Alejandra Cabrera
About Alejandra Cabrera
Alejandra Cabrera is a Data Product Manager at Clearbit, known for enhancing data management by focusing on scalable quality solutions and utilizing advanced technologies like ML/AI and Telmai to ensure high data integrity across millions of records.
Data Product Manager at Clearbit
Alejandra Cabrera holds the position of Data Product Manager at Clearbit. She plays a crucial role in managing and maintaining the quality and freshness of an extensive dataset, which includes 50 million company records, 389 million contact records, and 4.5 billion IP addresses sourced from over 250 different origins. Her work focuses on creating a scalable data quality solution that demonstrates the value of high-quality data to customers.
Data Quality as a Product
Alejandra focuses on treating data quality as a product by applying product life cycle methodologies to data changes. This approach involves continuous improvement and iteration, ensuring that data meets high standards of accuracy, completeness, and validity. By treating data as a product, she aims to proactively identify and resolve data quality issues before they affect data consumers.
Utilizing Telmai for Data Quality Management
Alejandra utilizes Telmai to gain centralized visibility into critical data quality KPIs. These KPIs include completeness, accuracy, validity, and freshness. She chose Telmai for its scalability and its ability to analyze Clearbit’s entire dataset without sampling. Telmai's capabilities allow her to monitor and investigate data quality issues at the record level, measure accuracy, and detect anomalies and data drifts effectively.
Implementing ML/AI for Data Quality
Alejandra implements machine learning and artificial intelligence technologies to detect unknown issues in third-party data. By leveraging ML/AI, she can uncover hidden data quality problems, thus enhancing the reliability and robustness of the data used by Clearbit. This proactive approach helps in maintaining a high standard of data quality continuously.
Developing Data Quality Metrics and Scorecards
Alejandra has developed metrics that measure data quality at the record level, allowing for detailed tracking of data quality improvements over time. She is also working on building a Data Quality scorecard that evaluates eight dimensions of data quality: Accuracy, Freshness/Timeliness, Completeness, Consistency, Integrity, Reasonability, Uniqueness, and Validity. This scorecard aids in systematically managing and improving data quality across Clearbit’s datasets.