Data governance blueprint in the GenAI era

As we move deeper into the world of ML and GenAI, an emphasis on data quality becomes critical. John Jeske, CTO of KMS Technology's Advanced Technology Innovation Group, delves into data governance methods such as data lineage tracking and federated learning to ensure top model performance.

Data quality is key to model sustainability and stakeholder trust. During the modeling process, data quality makes long-term maintenance easier and enables you to build user confidence and confidence among your stakeholder community. The effects of ‘garbage in, garbage out’ are exacerbated in complex models, including large-scale languages ​​and generative algorithms. "Jeske said.

Genetic AI Bias and Data Representative Issues

No matter which model you choose for your use case, poor data quality will inevitably lead to distortion of GenAI models. Pitfalls are often From training data that misrepresents the scope of the company, customer base, or application scope.

The real wealth is in the data itself, not the ephemeral model or modeling structure. Over the past few months , with the emergence of a large number of modeling frameworks, the value of data as a monetizable asset has become more prominent

Jeff Scott, senior vice president of software services at KMS Technology, further explained: "When the content generated by AI is consistent with the expected output When there is a bias, it is not an algorithm error, but a reflection of insufficient or distorted training data

Strict data integrity governance

Best practices for data governance include metadata management, data management and automation Quality inspection and other activities. For example, ensure the reliability of data sources, use certified datasets when acquiring data for training and modeling, and consider using automated data quality tools. Although this may add complexity, these tools are very helpful in ensuring data integrity

To improve data quality, we use tools that provide properties such as data validity, integrity checks, and time consistency, which Promotes reliable, consistent data, which is essential for robust AI models.

Accountability and continuous improvement in AI development

In everyone’s eyes, data is a problem. Within a company, assigning responsibility for data governance is an important task

The most important thing is to ensure that features work as designed and that the data being trained on makes sense from a potential customer's perspective. Feedback enhances learning, which is then taken into account the next time the model is trained, invoking continuous improvement until the point of trust.

In our workflow, AI and ML models undergo rigorous internal testing before being launched publicly. The data engineering team receives continuous feedback, allowing iterative improvements to the model to minimize biases and other anomalies

Risk Management and Customer Trust

Data governance needs to be implemented in relevant business areas Conduct data management and require ongoing involvement of subject matter experts to ensure data across teams and systems is appropriately curated and consistently accountable

Must understand the risks associated with receiving inaccurate results from technology, companies Transparency must be assessed, from data origins and handling of intellectual property to overall data quality and completeness.

Transparency is integral to customer trust, and data governance is not just a technical exercise, it can also impact a company’s reputation as risks are transferred from inaccurate AI predictions to end users.

With the continuous development of GenAI, mastering data governance has become increasingly important. This is not only to ensure data quality, but also to understand the complex relationship between data and AI models. This insight is critical to technological advancement, business health and maintaining the trust of stakeholders and the wider public

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