The DoT Blog

  Image by DC Studio on Freepik Dear friends, Yes, it’s true, organizations worldwide are racing to adopt artificial intelligence (AI), but many are tripping over a surprising obstacle: their data. But why? Well, because the very fuel AI relies on, the business data that should inform decisions, is often fragmented, inconsistent, or simply unreliable. This is not a niche issue; surveys consistently show that more than half of business and technology leaders cite poor data quality as a top barrier to AI adoption. The paradox seems clear: companies invest in sophisticated AI models without first ensuring their data is ready to support them. The result? AI projects stall, insights are misleading, and innovation slows. But fixing this doesn’t mean just collecting more data; it means rethinking how data is structured, connected, and understood. So, below are five takeaways that challenge conventional wisdom and reveal how organizations can, potentially, turn data from a liability into a...

  Image by Geralt (Pixabay) In the last couple of years, I have seen one of the most consequential shifts in the modern data platform  landscape market happening quietly but decisively: the point of control is moving away from the database management system (DBMS) and toward the governance catalog. This transition is not accidental. It seems to be driven by three reinforcing forces: the industry’s move toward open data models, the growing necessity of governance in the AI era, and the strategic positioning of major players such as Databricks and Snowflake,   among others. Together, these forces are redefining where power, control, and differentiation sit in the data stack. What follows is a brief look at why these matter and why it is happening now. From Execution Control to Governance Control Traditionally, the DBMS served as the primary control plane of the data platform. It enforced access policies, managed read-write operations, and effectively functioned ...

Brief disclaimer: Given Denodo’s long-standing leadership in data virtualization, it’s no surprise to see them backing this particular data strategy. That said, the focus here is strictly on the methodology and the strategy itself, approached from an independent, vendor-agnostic point of view. As data ecosystems stretch across clouds, regions, and business domains, yes, and GenAI starts demanding fresher, smarter, more context-aware data, the pressure on organizations is reaching a breaking point. The question everyone is quietly asking in boardrooms and architecture meetings is simple: How do we deliver governed, timely, business-ready information without drowning in complexity? Christopher Gardner’s new book, The Rise of Logical Data Management: An Essential Data Strategy for Transforming Your Business in the Age of AI , jumps right into that tension. It’s written as a practical playbook for business leaders and senior technologists trying to navigate today’s messy, distributed lands...

The accelerated evolution of the big data and database markets and the increasing need for managing larger volumes of diverse types of data have favored and created the need for the emergence of so-called data lake management providers. One of these key new players in this market is Zaloni . Zaloni, provides enterprise data lake management, governance and self-service data solutions aimed to ensure organizations to have a clean, actionable data lake. Co-founded and led by Ben Sharma (CEO) & Bijoy Bora (COO) , this North Carolina based software company relies on the experience of its team to provide a reliable solution for managing reliable and efficient data lake services. Recently we have the opportunity to speak with Scott Gidley ―Zaloni’s VP of Product Management― about the company, its solutions and in general, about the big data and data lake markets. Scott leads the strategy and roadmap of the Zaloni platform portfolio. He is a 20-year veteran of the data m...