Why Is Data Quality Still So Hard to Achieve?
Dataversity
OCTOBER 25, 2023
We exist in a diversified era of data tools up and down the stack – from storage to algorithm testing to stunning business insights. appeared first on DATAVERSITY.
Algorithm Data Quality Data Silos 
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Data Integrity: The Foundation for Trustworthy AI/ML Outcomes and Confident Business Decisions
ODSC - Open Data Science
APRIL 28, 2023
As critical data flows across an organization from various business applications, data silos become a big issue. The data silos, missing data, and errors make data management tedious and time-consuming, and they’re barriers to ensuring the accuracy and consistency of your data before it is usable by AI/ML.
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Why Your Data Governance Strategy is Failing
Alation
OCTOBER 5, 2021
Yet there is often lack of awareness of the trustworthiness (or lack thereof) of the data that these algorithms are being trained on. It is commonplace for a company to create an enterprise data governance strategy that fails to even consider the end user. Roadblock #3: Silos Breed Misunderstanding.
Mentoring Women in Big Data: A European Perspective
Women in Big Data
FEBRUARY 1, 2023
She goes on to explain the one of the most beneficial features of One Data’s enabling technology, One Data Cartography , is record linkage combined with data quality. This feature enables holistic and seamless data tracking across system boundaries, based on algorithms and automatic checks for quality anomalies.
Data Intelligence empowers informed decisions
Pickl AI
DECEMBER 4, 2023
So, what is Data Intelligence with an example? For example, an e-commerce company uses Data Intelligence to analyze customer behavior on their website. Through advanced analytics and Machine Learning algorithms, they identify patterns such as popular products, peak shopping times, and customer preferences.
Solving Complex Telecom Challenges with Data Governance and Location Analytics
Precisely
FEBRUARY 12, 2024
They use advanced algorithms to proactively identify and resolve network issues, reducing downtime and improving service to their subscribers. Effective data governance assures that each data set has a clear owner and that the organization has mechanisms in place to measure and score things like data quality.
Top Data Challenges Facing Modern Retailers
ODSC - Open Data Science
JULY 13, 2023
The underlying issue is quality. For example, feeding an algorithm statistics about consumer purchasing behavior from stores in one location might lead to poor optimization in another because the data might not be applicable. A retailer must connect data silos across the entire organization for proper consolidation.
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