Company Filing History:
4 out of 100 
1 out of 46 
Years Active: 2013-2024
Introduction
Amit Prakash, an accomplished inventor based in Centreville, VA, has made significant contributions to the field of data quality evaluation with a portfolio of five patents. His innovative solutions address critical challenges in measuring data quality over time, showcasing his expertise and commitment to advancing data systems.
Latest Patents
Among his latest innovations, Amit developed systems and methods for measuring data quality over time. These patented systems include advanced methodologies that evaluate data integrity by storing and analyzing records based on shared attributes across different time periods. By comparing first and second values against pre-established business rules, Amit’s inventions generate consistency data that reflect adherence to these rules. Furthermore, they formulate a quality change rate that assesses the evolution of data integrity, providing valuable insights for businesses seeking to enhance their data management processes.
Career Highlights
Amit has made a substantial impact during his tenure at notable organizations such as the Federal Home Loan Mortgage Corporation (Freddie Mac). His experiences at such esteemed companies have equipped him with the necessary skills and knowledge to tackle complex data challenges effectively.
Collaborations
Throughout his career, Amit has collaborated with talented professionals like Geni Gomez Morejon and Charles C. McKinney. These partnerships have enriched his work and fostered an environment of innovation and creativity, essential for developing groundbreaking solutions in data quality measurement.
Conclusion
In summary, Amit Prakash stands out as a key figure in the innovation landscape concerning data quality management. His extensive patent portfolio and collaborative efforts reflect a dedicated commitment to enhancing data systems, proving his invaluable contribution to the industry. As he continues to push the boundaries of technology, Amit's work will undoubtedly shape the future of data quality assessment.