In this contributed article, engineering leader Uma Uppin emphasizes that high-quality data is fundamental to effective AI systems, as poor data quality leads to unreliable and potentially costly model outcomes. Key data attributes like accuracy, completeness, consistency, timeliness, and relevance play crucial roles in shaping AI performance and minimizing ethical risks. Uppin argues that robust data governance practices, including regular data checks and a company-wide data management culture, are essential for sustaining AI accuracy and reducing discrimination.
Business Leaders Must Prioritize Data Quality to Ensure Lasting AI Implementation
July 24, 2024 by
In this contributed article, Subbiah Muthiah, CTO of Emerging Technologies at Qualitest, takes a deep dive into how raw data can throw specialized AI into disarray. While raw data has its uses, properly processed data is vital to the success of niche AI. Industries such as medical, legal, and pharma require data that is contextualized, categorized, and verified. Business leaders must master data processing to succeed in an AI-powered world.
In 2024, Data Quality and AI Will Open New Doors
May 1, 2024 by
In this contributed article, Stephany Lapierre, Founder and CEO of Tealbook, discusses how AI can help streamline procurement processes, reduce costs and improve supplier management, while also addressing common concerns and challenges related to AI implementation like data privacy, ethical considerations and the need for human oversight.
Cloud Migration Alone Won’t Solve Data Quality. Here’s Why CDOs Need a More Holistic Approach
April 1, 2024 by
In this contributed article, Emmet Townsend, VP of Engineering at Inrupt, discusses how cloud migration is just one step to achieving comprehensive data quality programs, not the entire strategy.
Scaling Data Quality with Computer Vision on Spatial Data
February 12, 2024 by
In this contributed article, editorial consultant Jelani Harper discusses a number of hot topics today: computer vision, data quality, and spatial data. Computer vision is an extremely viable facet of advanced machine learning for the enterprise. Its utility for data quality is evinced from some high profile use cases. This technology can produce similar boons for other facets of the ever-shifting data ecosystem.
Three Roadblocks to Using Data to Its Full Potential
December 2, 2023 by
In this contributed article, Sridhar Bankuru, VP of Software Development at RightData, walks us through the top pain points of businesses today within their data trust journey, and looking ahead, how they can start trusting their data again.
Who Done It? 3 Possible Suspects in this Halloween’s Bad Data Horror Movie, And How Data Teams Can Make It Out Alive
October 31, 2023 by
In this contributed article, Lior Gavish, CTO and Co-Founder of Monte Carlo, outlines some of the ways companies can erase themselves from ever appearing in these bad data horror stories, ranging from simple tips to bolster governance within their organization, to tools and best practices that will save data teams the time, hassle, and headache that comes with dealing with bad data.
State of Data Quality Report
June 13, 2023 by
Bigeye, the data observability company, announced the results of its 2023 State of Data Quality survey. The report sheds light on the most pervasive problems in data quality today. The report, which was researched and authored by Bigeye, consisted of answers from 100 survey respondents.
The Importance of Data Quality in Benefits
June 6, 2023 by
In this contributed article, Peter Nagel, VP of Engineering at Noyo, addresses the benefits/insurance industry’s roadblocks and opportunities — and why some of the most interesting data innovations will soon be happening in benefits.
Study Finds Data Quality is Still the Largest Obstacle for Successful AI and Greater Human Expertise Needed Across ML Ops Lifecycle
May 28, 2023 by
iMerit, a leading artificial intelligence (AI) data solutions company, released its 2023 State of ML Ops report, which includes a study outlining the impact of data on wide-scale commercial-ready AI projects. The study surveyed AI, ML, and data practitioners across industries, and found an increasing need for better data quality and human expertise and oversight in delivering successful AI. This is especially true as powerful new generative AI tools and continuous improvements to automation are rolled out at an increasingly rapid pace.
