companies are working to strengthen their observability capabilities.
Data Observability for Operations

In industries like financial services, which handle massive volumes of personally identifiable information, the ability to answer questions about data and quickly address data issues can impact customer satisfaction, compliance status, and the sophistication of digital products. However, most data observability and data monitoring solutions are too technical for operations teams to use effectively.
Key Terms in Data Observability

If your job involves working with IT and operations teams to answer questions about your company’s data, then you’ve likely come across a glossary of terms like “data quality,” “data monitoring,” and “data governance.”
How Our Clients Scored Big in 2023

How our customers improved their data quality this year by discovering inefficiencies, missing information, and faster processes.