I am a user who has worked at companies that use Collibra Platform as their data catalog and data intelligence platform tool. My first company, MetLife, used Collibra Platform, but I wouldn't know where they purchased it from because I joined after the implementation. Then I worked at HCL Tech, and one of our clients, Genmab, a pharmaceutical company, also used Collibra Platform. I was onboarded onto that project after the licensing and purchase were completed. I worked at MetLife, which is an insurance company with different lines of businesses including US business, EMEA business, and LATAM. Based on the different geographies and lines of businesses, we needed to ingest the metadata of insurance products. Insurance, as part of the financial services industry, is highly regulated and must comply with regulations such as GDPR, HIPAA, and BCBS 239. Many insurance companies have faced heavy fines when they failed to comply with regulations, experienced customer data leaks, or had privacy breaches. Having data governed became critically important. The main purpose of data governance in any industry is to have a single source of truth. For example, at PeerSpot, if you ask what a customer means, one person may have a specific definition while someone else may have a different definition. However, as an enterprise, you would want to define what a customer is, establish which attributes a customer should have such as customer ID, the date the customer was onboarded, and the revenue generated from that customer. Data governance ensures that every organization has a single source of truth and users have a common vocabulary. The main challenge organizations face today is that business and IT are often at odds with each other. Business uses the data while IT generates it, leading to constant debates about ownership, control, and governance. My role as a data governance consultant was to build a bridge between the business and technical folks, and between business stakeholders and IT staff. We achieved this by leveraging Collibra Platform. We started by creating the community structure. Community structure means organizing the metadata based on lines of business or geography. We created communities based on the geography and line of business. For the different communities we built, I worked closely with the US business data governance council, and most of our work was for the US business. Within the US business, there were many sub-lines of businesses, each of which had a business glossary. A business glossary is a container that contains all the business terms used in an organization. Every business term would have a definition, indicate which column it is stored in, and describe what business rule governs the business term. Next, we ingested the technical metadata, which is called a physical data dictionary. Technical metadata includes schemas, tables, and columns. Collibra Platform has a unique functionality called Edge. Edge extracts metadata and registers any source such as databases stored in SQL, Oracle, Snowflake, and Fabric, which are all different sources we worked with. Collibra Platform has a tool using Edge, and the benefit is that the Linux servers are stored on your organization's server. For example, at MetLife, Collibra Platform's Edge servers would be stored on our MetLife cloud only, not externally. The organization is assured that their data is safe. Using Edge, we extracted the technical metadata of schemas, tables, and columns. We created the business glossary and the physical data dictionary, then ingested the business rules and the data quality rules. Finally, we created a mapping specification using field mapping to create a lineage. A lineage is something which every stakeholder looks for and represents the flow of data or metadata from different sources to targets. In a typical scenario, metadata starts from a system of record or SOR, flows to a raw data zone or RDZ, then to a curated data zone or CDZ, and finally to a distribution data zone or TDZ. These are four different layers, and some organizations use a Medallion structure with gold, silver, and bronze levels. A lineage gives users a visual sight of where the metadata is coming from and where it is going. Lineage helps with impact analysis. If an organization experiences a security breach and does not have Collibra Platform or data governance in place, they will be wondering where the data can be impacted and what customer data could be leaked, requiring reactive analysis. However, if lineage is already established, when a bug or ransomware hits systems, we already have lineage in place and can mitigate the downstream systems so that before data reaches them, we can pause dashboards or disconnect connections. We can understand the impact as soon as an incident occurs, allowing us to be proactive rather than reactive. This is why lineage is so critical to have in place beforehand. For each of my different customers within MetLife, including those working on different insurance products such as long-term disability, short-term disability, and accident and health insurance, my role was to create the business glossary, the physical data dictionary, the business rules, the data quality rules, and ultimately the lineage.
Data scientist at a wholesaler/distributor with 1-10 employees
Real User
Top 20
Dec 5, 2025
Collibra Platform serves as the central place to document, govern, and understand our data assets. I use Collibra Platform in my day-to-day work to build out a business glossary and the data catalog to describe our key data assets.
In the energy sector, Australia is currently undergoing a rapid transformation with ESG reporting coming into place, and we needed a platform that could support our cloud migration, data modernization, and collaboration across business units by breaking down the data silos and enabling self-service analytics, AI use, and efficient data sharing. We needed a tool that could help us with mission-critical applications where we required asset performance management, improved customer experience management, and enhanced finances. Our use case was to use Collibra Data Intelligence Platform to manage and govern our energy data effectively and safeguard our data quality so that we could meet the compliance requirements and unlock the true value of the data required to achieve our sustainable energy goals.
Informatica Administrator EDC, Axox, PC, MDM at itcinfotech
MSP
Top 5
May 21, 2025
Regarding my most common use cases for Collibra Data Intelligence Platform, I can describe them clearly. The platform provides me with data cataloging features, which is really helpful.
I work with Collibra Data Intelligence Platform. I am experienced in both platforms, but more experienced with Collibra Data Intelligence Platform. I have experience creating workflows, harvesting technical lineage, and working with Data Governance along with data quality.
Collibra Platform is preferred for workflows, data lineage, and a user-friendly interface. It enhances metadata management with robust collaboration, flexible customization, and powerful reporting, aiding organizations in effective data management.Collibra Platform provides dependable solutions for metadata management, data lineage, and governance. It strengthens data governance with cataloging, glossaries, automation, and integration, supporting compliance and data quality management....
I am a user who has worked at companies that use Collibra Platform as their data catalog and data intelligence platform tool. My first company, MetLife, used Collibra Platform, but I wouldn't know where they purchased it from because I joined after the implementation. Then I worked at HCL Tech, and one of our clients, Genmab, a pharmaceutical company, also used Collibra Platform. I was onboarded onto that project after the licensing and purchase were completed. I worked at MetLife, which is an insurance company with different lines of businesses including US business, EMEA business, and LATAM. Based on the different geographies and lines of businesses, we needed to ingest the metadata of insurance products. Insurance, as part of the financial services industry, is highly regulated and must comply with regulations such as GDPR, HIPAA, and BCBS 239. Many insurance companies have faced heavy fines when they failed to comply with regulations, experienced customer data leaks, or had privacy breaches. Having data governed became critically important. The main purpose of data governance in any industry is to have a single source of truth. For example, at PeerSpot, if you ask what a customer means, one person may have a specific definition while someone else may have a different definition. However, as an enterprise, you would want to define what a customer is, establish which attributes a customer should have such as customer ID, the date the customer was onboarded, and the revenue generated from that customer. Data governance ensures that every organization has a single source of truth and users have a common vocabulary. The main challenge organizations face today is that business and IT are often at odds with each other. Business uses the data while IT generates it, leading to constant debates about ownership, control, and governance. My role as a data governance consultant was to build a bridge between the business and technical folks, and between business stakeholders and IT staff. We achieved this by leveraging Collibra Platform. We started by creating the community structure. Community structure means organizing the metadata based on lines of business or geography. We created communities based on the geography and line of business. For the different communities we built, I worked closely with the US business data governance council, and most of our work was for the US business. Within the US business, there were many sub-lines of businesses, each of which had a business glossary. A business glossary is a container that contains all the business terms used in an organization. Every business term would have a definition, indicate which column it is stored in, and describe what business rule governs the business term. Next, we ingested the technical metadata, which is called a physical data dictionary. Technical metadata includes schemas, tables, and columns. Collibra Platform has a unique functionality called Edge. Edge extracts metadata and registers any source such as databases stored in SQL, Oracle, Snowflake, and Fabric, which are all different sources we worked with. Collibra Platform has a tool using Edge, and the benefit is that the Linux servers are stored on your organization's server. For example, at MetLife, Collibra Platform's Edge servers would be stored on our MetLife cloud only, not externally. The organization is assured that their data is safe. Using Edge, we extracted the technical metadata of schemas, tables, and columns. We created the business glossary and the physical data dictionary, then ingested the business rules and the data quality rules. Finally, we created a mapping specification using field mapping to create a lineage. A lineage is something which every stakeholder looks for and represents the flow of data or metadata from different sources to targets. In a typical scenario, metadata starts from a system of record or SOR, flows to a raw data zone or RDZ, then to a curated data zone or CDZ, and finally to a distribution data zone or TDZ. These are four different layers, and some organizations use a Medallion structure with gold, silver, and bronze levels. A lineage gives users a visual sight of where the metadata is coming from and where it is going. Lineage helps with impact analysis. If an organization experiences a security breach and does not have Collibra Platform or data governance in place, they will be wondering where the data can be impacted and what customer data could be leaked, requiring reactive analysis. However, if lineage is already established, when a bug or ransomware hits systems, we already have lineage in place and can mitigate the downstream systems so that before data reaches them, we can pause dashboards or disconnect connections. We can understand the impact as soon as an incident occurs, allowing us to be proactive rather than reactive. This is why lineage is so critical to have in place beforehand. For each of my different customers within MetLife, including those working on different insurance products such as long-term disability, short-term disability, and accident and health insurance, my role was to create the business glossary, the physical data dictionary, the business rules, the data quality rules, and ultimately the lineage.
Collibra Platform serves as the central place to document, govern, and understand our data assets. I use Collibra Platform in my day-to-day work to build out a business glossary and the data catalog to describe our key data assets.
In the energy sector, Australia is currently undergoing a rapid transformation with ESG reporting coming into place, and we needed a platform that could support our cloud migration, data modernization, and collaboration across business units by breaking down the data silos and enabling self-service analytics, AI use, and efficient data sharing. We needed a tool that could help us with mission-critical applications where we required asset performance management, improved customer experience management, and enhanced finances. Our use case was to use Collibra Data Intelligence Platform to manage and govern our energy data effectively and safeguard our data quality so that we could meet the compliance requirements and unlock the true value of the data required to achieve our sustainable energy goals.
Regarding my most common use cases for Collibra Data Intelligence Platform, I can describe them clearly. The platform provides me with data cataloging features, which is really helpful.
I work with Collibra Data Intelligence Platform. I am experienced in both platforms, but more experienced with Collibra Data Intelligence Platform. I have experience creating workflows, harvesting technical lineage, and working with Data Governance along with data quality.