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Organizations increasingly depend on accurate insights from theirdatato drive decisions, fuel innovation and maintain their competitive edge.
Yet, the ability to extract meaningful, high-quality insights from this data is dependent on effective data governance.
Implementing data governance is critical, but like all data initiatives, it requires internal adoption and organizational fit.
Generative AI is emerging to transform the way organizations streamline data management processes.
It is a strategic framework that ensures data is accessible, secure and aligned with organizational goals.
Data governance relies on four core pillars for success.
The first is having people to define and execute the policies and standards.
Chief Product Officer, Ataccama.
This diversity, along with the growing volume of data, makes integration, management and effective use difficult.
They may also lack the skills to follow data governance policies.
It can transform data governance in several ways.
This not only enhances data quality but also strengthens security and promotes seamless integration across systems.
Data trust and its role in governance
Data trust is the mission-critical consequence of effective data governance.
In decentralized models, individual business units retain autonomy while following governance principles.
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The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
