We explore why there can be significant differences between proposals that address the same need, what aspects are not shown in the price and implementation timeframe, what the main selection criteria are, and why choosing a technology partner involves looking beyond the immediate project.
Many organizations invest in ambitious governance frameworks that end up being difficult to implement. We propose an approach that moves from concept to implementation.
We present the results of the Data Governance Maturity Study and identify the gaps that most impact the ability to scale AI, comply with regulations, and reduce operational risks.
Personal data protection: obligations, third parties, and evidence. What to request and how to audit
In a context where data circulates between multiple systems, teams and third parties, we propose a practical approach on how to move from declaration to control, and from control to evidence.
We analyze an international treaty that proposes a structural response to the challenges of the digital age, establishing common principles that seek to protect people without hindering technological innovation. Understanding its scope is key to anticipating the direction data regulation will take in the coming years.
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 share a practical look at how to use the 7Rs framework to streamline your portfolio and build a modernization roadmap that combines speed with control.
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.
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