Artificial intelligence (AI) governance

AI starts with trusted data

AI governance

Overview
Framework
Stats
Benefits
Resources

AI without trusted data isn’t intelligent – it’s a recipe for disaster. Collibra powers organizations that need the highest-quality data for the highest-quality AI. With active metadata at its core, Collibra Data Intelligence Platform serves as the foundation for your AI strategy. Govern AI with the proper rules, processes and responsibilities to drive maximum value by ensuring streamlined and ethical AI practices that mitigate risk, adhere to legal requirements, and protect privacy for unparalleled productivity gains.

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Collibra drives data governance – the start of your AI governance journey.

As it is for all data governance initiatives, it’s critical to know where to start with AI governance. Our team of AI experts and data scientists have developed a framework as an easy-to-implement guide to getting your AI governance program in place. Below are the four steps for managing and governing AI across its life cycle.

Define use case

Capture ideas from the business, assess feasibility and define the AI use case, including the data and model(s) leveraged and the intended purpose. Set desired business outcomes and KPIs, assess risks and assign ownership and accountability.

Identify and understand data

Collect and assess the data that’s available, whether or not it’s high-quality/certified, and whether its use in connection with the use case is legally permissible.

Document models and results

Document, trace and track the model, associated data products and usage, allowing for model analysis and reporting.

Verify and monitor

Verify your model and continuously monitor to ensure the quality and compliance of the underlying data products. Retrain, test and audit models regularly.

Data intelligence fuels data and AI governance

Underlying AI governance is the documentation, assessment and monitoring of the data products that feed into the models. At each step of the framework, data intelligence capabilities come into play. Because AI is driven by data, AI governance fits into your larger, enterprise data governance plan.

Define use case

Identify and understand data

Collect and assess the data that’s available, whether or not it’s high-quality/certified, and whether its use in connection with the use case is legally permissible.

Document models and results

Document, trace and track the model, associated data products and usage, allowing for model analysis and reporting.

Verify and monitor

Verify your model and continuously monitor to ensure the quality and compliance of the underlying data products. Retrain, test and audit models regularly.

Data intelligence fuels data and AI governance

Are you ready for AI?

72%

Leaders who said problems with data are most likely to jeopardize the achievement of their AI goals between now and 2025.¹

78%

Leaders who said scaling AI and ML use cases to create business value is their top priority over the next three years.¹

70%

Internally developed apps that will incorporate AI or ML.²

AI governance delivers impact across your organization

High-quality AI output

Training your AI model shouldn’t be a one-time event. AI governance helps you continuously monitor and retrain models for continued high-quality AI output.

Increased productivity

AI governance helps you deliver high-quality AI to automate customer service, process documents and even create content for your organization.

Business growth

Properly governed AI can add tremendous value and growth across the organization, helping to improve customer experience (CX), improve product development and other revenue-driving use cases.

Compliance and trust

AI regulations like Europe’s AI Act are fast approaching and will have heavy penalties for those out of compliance. An AI governance framework helps you put the right policies and procedures in place to keep compliant and retain the trust of your customers.

The hardest part is not the technology, it’s [the] data products. It's this practice that you're going to commit to, to being data driven, and that starts from the top.

Teresa Tung, on the topic of AI governance and implementing AI

Cloud First Chief Technologist

Accenture