How to Improve Data Quality for Generative AI – Tejasvi Addagada
Download as PPTX, PDF0 likes1 view
Tejasvi Addagada shares expert insights on how to ensure data quality for successful implementation of Generative AI. Learn how data governance, quality checks, and privacy enhancing technologies play a key role in building trustworthy AI systems. Discover best practices to manage data risk and improve AI outcomes with a robust data management strategy. Watch now to elevate your AI initiatives with high-quality data.
How to Improve Data Quality for Generative AI – Tejasvi Addagada
- 1. ENGLISH VIDEO LESSON DATA QUALITY FOR GENERATIVE AI SUCCESS HOW TO ENSURE
- 2. Generative AI is transforming industries from healthcare to banking and beyond. It helps automate processes, create intelligent content, and support decision making. However, one often overlooked but crucial component of AI success is data quality. Without clean, accurate, and governed data, even the most advanced AI models can fail. This article explores the vital role of data quality generative AI, why it matters, and how organizations can ensure their data meets the standards required for successful AI applications.
- 3. THE CRITICAL LINK BETWEEN DATA QUALITY AND GENERATIVE AI Generative AI models learn from existing datasets to generate new content, insights, or predictions. If the input data is flawed, biased, or inconsistent, the output will be unreliable. From our experience in enterprise AI implementations, we have seen that poor data quality leads to inaccurate models, flawed predictions, and a lack of user trust. On the other hand, when the data is reliable, AI delivers consistent, high-impact results.
- 4. Data Quality and Governance is Imperative for AI Success To maintain high data quality, a solid data governance strategy is essential. Governance includes setting rules, roles, and responsibilities for data use and management. It helps in standardizing data sources, formats, and access permissions. Key elements of a data governance strategy: • Data ownership and stewardship • Metadata management • Quality standards and validation rules • Data lifecycle management • Access control and auditing By integrating governance with AI development, organizations can ensure their models are trained on consistent, trustworthy data.
- 5. Unlocking Generative AI: The Role of Data Quality A trained generative AI model depends on the richness and integrity of its training data. For example, in one project with a financial institution, we saw significant model improvement just by removing outdated and duplicate records. This drastically improved the model’s predictions and reduced bias. Clean, diverse, and up-to-date data ensures that the AI: 1.Learns from accurate patterns 2.Produces relevant and reliable content 3.Avoids repetition and hallucinations 4.Adapts effectively to new inputs 1.Data Profiling: Understand the current state of your data—identify gaps, errors, and inconsistencies. 2.Data Cleansing: Fix issues such as missing values, duplicates, and incorrect formats. 3.Data Enrichment: Add external or complementary datasets to improve variety and coverage. 4.Standardization: Align data into consistent formats and structures across systems. 5.Validation Rules: Set rules to prevent bad data from entering systems in the first place. A Guide to Improving Data Quality in Generative AI Here are some foundational steps to ensure high-quality data for generative AI:
- 6. Remediation of Data Quality Issues Addressing data quality issues should be a continuous process. Based on our field experience, these are some effective remediation methods: 1.Automated tools that detect anomalies using AI 2.Manual intervention for complex issues like semantic conflicts 3.Ongoing monitoring through dashboards and alerts 4.User training to prevent incorrect data entries 5.One client we worked with used AI tools for anomaly detection and reduced their data errors by over 60% within three months. 1.Data Profiling: Understand the current state of your data—identify gaps, errors, and inconsistencies. 2.Data Cleansing: Fix issues such as missing values, duplicates, and incorrect formats. 3.Data Enrichment: Add external or complementary datasets to improve variety and coverage. 4.Standardization: Align data into consistent formats and structures across systems. 5.Validation Rules: Set rules to prevent bad data from entering systems in the first place. A Guide to Improving Data Quality in Generative AI Here are some foundational steps to ensure high-quality data for generative AI:
- 7. How Does Data Quality Impact Generative AI Performance? Data quality directly affects model performance in several ways: 1.Accuracy: The model produces better results when trained on accurate data. 2.Fairness: High-quality data reduces bias in generative content. 3.Efficiency: Clean data speeds up training and reduces computational cost. 4.Trust: Users trust AI outcomes when the data behind it is sound. 1.Completeness – Missing data limits AI learning. 2.Consistency – Uniform data across systems reduces confusion. 3.Validity – Data must follow specific rules (e.g., date formats). 4.Timeliness – AI needs up-to-date information to be relevant. 5.Accuracy – Mistakes in data will reflect in AI-generated outputs. 5 Key Factors Linking Data Quality and Generative AI In simple terms, garbage in, garbage out, AI is only as good as the data it learns from.
- 8. Understanding Data Quality's Significance Data quality is more than just a technical metric it is a strategic asset. Without reliable data, AI initiatives can lead to poor decisions, reputational damage, or even compliance violations. In sectors like healthcare, banking, and public services, we have seen how data quality impacts everything from service delivery to legal risk. That is why aligning data quality with a clear data governance strategy and data risk management framework is crucial.
- 9. Conclusion For Generative AI to truly succeed, data quality must be a top priority. From data profiling and standardization to governance and risk management, each step plays a critical role in ensuring AI outputs are accurate, fair, and actionable. By investing in a clear data governance strategy and ongoing data risk management, organizations not only boost AI performance but also build trust in their digital transformation journey. Now is the time to treat your data like the valuable asset it is, because with data quality generative AI, better data means better outcomes.
- 10. Thank you