The main use case has been drafting business glossaries, data dictionaries, assigning ownerships, and prioritizing data elements.

Once a data element is prioritized, we assign ownership on the basis of seniority or the basis of expertise in a particular field. Then we assign the ownership and wait for the owner to confirm or approve their ownership from their end.

I have also created some data quality rules on Collibra Platform to monitor the accuracy, timeliness, and completeness of the data. Apart from that, I have also tried to create some workflows and some policy measures, tried to create some data products, and data contracts as well.

Collibra Platform has been used by my organization for various clients for internal company data cataloging. When we have governed the data, when we have cataloged the data, and given it a business glossary name, after that, we have been able to build many use cases on AI, and that has been really helpful.

Right now, we are also working with an AI use case or making an AI chatbot to answer quickly and to have the metrics quickly. To build that, we need to catalog some amount of data related to around 45 to 46 reports. First of all, we don't have to do it manually. We can enter the data element in Collibra Platform, and then we can define the data type or the data definitions.

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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.

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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.

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My main use case for Collibra Platform is that we use it on a day-to-day basis. We configure workflows for clients, and we have multiple clients for which we work. Our task is to manage the metadata of their company or design workflows. These two are the major components that we work with.

One specific example of how I use Collibra Platform for managing metadata or designing workflows is that some people have access to certain information only within the company. We help them configure workflows where only a few people have access to that particular data. Apart from that, whenever they have different definitions across different departments, we help them create a data dictionary and business dictionary within Collibra Platform so their data could be unified.

This is just one of the use cases, as it depends upon the client what they ask for. We have previously worked with data lineage as well, creating policies and AI governance. We have shown them how it could benefit their AI infrastructure. Those are the other core areas we have worked upon.

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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.

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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.

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My main use case for Collibra Platform is data lineage and data traceability, as it has extensive functionalities around those tasks.

Collibra Platform is particularly powerful for enterprise data governance. Currently, my company is undergoing a migration process between one database and another, so we use Collibra Platform mostly for data governance tracking.

When we need to find equivalences between one data platform and another, we can search among data sets and metadata. We can also assign ownership or stewardship.

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Collibra Platform is used for data cataloging, business glossary, data lineage, DQ integration, workflow automation, custom workflow automation, policy and governance management, role-based access, and stewardship workflows.

My day-to-day use of Collibra Platform involves working on the glossary, working on the lineage, developing custom workflows according to requirements, and aligning the whole process according to business needs.

My day-to-day use cases include asset approval workflows, triggering workflows for business users for bulk approval of their assets, change management, and the associated approval processes. Connectivity and metadata ingestion are also part of my day-to-day work.

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I have used Collibra Platform for metadata management, which is a data intelligence platform. I have also used Collibra DQ platform for checking data quality purposes as part of data governance.

I built metadata management by identifying data owners and data stewardship, including producers and consumers, and created workflows for approvals. I also established data lineage to understand the source, target, and how producers and consumers are linked.

Using Collibra Platform for data lineage and workflow approvals has impacted my daily work by allowing me to understand where data comes from and what systems are involved, how data is persisted until it reaches the target, and who is the right person to make any changes using workflow approvals to identify producers and consumers and their linkage.

Additional use cases with Collibra Platform would include monitoring data quality rules and scorecards, and assigning remediation tasks whenever there is a failure or the score decreases.

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My main use case for Collibra Platform is for a banking client in the Netherlands, who wanted to create a centralized system to organize data assets, define ownership, ensure data quality, and enforce governance policies.

This was for a banking client in the Netherlands, and Collibra Platform is used to govern the customer balance. Without Collibra Platform, we faced issues because retail and risk and finance teams all calculated the balance differently, resulting in inconsistent reports. With Collibra Platform, we defined the correct customer balance in a business glossary and assigned a data owner such as the head of retail or head of finance. It helped us show the lineage from core banking to data warehouse reports and apply access rules, authorizing teams to see sensitive data related to the customer. Basically, it was for reporting, risk analysis, and regulatory compliance.

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Implementing Collibra Governance is definitely something that all organizations having data need. It's the kind of tool especially for large enterprises where data is distributed across multiple data stacks, bringing rigor for both data management and data engineering.

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The main use case for Collibra Lineage is focused on the data product integration. They want the data lineage, data product lineage, and data products. They have some AWS platform in their state, and they have implemented external layers on top of that, including iceberg tables. The data lineage and delta are used to write a model with data products in Snowflake. In the Snowflake layer, they have their reporting in Tableau.

They have implemented a model with data products in Snowflake, which helps them in reporting in Tableau.

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We have a data platform and we use Collibra Catalog as enterprise governance tools. We are using Collibra Catalog for basic functionality.

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My main use case for Collibra Platform is data cataloging as well as AI governance.

In my day-to-day work, I connect with data sources such as Snowflake and a variety of data sources like Hive, and then use the metadata to define the data dictionary, while also using Collibra Platform to define the business glossary and tie it up with the metadata so that any user logged in can understand what the data describes. For AI governance, I have a lifecycle where the user submits a workflow to get approved for their AI use case, and then I use that workflow to analyze the use case and the associated data, completing a data quality check using Great Expectations before approving the use case if there is no risk associated with the data. I use Collibra Platform for both AI governance and data catalog.

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I used Collibra Catalog primarily for cataloging all the assets. We created data dictionaries by ingesting data from multiple databases into Collibra Catalog and setting up workflows for review and approval processes.

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I work as a data consultant for IBM Bluetab company, and my role involves data analysis, data engineering, and conveying messages in terms of research and reports.

I have been using Collibra Platform for over two years, and I find the platform to be very unique in terms of data governance, as it is very aligned to most of my needs.

I use Collibra Platform primarily for searching domains and all information from my data catalog. Currently, we are in the process of migrating databases from Oracle to Azure. I type mostly definitions, check who has ownership of a field or domain, and search for categories when I need to find similarities between one data warehouse and another type of database. I use the artificial intelligence tool set to receive hints on words and make traceability of where certain data could be located. I use most of those cases.

A specific example of how I used Collibra Platform recently involves traceability needs. The platform allows me to input information regarding where a certain field within a table begins, and then I need to understand what the equivalent is in another database. I typically search for the name I already have in mind, from which I can see details about ownership, the tables it is linking in that position, and check definitions. I get everything summarized in tables and can also see more details about the parent table and relative tables, so I could benefit from the diagrams they offer.

I have additional insights about how I use Collibra Platform in my work. For example, when I know the term but specifically need to get more insights about what could be the next definition deriving from this term, it would be great if I could get suggestions from the formation of two terms so I would not need to search in other places.

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I have been working with Collibra Lineage for more than five years now, overall almost five years. I am using the product myself for molecular consultants and resellers at a consulting firm.

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We are a customer of Collibra. Collibra Governance is applied in our data management workflows by creating all the business functions separately, maintaining the data sources of those KPIs and everything separately on those businesses.

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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.

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It is mainly for documentation purposes, to document metadata in a data catalog, and also to improve understanding, access, and exploration of data.

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I use the Collibra Lineage visualization features. The solution helps me understand data flows effectively. Collibra Lineage impacts my data analysis by helping to identify where data is used, making it significantly useful. The solution facilitates compliance by defining where sensitive data is located, helping to trace sensitive data.

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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.

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We use Collibra Catalog for data catalogs, data classifications, and all aspects of data categorization. My focus is on metadata, particularly how easy it is to extract an application's metadata and integrate it into Collibra Catalog. View full review »

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.

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I am an implementer working as an enterprise cloud and AI security architect. While working with BT clients from Accenture, they were using Collibra Governance on on-prem infrastructure and wanted to integrate it into Google Cloud Platform (GCP). We compared it with GCP's Data Catalog to determine which one was better. View full review »

We created custom data models in Collibra Lineage for domains and communities. Under each domain, we created databases and linked data dictionaries. We set up workflows by linking controls, which were attested by control owners quarterly. All of this was automated.

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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.

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The use case for Collibra Catalog is for metadata management.

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My domain is master data management. In master data management, basic data quality, lineages, rule profilings, and similar processes come under Collibra profiling. I use it for these purposes.

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My use case for Collibra Lineage is for data governance; the purpose is to manage and govern our data effectively.

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The primary use case for Collibra Catalog in my organization is for documenting all the metadata that we have in the company. Apart from that, I am also focusing on many different layers on it, including data lineage, data quality, business glossaries, and access management workflow.

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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.

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There are different use cases, including integration with other applications, setting up operating models for specific business domains, such as finance or manufacturing, defining domain structures, and implementing data quality rules and scorecards. The specific use cases can vary from client to client, but they generally involve leveraging Collibra's capabilities to establish a strong governance framework and improve data management across the organization.

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The product’s primary use case is metadata management. It helps us capture different datasets, including images, glossaries, etc.

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Collibra is for the classification of data. Collibra is being sourced to all the intakes for the company, which might be legacy systems or new systems or might be lineage as well. Collibra takes up and does its own governance over there, and the data is fed into Ataccama. Ataccama and Collibra seem to be cascaded, but Collibra is the first player. But that doesn't mean it saves a lot for Ataccama.

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I am using Collibra at one of my clients. We implemented Collibra there as well.

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Collibra Governance is used as a data governance tool to manage your business metadata and technical metadata from various systems. Customers also use the tool to provide a data cataloging option, create a data lineage, and automate some of the business processes.

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Collibra Governance is used to maintain and govern our company's data and evaluate the data quality. 

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We use the solution's Data Stewardship part. We tried to explore the policy management piece, but we were unable to get through it. We haven't worked excessively on that. It was just some POCs or MVPs, but this is something that we would like to explore in the future. This is something that will be our future requirement in six to seven months down the line.

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We use the product for classification based on the different regulations and compliance requirements.

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I use Collibra Catalog for cataloging various reports, data elements, and tables, along with schema, associated with all the various attributes of the element itself, like business rules and sensitive data.

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We primarily use the solution for when the customer needs data governance to establish cases. We're choosing the product in order to meet the requirements of the customer. That includes requirements such as establishing a governance council if the customer does not have one. It's for data management and the solution handles data quality and metadata management, et cetera. 

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Collibra Governance has many use cases. I'm part of the enterprise data team and I also focus on other types of data enterprise, wherein governance is one of the workstreams. I'm working for a pharmaceutical company, so I try to onboard people on Collibra Governance from various verticals such as HR, vaccines, pharma supply chain, etc. Sample use cases for the solution include data quality for cloud stream software, automated data lineage, and other types of lineage. In general, I'm using it mainly for data governance.

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The primary use case for the solution is the governance of data dictionaries. It is useful for managing and classifying data on a large scale.

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Catalog is primarily used for metadata cataloging and enriching it with additional information, such as adding business terms. Then, if lineage support is available in the tool, we can link business terms to technical terms. 

Additionally, we can store sample data. If there's a client requirement, we can link physical metadata with reports or datasets. This allows users who need access to a particular dataset to obtain it by creating a dataset from the Catalog metadata.

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In my company, we use Collibra Governance for data catalog.

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Collibra is generally used as a data governance tool, maintaining all metadata repositories. For each and every data that has been cataloged in Collibra, there'll be an owner assigned to it, whether it's a subject matter expert, a data streamer, data owner, etc. All the data is catalogued into Collibra. It has a single source of truth for data. I'm a senior Collibra consultant and we are customers of the company. 

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Our major use case for Collibra Governance involves maintaining lineage, as well as linking business terms and technical terms together. Then, our use revolves around linking policies with actual data, such that we can create data sharing policies within the tool, storing data and quality results.

I am currently working on about 10-12 projects with Collibra Governance using the latest cloud version. It is available on both cloud and on-premises, but currently all our solutions are moving to the cloud.

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Collibra Governance is mainly used as a one-stop solution for data discovery.

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We used Collibra Lineage mainly for end-to-end lineage. Additionally, we wanted to use it for business velocity data cataloging and data quality.

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There is a master glossary which includes all of our business fields. The fields are related to particular lines of business such as account numbers. We then build business rules around these fields and complete data integrity checks. 

We plan to increase our usage of this solution over time. We will have to use other functionality within this application as it is currently limited. We currently have 12 team members using this solution. 

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We've used it for cataloging or defining our business data entities and their associated attributes or data elements. Where appropriate, we are defining their sources and their modes of derivation. Rising above that, we are also using Collibra for the definition of our business metrics that are calculated from those data elements and the derivation definitions associated with them.

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We are focused primarily on the Data Governance Catalog (DGC) for our data dictionary use. We are not using it for information governance in terms of regulatory compliance, etc. We are focused on business glossary, data catalog, data dictionary, and some workflow processes to help with metadata management and other things.

We are fully updated, and we are using its latest version.

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The solution that I had worked on was related to the technical implementation of metadata for capturing analytics. That said, that particular implementation missed the bus with using it for business use and getting a proper buy-in from the users.

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I use the solution to link the technical and business terms and see the lineage between different technical terms. To track data flow, we start on the report level if there is a compliance requirement. We can see which data set a particular resource is using. We can see how the data table for the data set is created. We go back and trace the source. We try to define it from the source to the target end to end.

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We mostly use Collibra Governance for governance-related tasks like building business glossaries and creating policies.

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We use the solution to organize data. The IT team has access to the data and knows where it is stored, but when it comes to legal compliances and various regulations, the business itself does not have control over where the data is stored and how it is stored. Typically, the business wants to have control over where the data is stored, the various data classifications, and data sensitivity. That is the main reason why we use Collibra Governance.

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I primarily use Collibra Catalog for data management and governance. It lets you build data catalogs, making sure you've got the right definitions for business terms so that everyone's aligned. It also ensures that the correct roles and responsibilities are assigned and the correct data stewards are assigned to the correct data set.

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We use this solution for workflows and for specific use cases. For our retail and insurance clients, we meet governance requirements by using all the features that Collibra provides. We do most of the work using workflows and connectors. We build connectors where the data will be extracted from a third party tool and  input in Collibra.

We have 40 to 50 team members using this solution. We have three admins who manage this solution. 

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I have been working on Collibra for a data governance assignment. Along with that, we also use Ataccama for data quality purposes. It is a Collibra/Ataccama mixture that we use.

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Collibra Governance can be deployed on-premise and in the cloud. The solutions can be used for lineage data quality, business glossary, and data cataloging.

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We use Collibra for IT data governance, specifically for GDPR compliance. Our company is in the banking and financial institution sector and we are customers of Collibra. 

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We use it for compliance and data quality.

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We use the solution mostly for a client. There are a port catalog and dashboard cataloging features that they use. That said, the principal use case, even though there are a number of use cases, is using it as a business glossary for terms throughout the institution.

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Previously, I was in high-tech, now I'm in healthcare. In both instances, we used this solution to increase data literacy and business literacy. In short, we were building out business glossaries, coming to an agreement on term definitions, understanding what reports mean, what the metrics and calculations mean — essentially getting it all documented in an enterprise-facing view. That was the bulk of the focus in my prior company — along with some reference data. At my current company, we are focused heavily on bringing in data dictionaries, including schema tables and columns, and understanding what data is in which database. We also look at what data is flowing from database to database and how it's used in Tableau reports or in a business or archive.

Currently, we're on-premises, but we're planning on moving to the cloud later this year.

Over the last 12 months, we've had 643 unique new users. Most of our users tend to be what I would call "data geeks". They are the ones that understand the value of and/or the aspect of leadership to catalog their metadata for their data dictionaries, etc. The business users have not had as much adoption at my current company because that's not the particular strategy at this time. At my prior company, that was the singular strategy — to increase business literacy.

In regard to my previous company, for eight years, our primary customers were business users, not technical users. They just wanted to come in and say, "What does it mean? Who do I talk to if I have questions? Where do I go for more information? How does this relate to that, and how do those things affect me in my business world?" That was a fantastic use case at my prior company. In my current company, we're utilizing the tool specifically to support our data management strategy. Our technology risk office is very much supporting us in the effort to do the cataloging of all of the metadata for all of the systems that we're utilizing, cross-functionally.

Maintenance must be defined. As with any software product that exists, there are regular maintenance upgrades that occur that the company pushes out. If you're on-prem, you can choose to do it sooner versus later. In the Cloud, they push it to you unless you state that you wish to wait. From a maintenance perspective, I have talked to other customers who are in a cloud environment. They say that the maintenance has basically dropped to nil. For on-prem customers, it does require a little more maintenance because obviously, you've got to make sure that the servers are up and running, etc. In my prior role at my previous company, I was responsible for the business IT side of it, which meant I kind of managed the platform. I just didn't manage the server.

I took care of the customers, etc. I'm not even a technical person at all, but still, it was not difficult for me. From a maintenance perspective, I don't feel that it is difficult.

If you have custom integrations that have been created, then it will require a little bit more maintenance because those integrations need to be monitored. Some customers require a lot of integration, and some customers don't. It really depends upon the use case. Custom integration is really where it gets more challenging. Depending on whether it's a one-way integration or a two-way data feed, it can get pretty complex. Two-way data feeds are always custom. But that's something that will be on the customer anyway, not Collibra.

We've only just relaunched this solution within the last 15 months. Currently, we only have about 240,000 assets in the tool, but we have a roadmap and plan to onboard a number of our customers. We'll probably double or triple our users over the next 12 months. 

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I'm not a part of the tech team. I'm definitely part of the master data management, wherein we are providing services with respect to master data. Our services are focused on just the business-specific areas such as pharma commercials and pharma supply chains, and we have also built the lineage in R&D areas.

In some specific use cases, we are mapping the processes, the respective sources and basically using the entire asset model of Collibra, starting from process, to attributes, to data modeling. We are tracking everything in Collibra.

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Our use cases include connecting a lot of legacy data systems to our logical components. For example, if somebody has a question that they post to us and say, "Tell me everywhere in our organization where we have a policy stored?" the primary use case is to logically define what a policy is, and then we use Collibra to tie that logical construct to a technical implementation. We may have six or eight, however many, different admin systems. We bring in the schemas of the way that those systems look, and then how a policy exists in this database and this table and this column, for example, in that legacy system. 

The second use case that we implement is the ability to track the provenance or the lineage as to how something changes over time. For example, if we bring data in from a legacy system and we use some tool set (e.g. Azure Data Factory) to extract the data into a Hadoop data lake, and then perform some transformations on it, we want to be able to track it; "It came from the source system here, and this field got changed to this name, and we applied this transformation on this field and it eventually shows up on this report here."

We use it to track where a policy exists and also how it got there: it exists on this report and here's how it got on that report, here are all the steps that it took getting through to that particular report from the actual source system itself. Because quite often what we're finding is that our business users will get a report and they'll say, "I think your report's wrong. How did you get that value on that report?" That provenance or lineage is what helps answer those questions.

We have data stewards who are the resources that if somebody proposes a new logical asset based on what they think the customer means, these data stewards are the ones that would get together and look at what's being proposed and make sure it works across all of our business units for a generic implementation, or create business unit specific terms if required. They're the ones that say a particular system or term or logical construct is ready for consumption by end users.

Another group we have is the end users. We try to have people use Collibra by asking, "Don't tell me what system you want to get access to, tell me what you're looking for in business terms/constructs." In our example, it would be the question, "Tell me about all the policies in our system." They would go to Collibra and "shop" for that data and pick a policy and put it into the shopping cart basket that Collibra provides as part of their interface. Then they would submit that request for approval/access to the underlying data.

We also have data stewards who approve the use of new/updated business terminology and end users who are looking for their data to make business decisions. We also have some power users who are the resources who are setting the direction for the application of where we want to go with it, (e.g. new workflows or new functionality within Collibra).

For us, the Collibra application is an on-premise installation (although we use IaaS VMs to host it on cloud); it is not their SaaS implementation.

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I've been working with multiple companies, but with two of the companies we have been using Collibra mostly for data governance. With these companies, our use case is all about metadata governance, lineage, and data-related policy management. We're doing policy management directly inside Collibra and we're also using it for issue management on the analytics side.

If someone has a data concern, they just call me in and then put that concern into Collibra as a front-end UI for the data stewards and data scientists, and we start processing them.

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My company, a financial institution, decided to implement data governance for data analytics, et cetera. We designed an entire metadata management system using Collibra. Initially, we designed the metadata management, and then we enabled the receivership at the organization-level and also roles and responsibilities. After that, we created the lineage between the technical and business assets, and we automated the process of insertions and updates.

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The main use case for our organization was to collect and collate information for our business users, such as definitions, business rules, etc. 

The customers' names were stored in a data warehouse on Oracle, and with this product, you could insert information related to the customer. You could use Excel, for example, to add in data. You'd have all the information you collected from other places on one tool that users could navigate across to see all of the information.

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I am using it essentially for the GDPR implementation over here in Europe. This is my second project on Collibra. Before that, I have worked on the CCPA part for a US-based project.

I have worked on an on-prem solution and a solution on the cloud. I was the one who had created all the components on AWS because our client was not ready to move onto Collibra's cloud solution, but I believe they will be moving to SaaS soon.

We are on version 5.7.5. Version 5.7 is the latest, but because we're not on SaaS, it's quite tedious to upgrade each and every environment. We have four environments or five if you include what the developers work on, so it is a bit tedious to upgrade. 

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We have a client that was planning to do a digital transformation. They came up with the use cases and they have a couple of departments. They are mainly based on legal systems including corporate trust, etc., that they want the data to govern. Their requirement was basically if any new person is hired, they would have everything in Collibra, including all of the business glossary or the tables or the reports. Basically, everything for their onboarding process was available there. It was also necessary to give that to anyone that wanted to know about their department.

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I worked with this solution a few months ago and the main use case of Collibra was as the central metadata tool for the enterprise. The plan was to have all of our data dictionaries, our business glossary, to expose data lineage through Collibra and show all of the relationships and connections between our various tables and databases and the actual semantic business layer, in Collibra. It was a way to unify our technical metadata with our business metadata and with our actual applications.

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Our clients are typically using it for metadata management. From a broader perspective, Collibra establishes an excellent foundation on which to build an enterprise data governance program. 

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We have a full-time user interface in-house.

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Collibra Governance helps us set the business rules and define critical data elements.

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I am part of implementing the workflow through it, setting up the glossaries, and importing the assets to Collibra Governance. 

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We use Collibra Catalog for the business glossary.

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Our use case is to manage master data inside of Governance.

The solution is deployed on cloud. We have around 10 data analysts using the solution.

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We are using the product mainly for metadata management as well as some data governance practices such as roles and responsibilities set up. We're also using different workflows - mainly approval workflows or request access workflows where people can actually request access to data. We have use cases based on different teams, based on what they need. It's mainly the glossaries, such as business glossaries, data dictionaries, and report catalogs.

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I don't use it as an individual. It's basically for my clients. I serve as a consultant for the tool. The typical use cases for the clients that we get is around metadata management and around building lineage. You can create a lineage by modifying out-of-the-box Collibra asset models.

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We use it for metadata management, data lineage, and automated governance processes. I am working on the SaaS deployment, but I have also worked on the on-premise deployment. I am currently using its latest version.

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We're a very large pharmaceutical organization so it's difficult to quantify exactly how many users are using Collibra.

Within our organization, there is a data governance team that we had set up. I am the person in charge of that team. While working on the data governance processes, we thought of leveraging Collibra for things like data dictionaries, developing data lineage, ensuring that business artifacts like KPI catalogs and board catalogs can be built within Collibra itself. 

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Our primary use case of this product is for metadata information for our technology assets. We also use it for all the governance dictionaries, and for minutes as well.

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The primary use case is generally about IT people trying to manage their metadata to control what data their organization has and where it's going, as well as to manage data quality and integration. Ideally, that needs to be tied to their business outcome, but that's still quite a rare use of the product. I'm the president of our company and we are partners with Collibra.  

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