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.

Our energy company was struggling to maximize our asset data governance, and as a result, there were inefficiencies across our teams. We embedded Collibra Data Intelligence Platform's data governance suite into our data management system, and as a result, we had a structured, standardized approach which ultimately benefited us. We obtained consistent, high-quality asset data across our departments. Additionally, it helped our business users across the organization have faster access to reliable data. Furthermore, it helped us enhance our data transparency, which supported us in achieving better operational designs and helped us in risk mitigation.

Over a period of consistent use over the year after implementing Collibra Data Intelligence Platform, we saw around 40% reduction in time across the organization, across users who were searching and validating data.

Collibra Data Intelligence Platform is highly regarded for its great data governance and data intelligence capabilities. Sometimes the user interface could be made a little bit easier. When it comes to integration depth, Collibra Data Intelligence Platform does integrate with a lot of popular data tools and platforms, but it would be helpful if they could extend deeper native integrations with many new emerging cloud-native data ecosystems. They have introduced AI-driven features, including a semantic model and automatic DQ issue detection, but if they could further expand their AI automation in terms of predictive data governance recommendations, that could further drive the efficiency and proactive data management. Pricing and licensing are always a concern for organizations. Nowadays, organizations would like to see more flexible licensing models or transparent pricing tiers so that it aligns better with the management funding budgets. Those are a couple of areas where Collibra Data Intelligence Platform as a product offering could be further improved.

I have been using Collibra Data Intelligence Platform for around four years now.

Collibra Data Intelligence Platform is stable in my experience.

Its scalability is decent.

The Collibra Data Intelligence Platform support team is very knowledgeable, but sometimes the response time can vary depending on the complexity of issues and service tier. More proactive and faster support, especially during critical incidents, could increase user confidence and help us reduce our downtime. Regarding documentation, Collibra Data Intelligence Platform has extensive documentation, but users find the materials overly technical or dispersed. Collibra Data Intelligence Platform has its university, and I have done the university trainings, but they are very high-level, not deep. Informatica, on the other hand, has its entire walkthroughs and live demonstrations of practically anything. For instance, you can go to Informatica's YouTube channel, type anything, and find videos on old on-premises versions of EDC and Axon or their latest IDMC cloud versions. Informatica has around 150 videos of each tool, for instance, how to scan this kind of database, how to scan that kind of source system, how to scan this kind of unstructured data, how to create this lineage, how to debug this, and how to solve that. Collibra Data Intelligence Platform has no YouTube presence and hardly anything. Their university material is not at all helpful, so that could be a big improvement which they should learn from their biggest competitor, Informatica.

I did not use a different solution before Collibra Data Intelligence Platform.

The initial setup and implementation was a significant investment in terms of time, consulting services, and resources. Collibra Data Intelligence Platform as a platform is complex, so there was a need for customization tailored to our organization, and that added to the setup costs. Licensing permits user-based and capacity-based tiers, so there is the provision to license core data governance, the data catalog, and the AI governance features separately or as bundles. In terms of what we got as a return, Collibra Data Intelligence Platform does deliver strong value which does, hopefully, translate into quantifiable business benefits and cost savings over time as we use it.

Our data governance program is not that mature that I can show you any tangible outcomes at the moment, but hopefully in the future, we should.

We evaluated Informatica, Alation, and Collibra Data Intelligence Platform before choosing Collibra Data Intelligence Platform.

Collibra Data Intelligence Platform is a very good product and is very self-explanatory. A good aspect is that they have done a great job in dividing the organization into different communities. My advice is to create communities based on your lines of businesses or your functional areas in the organization. For example, you could have an HR data governance community, a finance data governance community, or an investments data governance community. You could do it geographically as well, with a US business data governance community, Australian data governance community, LATAM, and America, and so on and so forth. That is a business decision which needs to be taken. Once you have created the communities, you go down into the domain structure. Domain structure means glossary domain, which contains business terms, Rulebook domain, which contains business rules or data quality rules, mapping domain, which contains field mapping for lineage, and so on. Then you start ingesting the metadata. You create edge connections to extract technical metadata from your sources, which gives you the schemas, tables, and columns. You also ingest the business metadata using the UI and can use Excel import templates for the business terms, business rules, and data quality rules. Additionally, you can integrate it with an external tool for data quality, such as Ataccama, IDQ, or you can take use of Collibra Data Intelligence Platform's own data observability.

Collibra Data Intelligence Platform is a great tool and business-friendly, but customer support could improve. If Collibra Data Intelligence Platform wants to expand its customer base, I strongly advise them to enhance Collibra University or come onto YouTube similar to Informatica has done, posting videos so that users can learn how to use the platform, because sometimes just providing a PDF is not enough. For example, to create an Edge connection, you'll tell me to copy the JDBC driver name, the host, the port, the server name, all this information. But what if the connection fails? When you click on test connection, and it fails, how do you do the troubleshooting? When you record video sessions and show all that similar to Informatica has done, it becomes very easier for any new user to understand. That further gives confidence to the business and to the organization that they can go with this tool. That is one thing which I would suggest to Collibra Data Intelligence Platform. I would rate this product an 8 out of 10.

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 can confirm that using the Collibra Data Intelligence Platform provides time savings, money savings, and other benefits that I have experienced.

Collibra Data Intelligence Platform impressively provides AI features.

I have noticed it works within one ecosystem where each tool is collaborating effectively.

In terms of improvements, I would highlight a few points that could be enhanced in the Collibra Data Intelligence Platform.

I am satisfied with the Collibra technical support team. If I ever contacted them, I might have had some issue or question for them.

If I had to rate them, I would definitely use a scale from one to ten to express my thoughts effectively.

I would give a 10 for the support team.

I have some experience deploying the Collibra Data Intelligence Platform, and the initial setup and configuration for the needs take a specific timeframe.

I may not recall exactly how long it took for my company or my clients to deploy the Collibra Data Intelligence Platform, but I can provide an estimate.

It is impressive to see how fast it can be set up, typically within a few days or working days, which I can confirm is really fast.

On our website, I did research on Data Governance tools, and I can share feedback on that type of tools.

I am representing PeerSpot, which is a review website, and I am collecting feedback on the tools to publish an article on our website.

For Data Governance tools, I have worked with Purview of Microsoft, though this was mainly for research and POC that I did for my clients.

I have been working with Collibra Data Intelligence Platform for a specific period.

On a scale of 1 to 10, I would rate Collibra Data Intelligence Platform a 10.

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.

The workflows in Collibra Data Intelligence Platform are unique as no other Data Governance tool has this feature. The data quality functionality provides cutting-edge, next-level insights when compared to other tools such as Alation and Microsoft Purview. In governance, Collibra Data Intelligence Platform has an upper hand.

The mapping features in Collibra Data Intelligence Platform allow users to import and export Excel files. While importing Excel files, users can map to numerous assets. We can build relations using these imports and mappings, and obtain lineage information. Additionally, there is integration between Data Quality and the data intelligence platform, which enables another type of mapping.

The workflows in Collibra Data Intelligence Platform save substantial time by allowing us to create and assign tasks to business stewards and stakeholders. Instead of sending emails each day, we can create tasks that automatically generate notifications to streamline the process.

The main advantage of Purview is its integration with Microsoft solutions. Data can be harvested from any solution within Azure, data fabric, or other platforms easily with Purview. With Collibra Data Intelligence Platform, users need to create and establish connections, which requires entering connection details. This process is more time-consuming in Collibra Data Intelligence Platform compared to Purview, where everything can be done in minutes. Additionally, Purview includes data products, which Collibra Data Intelligence Platform is currently introducing. For stakeholders, data products are crucial.

I have been using the solution for three years, precisely two years and ten months. This July it will be three years. I started as a fresher in Data Governance and have been working in this field since the beginning.

I have not experienced any downtime or stability issues until now.

I have not had any experience working with AI governance-related API. I am satisfied with their support team. I encountered some issues with data quality initially in Collibra Data Intelligence Platform, and they resolved those issues. I also faced challenges with establishing data quality rules in DQC aggregation files, which were resolved by the support team.

There were no blockers during setup. It was straightforward to create workflows and deploy them into Collibra Data Intelligence Platform by following the documentation.

Data quality in Collibra Data Intelligence Platform involves six dimensions: accuracy, completeness, integrity, and timeliness. The platform's Data Quality encompasses all these aspects.

Task completion time depends on the specific tasks in Collibra Data Intelligence Platform. For example, an approval task typically takes less than one hour.

Collibra Data Intelligence Platform introduced AI governance approximately two years ago. It features assessments that work in conjunction with AI governance, where users answer questions created by stakeholders or Data Owners.

I rate this solution 8 out of 10.