Data governance is no longer a responsibility reserved for the back-office of an organization, it should be front of mind and viewed as a cornerstone of organizational success. With the sheer volume of data generated daily, ensuring that it’s managed responsibly, securely, and effectively has become a top priority for businesses.
Merav Yuravlivker, Co-Founder of Data Society, explains her reasoning: “Building a strong data culture starts with leadership and requires clear data governance policies. Everyone within an organization needs to understand their role in handling data responsibly.”
At its core, data governance is about more than compliance. It’s about fostering trust, driving innovation, and ensuring data is a strategic asset—not a liability.
Data governance refers to the frameworks, policies, and practices that ensure data is collected, stored, and used responsibly. It includes establishing:
Without robust governance, organizations face significant risks, including breaches, misuse, and the erosion of customer trust.
“It only takes one misstep to negatively impact someone’s life,” says Yuravlivker. “That’s why clear guidelines and shared accountability are essential.”
As organizations embrace AI, data governance becomes even more critical. AI systems rely on vast amounts of data to function, and the quality, provenance, and security of that data directly impact the outcomes these systems produce.
Yuravlivker highlights a common challenge: “If we don’t know the provenance of the data we’re working with, it’s impossible to ensure it’s being used ethically and effectively. Data governance helps address these gaps.”
A survey by McKinsey found: “63% of respondents characterized the implementation of gen AI as ‘high’ or ‘very high priority,’ yet 91% of these respondents don’t feel ‘very prepared’ to do so in a responsible manner.”
By establishing clear governance policies, organizations can unlock the full potential of AI while safeguarding against unintended consequences. This includes ensuring that data used to train AI models is accurate, representative, and secure.
A strong data governance framework starts with leadership but must permeate every level of the organization. Here are three key steps:
As recommended by Forbes: “With a tailored strategy and a measured approach, organizations can design a scalable governance program that enables data to evolve in the right direction to deliver continuous value.”
To further elaborate, Yuravlivker explains: “Data governance isn’t just about compliance. It’s about creating a culture where data is respected and used as a force for good.”
As data continues to grow, the role of governance will only expand. Organizations prioritizing governance today will be better prepared to adapt to emerging regulations, mitigate risks, and build lasting trust with stakeholders.
The question is no longer whether data governance is necessary but how effectively organizations can implement it to drive sustainable success.
How is your organization approaching data governance, and what steps are you taking to build a culture of responsibility and trust? If you would like a team of experts to have your back, contact Data Society for a consultation.
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