Cobus Greyling says the attention economy is changing. It used to be about getting human clicks and views. Now, with AI, it's about competing for a different kind of attention: getting AI agents to select and use your services.

Maribeth Martorana says data maturity isn't about dashboards anymore. She outlines a three-stage journey for companies to move from internal savings to creating new revenue streams. The key is to combine data maturity with AI to create valuable, decision-ready intelligence.

Will Sullivan compares two types of AI agents: Agent Workforce, which replaces humans for tasks, and Agentic Orchestration, which helps humans do their jobs better. He explains how these different approaches impact a company's go-to-market strategy, from pricing to team structure.

Veronika Durgin says what we build today becomes tomorrow’s legacy. We should focus on customer needs and business impact. She suggests we learn from the past while embracing new technologies like AI to build a strong foundation.

Dr. Sven Balnojan says most data teams act like expensive consulting shops. They focus on fast tickets and stakeholder happiness instead of business impact. He argues they should be product teams, measuring success by decisions made, not dashboards delivered.

Rob Enderle argues that AI companies are failing users by prioritising hype over reliability. He points to the botched ChatGPT-5 launch as an example. Rob suggests buyers should demand stability, diversify tools, and conduct thorough pilot programs.

Simran Singh Arora says that AI agents need good data to succeed. Many projects fail because data is messy and hard to find. He argues that data catalogs and discovery are crucial for giving AI agents the context and trust they need to work at scale.

Sue Bethanis says that AI adoption is really a human issue. Leaders must become change agents to help people move from resistance to resilience. She believes this requires a mindset of expecting and embracing change, and a practice of empathy to help others.

Hyunuk Lim explores how AI can automate parts of data governance while keeping humans in the loop. The model: AI detects anomalies and patterns, while humans ensure accountability and trust in decision-making.

Why the next wave of AI isn’t one super-agent - it’s a whole digital team that changes how we work, what are the technical and cultural implications?

This article outlines how to assess whether an existing taxonomy can be adapted or if a fresh design is needed & provides practical criteria for making the right choice.

Job postings doubled in 4 months, $10–20M packages for top researchers, Big Tech hoarding talent like it’s a rare Pokémon, how can you prepare?

Iván Palomares Carrascosa explains why data visualisation is so important. It helps simplify complex information and uncover hidden patterns. Plus, it makes data stories more engaging and impactful, so people can make better, more informed decisions.

This is a conversation with author Randy Bean on becoming truly AI-ready, which is featured in a Forbes article, focusing on how winning companies will be those that invest as much in people and culture as in technology, especially since cultural resistance is often a big barrier to AI adoption, making CEO-led workforce upskilling essential.

Heather Hedden discusses taxonomy standards, including SKOS, ANSI/NISO Z39.19, and ISO 25964. She highlights challenges in defining international standards, especially with multilingual terminology and inconsistent translations. Hedden aims for greater consistency in thesauri through a revised ISO standard.

Calloc'h explores ROI‑driven AI strategy, prioritising use cases, adapting systems globally, and navigating complex B2B2C distribution challenges.

Seattle Data Guy and Celina Wong discuss escaping the ad-hoc data request trap. Reactive data teams lack influence. They are seen as input/output machines. Data teams should become proactive. This allows them to be strategic partners.

This is a fast-paced convo with a CIO on cutting through AI hype, real vs fake use cases, “agentic eclipse,” cybersecurity risks, and quantum’s impact, blending tech insights with leadership lessons and quirky.

This post talks about how GenAI is reshaping enterprises, shifting AI from lab experiments to real products embedded in workflows. Success now depends on guardrails, trust, and a hybrid model uniting engineering, data, and product to scale AI responsibly.

Cecilia Oliveira is joined by Jo Portlock to discuss workplace fairness on the Women in Data podcast. Fairness means visibility and access, not advantage. They highlight the importance of mentorship, sponsorship, and strong communities. Data is crucial for accountability and driving change.

Nikhil Singh and Swami Achari clarify Data Product Manager (DPM) and Data Product Owner (DPO) roles. DPMs set the product vision and strategy. DPOs focus on execution and backlog management. Both are crucial for data product excellence. They ensure data is transformed into valuable, actionable products.

Modern Data 101 recently released the Modern Data Survey Report 2024-25. It details how organisations manage and scale data amidst AI. The report identifies pain points like tooling gaps and governance issues. It aims to guide businesses toward effective data ecosystems.

This article explains a 5-point checklist for smarter tech and AI investments, focusing on solving real business problems, aligning culture, rethinking ROI beyond metrics, and prioritising long-term value over hype-driven decisions.

A “Kiss Cam” gone wrong shows how unmasked data can spark chaos, just like exposing raw PII in business. Data masking, RBAC, and anonymization aren’t optional; they’re survival.