Before we talk about AI, let’s talk about your data

More businesses are addressing their digital transformation initiatives by discussing how AI can help add value to their technical stack.?With ChatGPT leading to increased interest in #GenerativeAI, businesses are looking to see how this technology can fit into their enterprise workflow.?However, many times technology and business leaders are skipping an important step—looking at their Enterprise Data Hygiene.?Before?talking about Generative AI, it’s necessary to review your Enterprise Data.

Generative AI models heavily rely on the training data they receive to generate meaningful and reliable outputs. It only makes sense that you would work to “purify” your data ?to ensure that the data used to train these models is of high quality, accurately representing the desired outcomes. By implementing data validation, cleaning, and normalization practices, you can improve the accuracy and reliability of your Generative AI models. ?That’s why it’s so important to focus on your data governance and bring in the right tools to help.?For example, AWS provides services like AWS Glue and AWS Data Pipeline to facilitate data preparation, transformation, and validation. These services help ensure that your training data for Generative AI models is accurate and of high quality, improving the reliability and effectiveness of the generated outputs.

Data governance helps you navigate the complex landscape of data protection regulations and maintain the security of sensitive information. Setting up your data governance provides a secure and compliant infrastructure that can help you meet various regulatory requirements when you transition into working with AI. By implementing data governance practices, such as access controls, encryption, and auditing mechanisms, you can ensure compliance and protect your data. ?#AWS offers a wide range of services to help maintain compliance with data protection regulations. For example, Amazon S3 enables you to securely store and access your data, while AWS Key Management Service (#KMS) provides encryption and key management capabilities. AWS CloudTrail allows you to audit and monitor access to your data, ensuring compliance and enhancing security.

Generative AI models may require access to personal or sensitive data. Data governance enables you to implement privacy safeguards by anonymizing or pseudonymizing data to protect individual privacy and prevent the exposure of personally identifiable information (PII). It is important to build this governance before any project and before problems cascade.?Without the proper governance, transformational projects like Generative AI can compound issues.?You can use the tools that AWS offers to facilitate data privacy, giving you control over how data is handled and accessed.

AWS provides services like Amazon Macie, which automatically discovers, classifies, and protects sensitive data. By leveraging features like data anonymization and pseudonymization, available through AWS services, you can protect individual privacy and prevent exposure of PII when working with Generative AI.

Generative AI models have the potential to inherit biases present in the training data, leading to unfair or discriminatory outcomes. Data governance practices can help identify and address biases by carefully curating and evaluating training datasets. It is important to address these issues within your data automation. Amazon SageMaker Clarify can help identify and mitigate biases in your datasets. It provides model examinability and fairness testing capabilities, allowing you to ensure that your Generative AI models produce fair and unbiased outputs.

Generative AI has the potential to create realistic content, including text, images, and videos. Data governance plays a critical role in establishing ethical guidelines and ensuring responsible use of Generative? AI technology. It helps define usage boundaries, identify potential risks and limitations, and establish accountability mechanisms. By implementing ethical guidelines through data governance, organizations can prevent misuse of Generative AI technology and uphold ethical standards.

Optimizing your data to ?take full advantage of Generative AI can be challenging.?Therefore, it’s helpful to have outside experience, whether that be trusted colleagues or a good message board you can reference while planning.?Alternatively, you can work with solution architects from AWS or look to leverage outside consultants.?It is important to build a team of resources that you trust to execute your mission. If you are looking for guidance please feel to reach out to us at? Oxford Global Resources .