What is your primary use case for Collibra Data Intelligence Platform?

Collibra Platform is used for data governance, bridging business and technical data. I used Collibra Platform to import metadata from GCP projects by utilizing Collibra import APIs. Importing metadata from GCP projects helps to make data accessible for everyone at the company so that they can use this data because they have information about metadata.

In my current field, I have been there for nearly five or six years, mostly related to data governance, data quality, master data management, and all the associated things. I have been using Collibra Platform since the last six years and have been an avid user of Collibra Platform for different projects and use cases to implement for different organizations. There have been many use cases for Collibra Platform, and during the initial days, four or five years back, the primary use case used to be business glossary and data catalog and physical data dictionary. Right now the focus is shifting more into AI governance and governance, such as doing data governance through agents, then connecting Collibra Platform to different various platforms to make a unified data governance or AI governance solutions. For AI governance, Collibra Platform is being used to do governance for all the data models, including their explainability, the transparency, the biasness, and all these checks and how we are avoiding that. Governing the entire AI model and the AI frameworks is what Collibra Platform is currently doing in AI governance and what I am currently associated with working in Collibra Platform with AI governance. There has been another major shift with agentic, so you have to also govern your agents. Additionally, you can deploy agents into Collibra Platform to reduce your work time, so this has been the major shift. Apart from this, on the main use cases, there has been more usage of technical metadata, and linkage of technical metadata to business metadata has been a dire need of the present.

My main use case for Collibra Platform, previously from 2023, is supporting data governance and data cataloging. I use it to help structure and maintain business terms, data standards, business rules, data dictionary, and ownership or stewardship information. This mainly supports my work from a domain experience perspective. More recently, I use Collibra Platform from a managed services perspective, primarily for operational support, monitoring, connector health, release follow-up, monthly service reporting, and related activities. From a managed services perspective, my focus with Collibra Platform is on keeping the platform stable and ensuring that the client has clear visibility into how the service is performing. This includes monitoring platform availability, checking connector health, following up on Collibra Platform releases and updates, supporting incidents or service requests, and preparing monthly service reporting. The reporting typically includes uptime, connector status, known issues, incidents, changes, upcoming releases, or relevant platform updates. My work is less about creating governance content day to day and more about operational support, service quality, and ensuring that Collibra Platform setup continues to run smoothly. I maintain the platform in the managed services phase, so recently I made a minor tweak in a workflow by changing some logic based on the client's request. One aspect of my use cases with Collibra Platform is that I work with it as a bridge between business and IT. I focus not only on the technical setup but also on how the content, roles, responsibilities, and reporting support business users. I work with business terms, standards, rules, data ownership, workflows, and data quality visibility, as well as more operational topics such as environments, release monitoring, and service reporting. This combination helps me understand both how business users utilize Collibra Platform and how it needs to be supported from a service perspective.

My main use case for Collibra Platform is for data governance use cases. I use Collibra Platform for automated data cataloging and automated data classification of physical data, which has made a significant difference in our processes. In addition to data governance workflows, Collibra Platform is beneficial for other processes and departments involved in the governance efforts.

My main use case for Collibra Platform is the Collibra Catalog for metadata management. Data classification and metadata management, both technical and business metadata management, are my primary use cases. I use Collibra Catalog for metadata management to import technical metadata from our schemas, and we ensure that the tags are applied for sensitive data and financial data. We also leverage automated workflows.

My main use case for Collibra Platform is reporting governance and building the data lineage by connecting to Unity Catalog and documenting KPIs, dimensions, and business reports. Once those reports are implemented by a developer using this data lineage, we could do end-to-end traceability if some KPIs are shown wrong in the report, then we leverage this data lineage to check the actual data source, the underlying field, table, etc., to do the root cause analysis. For example, we have multiple data sources for building the reports using Collibra Platform, and we plugged in Tableau metadata and SAP Analytical Cloud, which are two of the main data sources for visualization, with our back end being SAP BW and sometimes SAP HANA. When a director from the business sends or documents the report in Collibra Platform, highlighting the requirements and KPIs, this is the first phase of the lifecycle of the report as a candidate or draft. Once it goes to the formal review process with business stakeholders and the data governance council, it is approved and goes for actual implementation to the developers, who implement these reports in BW and visualize them in Tableau or SAP Analytical Cloud platform. If one of the KPIs in a report is shown wrong, then using the data lineage in Collibra Platform, we could see which particular KPI is sourced from which SAP BW or SAP HANA table. Then the developer quickly finds out the root cause, fixes it, and showcases it to the end user using this data lineage capability in Collibra Platform. This is one of the practical use cases we implemented via Collibra Platform. We have some other use cases, such as building the data quality report on certain data assets where we leverage Collibra Platform. For example, we work with the business to document their data assets in the form of data attributes, which are consumed by other businesses, and they set up business rules against those data attributes. Our data quality team creates data quality checks that are documented in Collibra Platform, and using the data lineage, we could see the quality of a specific attribute. We have traffic light indicators, such as green for good and red for an obvious problem, and in cases with certain errors, it is easy for the end user to consume this data attribute for reporting or quality purposes by seeing the data quality scorecard, which helps in deciding whether it is worth using or not. If not, they could trigger a workflow to the end data owner to fix those data quality issues, allowing them to leverage those data assets in their reports or use cases.

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.