As we look forward to 2025, technology – AI in particular – will continue to drive conversations as leaders seek ways to generate impact and build an intelligence-driven ecosystem of insights. Daily human conversations, whether spoken, written or recorded, represent an invaluable source of data insights for businesses. These interactions can highlight customer concerns, preferences, […]
Data Modeling in Machine Learning Pipelines: Best Practices Using SQL and NoSQL Databases
Data, undoubtedly, is one of the most significant components making up a machine learning (ML) workflow, and due to this, data management is one of the most important factors in sustaining ML pipelines. An appropriate data model allows the respective data to be accessible all day long, operate at peak efficiency, and be adjusted to […]
Ask a Data Ethicist: What Is the Appropriate Etiquette When Using AI Tools?
Most of us understand how to behave in different social situations and interact with others in ways that are deemed acceptable. Yet, when we bring AI tools into the mix, we create the capacity for friction because the social norms for using these tools are not yet clear. Which raises this question … What is […]
How Data Will Reshape Industries in 2025
Data has become a driving force behind change and innovation in 2025, fundamentally altering how businesses operate. Across sectors, organizations are using advancements in artificial intelligence (AI), machine learning (ML), and data-sharing technologies to improve decision-making, foster collaboration, and uncover new opportunities. In the upcoming year, the focus is on making data more accessible, protecting […]
Data Sips: Interview with Tony Shaw
Did you know? We’ve partnered with Ippon Technologies to launch Data Sips, a video miniseries showcasing quick conversations with industry experts that took place during last month’s Data Governance & Information Quality (DGIQ) Conference in Washington, D.C. The first episode features DATAVERSITY founder and CEO Tony Shaw chatting with host Steve MacLauchlan, head of data […]
Data Management for Medical Device Clinical Trials: A Guide
Clinical trials for medical devices hold a unique significance due to their direct application in patient care and the rigorous scrutiny they undergo from regulatory bodies. Unlike pharmaceutical trials, medical device trials involve complex interactions between hardware, software, and human factors. This complexity is compounded by the high stakes associated with ensuring patient safety, making […]
The Role of Reinforcement Learning in Enhancing LLM Performance
Large language models (LLMs) are the backbone of modern natural language processing. They predict words, craft sentences, and mimic human language at scale. But underneath their polished outputs lies a limitation: They only replicate patterns seen in their given or training data. What happens when we want LLMs to go beyond this – when they […]
Overcoming Regulatory and Compliance Challenges with Logical Data Management
Businesses engage in mergers and acquisitions (M&A) to gain new capabilities, remove competitors, reduce operational costs, and join new markets. In the first half of 2024, the global value of M&A activity was $1.0 trillion, 4% higher than it was during the same period last year. M&A requires meticulous attention to regulatory and compliance issues. Navigating these […]
Book of the Month: Data Models for Banking, Finance, and Insurance
Welcome to the first “Book of the Month” for 2025. This time, we’ll be going over “Data Models for Banking, Finance, and Insurance” by Claire L. Frankel. This book arms the reader with a set of best practices and data models to help implement solutions in the banking, finance, and insurance industries. Right at the beginning […]
Rethinking Public Sector Data Centers for AI-Driven Digital Transformation
Artificial intelligence has become a cornerstone of public sector modernization, putting a higher importance on data centers, the energy they consume, and how to make them operate more efficiently. Generative AI models require immense computational power to operate. For example, an AI workload like a ChatGPT query can take more than 10 times the energy of […]