Insights
We analyze what a turnkey modernization project entails, why the first months of assessment are crucial, and how responsibilities are distributed between the organization and its technology partner.
What changes when a model must answer to a regulator? What kind of metrics can be used to assess fairness and potential biases? What information should be prepared before an audit?
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.
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.
