Enterprise Generative AI (GenAI) and Large Language Models (LLMs) programs struggle with sourcing high-quality training data. Without reliable data, AI models produce biased or inaccurate results, undermining the entire program’s credibility. Additionally, ensuring privacy and compliance with regulations like GDPR and CCPA poses significant challenges. This means that GenAI and LLMs thrive on a foundation of quality data. The Alex Automated Data Catalog becomes the cornerstone, offering a unified view of your data landscape. Catalog data items, link them to glossary terms, and ensure a cohesive understanding that serves as the bedrock for AI model training and development.
Confidence in AI outputs starts with traceability. The Automated Data Lineage within the Alex platform becomes your assurance, tracing the journey of data from its origin to the AI models. Alex provides a detailed map of how data flows through your organization, ensuring that every data point used by your AI models is traceable back to its source. This traceability is crucial for understanding the context and quality of the data used in AI models, enhancing the interpretability of AI outputs. Visualize dependencies, track changes, and ensure that every data point used by your AI models is not just accurate but also traceable, reducing the risk of inconsistencies and enhancing the interpretability of AI outputs.
Poor data quality significant challenges enterprise GenAI programs, affecting their accuracy, reliability, and overall effectiveness. The accuracy and reliability of AI outputs are compromised when the data used for training is of poor quality. Inaccurate data can lead to incorrect patterns and trends being identified by AI models, resulting in flawed insights and decisions. Alex Automated Data Quality ensures that your AI data meets the highest standards. Implement precise governance rules, visualize data dependencies, and enforce data quality, setting the stage for AI models that are not only powerful but also accurate. This helps reduce the risk of using inaccurate or unreliable data in AI models, improving the overall precision and reliability of AI-driven insights.
Generative AI and LLMs often involve complex models that interact with vast datasets. With Alex, empower your data science teams to ensure consistency across models. Gain insights into potential impacts, visualize dependencies, and ensure that each model is trained on a consistent, high-quality dataset. This consistency not only enhances the performance of your AI models but also increases their business value.
Generative AI and LLMs thrive on a foundation of quality data. The Alex Automated Data Catalog becomes the cornerstone, offering a unified view of your data landscape. Catalog physical data items, link them to glossary terms, and ensure a cohesive understanding that serves as the bedrock for AI model training and development.
With Alex, enterprises can implement robust data governance policies to ensure ethical AI practices. By tracking data lineage and enforcing compliance with regulations, Alex enables organizations to build Generative AI models that respect privacy and adhere to ethical standards.
Alex empowers organizations to build data literacy and Generative AI skills through its enterprise-wide business contextualized data governance. By providing a unified view of data assets, policies, and workflows, Alex enhances understanding and trust in data. This enables teams to safely and effectively use Generative AI technologies, detecting and mitigating pitfalls such as hallucinations.
Confidence in AI outputs starts with traceability. The Automated Data Lineage within the Alex platform becomes your assurance, tracing the journey of data from its origin to the AI models. Visualize dependencies, track changes, and ensure that every data point used by your AI models is not just accurate but also traceable, reducing the risk of inconsistencies and enhancing the interpretability of AI outputs.
With Alex automated data lineage, delve deep into the origins of AI insights. Trace where certain AI outputs derived from in datasets down to the column-level. Understand the transformations, joins, and calculations that led to these outputs, ensuring that your AI models are not only accurate but also transparent in their decision-making process.
Alex's automated column-level lineage enables granular impact analysis for AI development. Understand how changes to specific data elements or columns impact AI models, allowing for more precise and targeted model improvements. This level of detail ensures that your AI models evolve with your data landscape, maintaining their accuracy and relevance over time.
Alex Data Quality Dashboards provide a comprehensive view of your data quality, giving you insights into the health of your data. Visualize data quality metrics such as completeness, accuracy, consistency, and timeliness, allowing you to quickly identify areas that require attention. With the Rating Dashboard, you can ensure that your data meets the standards required for successful AI development.
Alex's Automated Data Quality solution allows you to enforce data quality policies and workflows, ensuring that your data is clean, consistent, and reliable. Define rules and guidelines for data quality, and automate the process of data quality assessment and remediation. With Alex, you can establish a robust data quality framework that supports your generative AI initiatives.
The Alex Business Glossary enables you to govern your data with full business context, thanks to its rich, automated, and fully customized ontology. By defining and categorizing your data assets according to your business needs, the glossary provides a clear and comprehensive view of your data landscape. This enables you to make informed decisions about your data, ensuring that it is used in a way that aligns with your business objectives and regulatory requirements.
The Alex Business Glossary enables automated Data Playbooks that are connected to all relevant data, policies, and more. These Data Playbooks provide a comprehensive view of your data ecosystem, allowing you to track data lineage, understand data quality, and make informed decisions about your data assets. By leveraging the power of the glossary, you can ensure that your AI initiatives are built on a foundation of trusted data, leading to more reliable and accurate outcomes.
Alex can massively reduce the time and effort required to execute key data governance workflows like data quality issue impact analysis. Having an automated detection and analysis platform supercharges data quality improvement.
Looking for key data to feed into your GenAI? Use Alex to find the data you need wherever it sits in your data stack. Assess its trust and start using it – fast.
Never use poor data again. Alex enables you to easily identify an impacted table, compliance issue or low quality source. Instead of creating inaccurate GenAI outputs, you can kick off remediation immediately within the lineage with native, automated workflows.
Alex automates compliance processes, so you can focus on AI value over regulatory risks.
Alex's provides rich insights into data's context, enhancing the accuracy and value of AI outputs.
Alex's automated data quality anomaly detection and controls maintains AI model standards.
Directly apply easily configurable risk and compliance policies, workflows and rules on anything in the catalog.
