We present a comprehensive look at the characteristics of a modern AI-powered pipeline, the capabilities it must possess, and the challenges companies face when implementing it. We also analyze how to achieve the right balance between automation and human oversight.
We explore what data maturity really means from a business perspective, how to identify an organization's current level of data maturity, and why advancing AI without a solid foundation can generate more risks than benefits.
We analyze what data stewardship means and entails, and the challenges it presents for individuals and organizations. We also address the role of the data steward and the disruption that artificial intelligence introduces to data governance processes.
We explain what upskilling AI teams means and entails, and the key competencies that need to be developed to fully leverage AI in data management. We also analyze the keys to boosting internal talent growth.
We provide key insights for selecting cases that truly drive business KPIs, using an impact/KPI matrix, feasibility analysis, and risk assessment. This includes a checklist and steps to develop an effective strategy.
In this article, we share a practical guide for CIOs, CDOs, CISOs, data architects, compliance departments, and purchasing teams who need to evaluate AI vendors and define the minimum acceptable requirements to bring a solution into production without falling into failed purchases.
We analyze why many organizations get stuck in the experimentation phase, what the most frequent roadblocks are when trying to scale, and what concrete steps allow AI to be transformed into a real business capability.
The challenge is no longer adopting AI, but scaling it. We analyze how data fabric enables the creation of real business capabilities and positions itself as a strategic enabler for CIOs, CDOs, and data architecture leaders in data-driven organizations.
How to go from data to decisions with AI: minimal architecture, governance, evaluation and steps to implement business-aligned models.
Why is AI-first a viable operating model and a path to business sustainability? We analyze its challenges, potential risks, roles involved, and a roadmap for moving from isolated initiatives to scale.
