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The Hidden Infrastructure Crisis: Why CIOs Face a Perfect Storm in IT Talent Management

As organizations navigate the complex landscape of digital transformation, CIOs are confronting an unprecedented crisis that extends far beyond the typical challenges of recruitment and retention. At its core, this crisis represents a fundamental misalignment between traditional IT infrastructure management and modern development practices – a gap that threatens to widen as experienced IT professionals retire […]

My Career in Data Season 2 Episode 36: Ngan MacDonald, Director, Data Innovation, Mathematica and Chief of Data Operations, The Institute for AI in Medicine at Northwestern University

Welcome back to season two of My Career in Data – a DATAVERSITY Talks podcast where we sit down with professionals to discuss how they have built their careers around data. This week we chat with Ngan MacDonald, the Director of Data Innovation at Mathematica and the Chief of Data Operations at The Institute for […]

How AI Is Changing SQL for the Better

Structured query language (SQL) is one of the most popular programming languages, with nearly 52% of programmers using it in their work. SQL has outlasted many other programming languages due to its stability and reliability. SQL doesn’t change dramatically from version to version, and that consistency, combined with a logical design that allows it to deliver […]

RAG (Retrieval Augmented Generation) Architecture for Data Quality Assessment

A large language model (LLM) is a type of artificial intelligence (AI) solution that can recognize and generate new content or text from existing content. It is estimated that by 2025, 50% of digital work will be automated through these LLM models. At their core, LLMs are trained on large amounts of content and data, and the architecture […]

MDM vs. CDP: Which Does Your Organization Need?

Most, if not all, organizations need help utilizing the data collected from various sources efficiently, thanks to the ever-evolving enterprise data management landscape. Often, the reasons include: 1) Data is collected and stored in siloed systems; 2) Different verticals or departments own different types of data; 3) Inconsistent data quality across the organization. Implementing a central […]