Insights
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.
In this article we organize the discussion around three key axes: the different possible modernization routes, the main trade-offs that condition decision-making, and the specific criteria to evaluate each scenario, with metrics and a roadmap adjusted to the maturity level of each organization.
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.
