Data First

Unleashing the Power of Generative AI

How Businesses can Fully Leverage Generative AI using Data Quality

It’s hot AI Summer, and Generative AI is what everyone is talking about. What is it? How can they use it? It’s exciting to dip one's toes into the Generative AI pool and see what it returns. Whether exploring tools such as ChatGPT or BARD or implementing some version of a Generative AI model within your own organization, one thing is clear – the opportunities are endless.

A recent McKinsey report identified at least 63 possible use cases across 16 business functions where generative AI “can address specific business challenges in ways that produce one or more measurable outcomes.” For some, those measurable outcomes translate to an estimated potential impact of $200 billion to $340 billion annually for banking and $400 billion to $660 billion a year for retail and consumer packaged goods “if the use cases were fully implemented.”

Generative AI can potentially be transformative for organizations and their people, yet successful implementation can be a little less clear. It requires a sound foundation of quality data – an overwhelming endeavor without certain best practices.

Data Quality: Building a Foundation for Generative AI

Improving data quality is the first step to fully leveraging the potential of generative AI technology. Best practices provide foundational steps that companies can take today to address data quality concerns and bring the business into the next generation of AI technologies.

Data Governance and Standardization: Establish a robust data governance practice to ensure data is collected, stored, and managed consistently across the organization. Simply standardizing data formats, naming conventions, and definitions can prevent data discrepancies and improve the accuracy of AI model outputs.

Data Cleansing and Preprocessing: Regularly clean and preprocess data to remove duplicates, inconsistencies, and errors. Implement data validation checks and data profiling techniques to identify and address data quality issues proactively.

Data Integration and Centralization: Integrate data from various sources into a centralized repository. This will ensure a comprehensive and holistic view of data, making it easier for AI models to access accurate and relevant information.

Data Profiling and Quality Metrics: Use data profiling tools to analyze and assess the quality of data. Define data quality metrics and establish thresholds for acceptable data quality levels.

Continuous Monitoring and Auditing: Implement regular data monitoring and auditing processes to track data quality over time. This will help identify trends and potential issues that need to be addressed.

Metadata Management: Maintain comprehensive metadata to provide context and insights into the data. Proper metadata management enables better understanding and interpretation of the data used by AI models.

Expert Data Input and Feedback: When it comes to large-scale, global data management initiatives, involve the subject matter experts in the movement and maintenance of data. Not only can this expertise streamline and optimize the project, but it can often help remediate data quality concerns and provide valuable insights and context for AI models.

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The Importance of Data Quality in Generative AI

Data quality plays a crucial role in the success of generative AI. Clean and accurate data is the foundation on which the enterprise AI solutions depend upon, ensuring reliable outcomes. Companies must start collecting and documenting data, metadata, business rules, and processes as part of their data quality initiatives. Without these foundational elements, AI models can't provide accurate and meaningful insights. By investing in data quality improvement initiatives, businesses can build a solid foundation for successful AI implementation.

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