I work with Data Hub as a user, but I also have some administrative responsibilities there. I'm not a final user; the final users are business users, and I play some administrative roles in the tool to have the metadata information available for all Uber users. I'm a Data Quality Engineer focused on data governance. I manage the metadata information for Uber, and I also use this to apply some data quality rules. My focus in my current job is to apply some rules and manage the metadata information and ensure it is accurate for the end users, which is why I'm using it.
My main use case for Data Hub is to enrich the metadata to classify for PII data. As an administrator, I crawl a number of data sources and bring the metadata into a single place, then assign the ownership, such as a data owner or steward, for all the data assets. With their help, we classify the data into PII direct and indirect, sensitive, non-sensitive, and so on. We add tags and glossary terms onto the data elements. The main use case is for DSAR compliance; for GDPR DSAR compliance, we try to identify the PII data in the catalog so that we know where the PII data is in our data inventory.
We adopted Data Hub in the context of a large enterprise customer operating in a regulated industry with a strong focus on data governance, data discoverability, and ownership clarity across multiple cloud-native platforms. The solution was deployed on AWS, and the main business problem was the lack of a centralized, reliable view of data assets, including poor data discoverability, unclear data ownership and stewardship, limited lineage visibility across ingestion and transformation layers, and high dependency on tribal knowledge held by a few individuals. Data Hub was selected as an enterprise data catalog and metadata backbone with the goal of enabling both technical teams and business users to easily understand, trust, and reuse data. We used Data Hub to create very good data discoverability, assign data ownership and stewardship, improve data quality processes, and establish good data governance for our customer in terms of data catalog, data lineage, and metadata management in general.
My main use case for Acryl Data is to extract insights from customer data. I use Acryl Data for a project in order to identify all the customers and find out which customer buys a lot of items.
Data Hub is an advanced platform designed to streamline data management processes, enhance data accessibility, and provide comprehensive analytics capabilities for informed decision-making. Data Hub offers a unified approach to handling large-scale datasets, empowering organizations to effectively manage, analyze, and extract insights from their data infrastructure. It provides robust features for data integration, storage, and visualization, supporting diverse business needs and driving...
I work with Data Hub as a user, but I also have some administrative responsibilities there. I'm not a final user; the final users are business users, and I play some administrative roles in the tool to have the metadata information available for all Uber users. I'm a Data Quality Engineer focused on data governance. I manage the metadata information for Uber, and I also use this to apply some data quality rules. My focus in my current job is to apply some rules and manage the metadata information and ensure it is accurate for the end users, which is why I'm using it.
My main use case for Data Hub is to enrich the metadata to classify for PII data. As an administrator, I crawl a number of data sources and bring the metadata into a single place, then assign the ownership, such as a data owner or steward, for all the data assets. With their help, we classify the data into PII direct and indirect, sensitive, non-sensitive, and so on. We add tags and glossary terms onto the data elements. The main use case is for DSAR compliance; for GDPR DSAR compliance, we try to identify the PII data in the catalog so that we know where the PII data is in our data inventory.
We adopted Data Hub in the context of a large enterprise customer operating in a regulated industry with a strong focus on data governance, data discoverability, and ownership clarity across multiple cloud-native platforms. The solution was deployed on AWS, and the main business problem was the lack of a centralized, reliable view of data assets, including poor data discoverability, unclear data ownership and stewardship, limited lineage visibility across ingestion and transformation layers, and high dependency on tribal knowledge held by a few individuals. Data Hub was selected as an enterprise data catalog and metadata backbone with the goal of enabling both technical teams and business users to easily understand, trust, and reuse data. We used Data Hub to create very good data discoverability, assign data ownership and stewardship, improve data quality processes, and establish good data governance for our customer in terms of data catalog, data lineage, and metadata management in general.
My main use case for Acryl Data is analytics.
My main use case for Acryl Data is to extract insights from customer data. I use Acryl Data for a project in order to identify all the customers and find out which customer buys a lot of items.