What do you like best about the product?

The ability to apply definitions and tags for almost any data asset is useful, especially the feature to attach existing documentation as a resource. Great search feature, and ability to propagate tags downstream from objects.

What do you dislike about the product?

The UI is sometimes difficult to navigate, and there are some screens that may be more suitable to view as a table. There's also sometimes a steep learning curve to go from basic read-only use (or single asset management), to enabling mass-tagging/documentation efforts.

What problems is the product solving and how is that benefiting you?

Atlan is supporting our initiative to tag and classify all sensitive data in our source system and downstream applications. Being able to propagate tags and view lineage has been really useful.

What do you like best about the product?

Atlan’s user-friendly interface encourages engagement across the board, regardless of technical expertise. The data lineage feature provides a clear, visual layout of our data stack, which has been particularly helpful in onboarding new hires by giving them immediate insight into our data architecture. The glossary feature is crucial for establishing a common understanding of terms and metrics throughout the company, with the ability to assign ownership to these definitions. Linking glossary terms to related assets further reinforces this shared understanding and accountability.

What do you dislike about the product?

The integration with Teams could be more refined. Specifically, it would be beneficial if requests for approval could be directed to the appropriate data owner or individual, rather than just an administrator.

What problems is the product solving and how is that benefiting you?

We lacked a consolidated view of our overall data landscape, and there was a clear need to standardize corporate terminology and metrics with defined lines of responsibility. Atlan addressed these challenges by providing a unified platform that supports our data governance and operational goals.

What do you like best about the product?

Having used other tools in this space Atlan has a much better administrative and user experience than other tools.
•It was easy to set up and integrate with our tech stack.
•Navigating it feels intuitive which is critical to a metadata management system you need to put in front of multiple user personas.

They are also developing at a rapid pace which features getting releases each week and an eagerness to hear/respond to feedback. Some of the newer features aren't perfect but they are generally equiavalent to competitors and at the pace of development I see they pulling ahead.

Support has been great - we actually get responses from people that want to help instead of it going into a black hole.

What do you dislike about the product?

Would like to see usage reporting built into the product to help tell the story on its value and ensure we are positively driving adoption. Right now this is only available from a 3rd party source which wasn't mentioned to us and we'd have to get approval to use.

What problems is the product solving and how is that benefiting you?

Its integrating metadata from multiple systems and exposing it to our users in a easy to consume way so its organized, Discoverable and trusted.

What do you like best about the product?

Its UX and its deployment added its data lineage and the easy integration with multiples sources.

What do you dislike about the product?

Its technical supports is to slow in the resolution and sometimes not very accurate

What problems is the product solving and how is that benefiting you?

Its resolve the data catalog and data lineage by organizin the company to self service information.

What do you like best about the product?

Atlan is modern, scalable, easy-to-use platform able to seamlesly integrate with majority of technologies within our organization, create a complete catalog on top of a complex environment and surface lineages and dependencies between various databases.

What do you dislike about the product?

Integrations with non-native cloud and on-prem databases requires additional infrastructure and networking setup.

What problems is the product solving and how is that benefiting you?

Atlan is helping our organization with the following:
- Data Catalog - An ability to surface catalog of Data Warehouse, Transactional Databases and Data Pipelines in a highly complex environment
- Data Quality and Ownership - Ability to pinpoint critical data and its impact to our clients
- Business Glossary - A single source of truth for all things data

What do you like best about the product?

One of a kind player in the data space! Our experience with Atlan from the Proof of Concept stage all the way to the implementation has been nothing short of exceptional. The team is incredibly thoughtful with communication and documentation. They bring to the table a strong willingness to understand the gaps and solve problems

What do you dislike about the product?

Looking for more customization for the different kinds of data practitioners

What problems is the product solving and how is that benefiting you?

- Ease of discoverability, reducing time to implementation
- Collaborating with various other data practitioners.
- Communicating and defining lineage

What do you like best about the product?

1. End-to-end view of data lineage across our whole data lifecycle: Atlan perfectly integrates with large parts of our tool stack (Snowflake, DBT, Tableau, Salesforce, Fivetran), which gives us great transparency into our data flows & structures. Especially being able to identify & export dependencies have been pivotal for our data incident management and requirements analyses for changes in our data structure. In order to paint a full picture of our tool landscape, we would love to see additional integrations being published for rETL tools like Hightouch, data quality tools like Metaplane or additional CRMs like Hubspot & Zendesk.

2. Outstanding support from our CSM & Customer Support team: Our CSM serves as a trusted advisor when it comes to the overall architecture of the tool and general data governance best practices. His level of support & commitment for us to get the most value out of the tool is far above the average SaaS CSM support. For instance, our CSM introduced us to a dedicated internal Data Governance Consultant, who took multiple hours of time to share his experience and advise us on how to effectively implement data governance at our organisation.
Likewise, the Customer Support team exhibit impressive technical understanding & structured problem solving, providing us with very timely and deep analyses of any issues coming up.

3. Great pace of delivery for new features: Atlan regularly publishes highly relevant new product features at a fast pace. Very regularly, if we provide feedback around missing or non-optimal functionalities, it turns out that the respective feature is already on the roadmap and might even be released within the next couple of weeks. It has become very apparent that the product strategy is very much centered around customer feedback & pain points, which we appreciate a lot.

What do you dislike about the product?

1. Workflows/Playbooks: Atlan offers the Playbook functionality to automate metadata population, data owner assignments, tag assignments, etc. While this functionality has proven very useful already, I would love for the automation engine to become more powerful. Top features on our wish list would be event-based triggers, more effective approval processes, metadata propagation across lineage and outbound API call functionalities.

2. Pricing: While the Atlan team has been ensuring that pricing should not be a blocker for our data governance efforts, the list prices for connectors and member licenses are fairly high compared to other SaaS tools and only economical at high discounts.

3. Bugs: While Atlan publishes new features at a very strong pace, there are somewhat regular cases where features do not work as expected and need to be fixed by the engineering team. It's worth calling out that in those cases, the fixes are implemented very quickly as well, though!

4. Permission management: The general permission management based on Personas & Purposes is somewhat too complex compared to other SaaS tools and not very user-friendly. We would love for our users to immediately see all metadata and assets that they have access to rather than having to switch between personas & purposes.

What problems is the product solving and how is that benefiting you?

Overall, we're very satisfied with Atlan and our collaboration with them. Atlan is a game changer for our data governance & data management efforts, especially for collaboration within our centralised data team.

Atlan enables us to effectively share knowledge both within our data team and with external stakeholders by serving as a source of truth for our most important term & metric definitions. Terms & metrics can be easily & extensively documented in the Glossary including descriptions, in-depth ReadMes, custom metadata and external documentation (e.g. Confluence). It's also very helpful to be able to link these terms to suitable dashboards and tables to indicate where the source of truth for the respective term or metric comes from.

We're also using Atlan for the documentation of our Snowflake tables. While some documentation has already been included in our DBT models directly, this documentation is not easily accessible for stakeholders or downstream consumers of the data (e.g. analysts). Using Atlan, we can democratise this documentation automatically by pulling the respective descriptions from DBT directly and populating them in Atlan. Using Atlans powerful API, we're also working on loading ReadMes from our directory directly, such that documentation for data engineers becomes easy to populate and consumable by stakeholders at the same time.

Atlan is proving to be highly effective to analyse up- & downstream dependencies of tables, data products and dashboards, significantly reducing communication efforts. For instance, exporting downstream dependencies for impact analysis and analysing upstream dependencies for root cause analysis in Atlan have become an integral part of our data incident management process. Being able to quickly identify impacted assets and their owners not only enables fast assessment of the incident impact. It also allows for immediate stakeholder identification, saving valuable time in the incident resolution.

Lastly, we're also currently starting to use the Data Products & Data Domain features in more depth. Atlan allows to extensively document data products (e.g. description, ReadMe, criticality, sensitivity) and relate relevant tables and dashbaords via automated rules. By being able to add both data products and individual tables & dashboards to data domains, the tool is becoming our source of truth for data roles & responsibilities and valuable input for domain-specific reporting on data governance maturity.

What do you like best about the product?

The best benefit of Atlan was the functionality for both front end users and our data engineering teams. Providing a common data glossary location tied directly with the data assets enabled us to make data more available and accessible to all data consuming teams. The flexibility in providing an area for data quality metrics to be visible right alongside each key dataset also provides next level confidence to our teams that the data they're accessing is accurate, complete, reliable, relevant, and timely.

Integration with our data platform was straightforward and ensuring all our data sources and reporting applications are all connected quick and effective. Any issues we had were resolved quickly with the customer success team and it's great to have regular check ins with them to learn of the new features on the product roadmap.

What do you dislike about the product?

The most difficult part is taking advantage of all the features. There is a lot it can do with Atlan and it will take time to implement. It's not a bad thing, it's just that you need to be aware of the extra time and resource required to make it as effective as it can be for your organisation.

What problems is the product solving and how is that benefiting you?

The main purpose of Atlan is bringing our data together in an easily searchable way for our data consuming teams. Secondly is about ensuring our data engineering team is always the first to know of any data issues and data quality tests will reflect in Atlan to ensure our staff always know the quality of the data they need to use.

What do you like best about the product?

Atlan is easy to implement and integrate with existing systems. It can ingest data schemas from various systems and provides a clean user interface to add metadata such as tags. Atlan enables data and compliance teams to easily identify ownership, and sensitive or private data objects.

What do you dislike about the product?

The Atlan supplied tooling to ingest schema from PostgreSQL databases could use some dependency management as many of the dependencies contained vulnerabilities at the time of use. This tool was also only available for download by requesting a link and not available as a container image enabling automated updates.

What problems is the product solving and how is that benefiting you?

We have a fairly complex data ecosystem, and it can be difficult to keep track of what data is stored where. Atlan makes it much easier to catalog, search for, and classify information across our organization; enabling our data governance and compliance goals.

What do you like best about the product?

Atlan has a comfortable UI that is fairly intuitive to navigate, even for team members who don't interact with data often.

What do you dislike about the product?

The lack of a native Airbyte connecter has prevented us from getting as much value as possible from Atlan, and while the integrations with other systems such as Tableau are there, it isn't as in-depth as it could be.

What problems is the product solving and how is that benefiting you?

Atlan helps us to establish a common language around our data, find data that is available, and understand how to utilize it.