article thumbnail

Monitoring Data Quality for Your Big Data Pipelines Made Easy

Analytics Vidhya

In the data-driven world […] The post Monitoring Data Quality for Your Big Data Pipelines Made Easy appeared first on Analytics Vidhya. Determine success by the precision of your charts, the equipment’s dependability, and your crew’s expertise. A single mistake, glitch, or slip-up could endanger the trip.

article thumbnail

Cloud Migration Alone Won’t Solve Data Quality. Here’s Why CDOs Need a More Holistic Approach

insideBIGDATA

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.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

State of Data Quality Report

insideBIGDATA

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.

article thumbnail

Business Leaders Must Prioritize Data Quality to Ensure Lasting AI Implementation

insideBIGDATA

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.

article thumbnail

The Importance of Data Quality in Benefits

insideBIGDATA

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.

article thumbnail

The Problem with ‘Dirty Data’ — How Data Quality Can Impact Life Science AI Adoption

insideBIGDATA

Jason Smith, Chief Technology Officer, AI & Analytics at Within3, highlights how many life science data sets contain unclean, unstructured, or highly-regulated data that reduces the effectiveness of AI models. Life science companies must first clean and harmonize their data for effective AI adoption.

article thumbnail

How to improve your data quality in four steps?

Dataconomy

Did you know that common data quality difficulties affect 91% of businesses? Incorrect data, out-of-date contacts, incomplete records, and duplicates are the most prevalent.