Discover why Shadow AI is reshaping the enterprise, often under the radar. Explore essential statistics, real-world risks, and proven solutions to manage Shadow AI, protect your data, and enable secure innovation in your organisation.
Organisations need to be AI resilient to protect and maximise their investments in AI. Here’s a roundup of how Commvault’s new Cloud Unity Platform is transforming enterprise resilience by unifying data security, cyber recovery, and identity protection to help you achieve AI resilience. Learn about key features, real-world benefits, and why this matters for your organisation.
Join our LinkedIn Live session on 18th November for a candid discussion on enterprise AI’s current state, challenges, and successes. We’ll explore real-world hurdles like AI hallucinations and the gap between investment and implementation. This is a unique opportunity for data leaders to share experiences and gain practical insights into effective AI strategies.
Discover which AI agents actually work in production today and why, and which are still just hype. This post unpacks real-world AI agent successes, common failures, and what every organisation needs for deployment,plus simple answers to the biggest FAQ about AI agents in 2025.
Balancing structured AI and BI project delivery with unexpected business-as-usual demands is vital for data leaders. Discover thoughtful leadership strategies to protect team wellbeing, maintain project focus, and build resilient, responsive organisations.
Amazon Web Services plans a $5 billion investment in South Korea by 2031, focused on AI data centres, complementing an earlier $4 billion partnership with SK Group. This strategic move aims to meet growing AI demand locally, and also AWS has made the smart move of being the ‘plumbing’ that connects everything together, through infrastructure deployment rather than just feature development as Microsoft and Google have been doing.
Trust – but verify! AI hallucinations, which occur when AI generates false information confidently as fact, present significant challenges and risks for organizations. A recent case involving Deloitte illustrates the dangers of unchecked AI, highlighting the critical role of data quality and governance in preventing such issues. Striving for accuracy, relevance, and human oversight is essential to develop trust in AI systems.
Effective AI implementation relies on a well-structured data landscape. Organizations face challenges with “data puddles,” which create silos, “data lakes” that provide organized access, and “data swamps,” which hinder AI initiatives. Success requires governance, metadata management, and purposeful data strategies to transform chaos into a competitive advantage, maximising AI’s potential.
Enterprise data is shifting towards strategic, distributed models, moving away from centralized data lakes, which often become unmanageable. Successful organisations prioritise how data flows and connects, implementing domain-led approaches that enhance autonomy, governance, and compliance. This evolution supports AI readiness, with a focus on metadata management as critical for effective data utilisation and business value delivery.
A comprehensive database design specification is crucial for organisations as it serves as a blueprint for data structure, ensuring alignment among stakeholders. It helps prevent costly redesigns, enhances data quality, supports scalability, and fosters collaboration between teams. Regular updates ensure its relevance. It is ultimately improving business outcomes and reducing development costs for rework.
Fivetran is in advanced talks to acquire dbt Labs, potentially valuing the merger between $5 billion and $10 billion. This consolidation signals major shifts in the data ecosystem, impacting vendor risk and prompting enterprises to reassess their strategies. The merger could enhance integration and drive AI initiatives but may increase vendor lock-in concerns.
The utilities sector is experiencing a profound transformation. Data-driven insights are no longer the exclusive domain of data scientists and IT specialists. Instead, we're witnessing