If you’re evaluating alternatives to Teradata, you’re not alone. Rising renewal costs, one global bank budgeted for a 9% increase and saw actual costs land between 12% and 23%, are pushing more enterprise data teams to ask whether their current platform still fits where their data strategy is headed, particularly with AI workloads now part

Every Teradata renewal conversation eventually lands on the same question: why did the number go up this much? The honest answer is usually a combination of three factors, and most teams only ever look closely at one of them. Footprint: The Obvious Culprit, But Not the Only One Footprint, the total volume of data sitting

Teradata renewal costs are outpacing what most IT budgets planned for. Organic data growth, expanding regulatory requirements, and new AI workloads are all pushing spend upward, often well past forecast. One global bank serving 11 million customers across 34 countries budgeted for a 9% increase in Teradata renewal costs. The actual increase landed between 12%

An AI agent that cannot connect to the systems where business actually happens is just a sophisticated chatbot. The intelligence is there. The value is not. AI agent integration is the work that changes this. It is the process of connecting autonomous AI agents to the enterprise applications, databases, APIs, and business tools that hold

Global Capability Centers have become one of the most significant structural shifts in how large enterprises organize their operations. What began as a cost-containment strategy of moving back-office work to lower-cost geographies has evolved into something fundamentally different: wholly-owned strategic hubs where companies concentrate their best engineering, AI, analytics, and product talent to drive competitive

Data migration is one of the highest-risk activities in enterprise technology. Moving data from one system to another (whether from on-premises to cloud, legacy database to modern platform, or one vendor’s stack to another) creates significant exposure: data loss, integrity failures, downtime, compliance violations, and cost overruns. According to Gartner, more than 83% of data

Every piece of AI-generated content (a chatbot response, a marketing image, a line of code, a synthesized voice) comes from a generative AI model. These systems learn the patterns, structures, and relationships in existing data and use that learning to produce entirely new outputs that did not exist before. Unlike traditional AI models that classify,

Most AI systems are built to respond. They receive a prompt, generate an output, and stop. Agentic AI works differently. It receives a goal, breaks it into steps, uses tools and data to act, monitors its own progress, and adjusts when something goes wrong, all without waiting for a human to tell it what to

Consumer packaged goods is a volume business with thin margins and almost no direct line of sight to the end customer. A grocery chain, not the manufacturer, controls the shelf, the price tag, and most of the data about who actually bought the product. That gap is what CPG data analytics is built to close.

Three weeks after a mid-sized insurer finished migrating its policy database to a new platform, a renewals analyst pulled a routine report and found premiums that didn’t match what customers had been billed. No one had touched the numbers. Nothing had crashed. The migration had simply “finished”  the load completed, the project closed, and everyone