Today, AI is reshaping industries with its unprecedented scale and transformative impact. At the heart of the AI revolution is the principle of ‘trusted data,’ a cornerstone for successful AI initiatives. Trusted data — characterized by its accuracy, reliability and integrity — is not just a requirement but a catalyst for transformative changes across organizations.
You can see the changes happening already. In the financial sector, AI is a game-changer, leveraging predictive analytics for market trends, risk assessment and customer service optimization. In customer service, the integration of AI, steered by the principle of trusted data, is transforming customer interactions, with AI-powered tools scaling and automating personalized engagements, marking a significant shift in the customer experience. For marketing teams, generative AI is revolutionizing how content of all types is produced — from written assets to visual graphics.
However, the integration of AI into data products — in other words, any product that uses data to achieve an operational or business goal — carries inherent risks, especially as AI efforts scale up. Effective AI governance, with a strong foundation in trusted data, is vital to harness AI’s potential for innovation and to ensure compliance with ethical standards, regulatory requirements and best practices.
Managing data effectively is a challenging yet crucial aspect of AI application development and management. The newest generative AI technologies, capable of creating human-like language, are only as effective as the data they are based on. If the data is flawed, the AI models trained on that flawed data will yield outputs that are seemingly impressive but fundamentally unsound.
The consequences of bad data can lead to significant implications for companies developing AI applications, including biased decision-making, inaccurate recommendations, and risks related to security, privacy, legality and organizational reputation.
To mitigate these risks and ensure trusted data, it’s important for organizations to embrace comprehensive AI governance practices.
Need trusted data for your AI initiatives? Explore our AI governance framework.
AI starts (and ends) with trusted data
You want to transform AI into value to fuel your business. But your AI models are only as good as the data they use.
At Collibra, our mission is to unite your entire organization with trusted data – including the data that trains your AI models – that’s easy to find, understand and access so you can do more with it.
To drive successful AI initiatives, AI governance is a crucial cornerstone for organizations that want to initiate or enhance AI initiatives, turning them into operational and competitive strengths.
For AI and data leaders, our AI governance framework provides a practical, repeatable process that empowers your AI roadmap to drive substantial value.
Read our blog, AI governance: Why our tested framework is essential in an AI world.
Collibra AI Governance
As the demand for AI applications grows, the need for AI governance is becoming increasingly urgent.
At Collibra, we are excited to support organizations in achieving their AI objectives, drawing from our extensive experience in data management.
For AI and data professionals driving AI initiatives, Collibra Data Intelligence Platform can help your organization:
- Quickly find, understand, and trust data: With a well-structured data catalog and governance, users can easily locate the data they need, understand its context, and have confidence in its quality. This efficiency saves valuable time and resources. You can’t start training AI without the data — but with data intelligence, you can hit the ground running.
- Drive a common language: Data governance facilitates the creation of a common language around data within an organization. This shared understanding speeds up decision-making, promotes collaboration and fosters a data-driven culture.
- Leverage automation: By integrating data governance with automation tools, organizations can keep pace with the rapidly evolving AI landscape. Automated processes ensure consistent adherence to data standards, enable efficient data discovery and enhance data quality.
- Mobilize workforce collaboration: Data governance encourages collaboration among various stakeholders, including data professionals, business users and IT teams. By breaking down silos and promoting cross-functional collaboration, organizations can unlock the full potential of their data assets. Anyone who has ever managed to put AI into production knows it is an intense team sport.
- Ensure compliance and mitigate risks: Data governance helps organizations meet regulatory requirements, such as data privacy regulations (e.g., GDPR) and industry-specific standards. By implementing controls and monitoring mechanisms, organizations can mitigate risks associated with data breaches and non-compliance.
If you’re an AI-driven organization (or striving to be one), now is the time to implement AI governance as not only a risk-mitigating element of your efforts but as a catalyst for creating scalable AI with trusted data.
Looking to understand common AI governance terminologies better? Our AI Governance Glossary, expertly curated by Collibra’s data experts, serves as a crucial resource for anyone interested in or engaged with AI governance.
Explore Collibra AI Governance to understand how trusted data can transform your AI initiatives into sustainable success.
