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Data architecture strategy for data quality

IBM Journey to AI blog

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Unfolding the difference between Data Observability and Data Quality

Pickl AI

In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications.

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12 AI Insight Talks to Help Improve Your Company’s AI Game at ODSC West

ODSC - Open Data Science

Delphina Demo: AI-powered Data Scientist Jeremy Hermann | Co-founder at Delphina | Delphina.Ai In this demo, you’ll see how Delphina’s AI-powered “junior” data scientist can transform the data science workflow, automating labor-intensive tasks like data discovery, transformation, and model building.

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Exploring Different Types of Data Analysis: Methods and Applications

Pickl AI

Clustering: Grouping similar data points to identify segments within the data. Applications EDA is widely employed in research and data discovery across industries. Researchers use EDA to better understand their data before conducting more formal statistical analyses.