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Streamline the Metadata Management: Unlocking Efficiency and Accuracy
What do you like best about the product?
I'm responsible for building up the glossary in Atlan - the reason why we believe Atlan is a user-friendly and collaborative metadata management platform and is the perfect fit for building up our glossaries are below:
1. Definitive definitions: 1) The glossary allows users to define and document business terms and metrics, providing clear and consistent definitions to ensure a shared understanding. 2) The glossary provides contextual information, including synonyms, abbreviations, acronyms, and related terms. 3) Terms and metrics can be categorized and organized in a hierarchical structure, facilitating easy navigation and browsing based on domains, topics, or functional areas.
2. Search and Discovery: The glossary offers powerful search capabilities, enabling users to quickly find specific metrics or related information. Users can search by keyword, browse categories, ownership, or apply filters to refine their search results.
3. Data Lineage and Impact Analysis: The glossary is integrated with data lineage and impact analysis features. Users can understand how terms are used within reports, dashboards and other data assets, and trace their impact across the data ecosystem.
4. Collaboration and Annotation: Users can collaborate by adding comments, annotations, or additional context to term definitions. This encourages knowledge sharing, discussions, and alignment on the understanding of terms.
1. Definitive definitions: 1) The glossary allows users to define and document business terms and metrics, providing clear and consistent definitions to ensure a shared understanding. 2) The glossary provides contextual information, including synonyms, abbreviations, acronyms, and related terms. 3) Terms and metrics can be categorized and organized in a hierarchical structure, facilitating easy navigation and browsing based on domains, topics, or functional areas.
2. Search and Discovery: The glossary offers powerful search capabilities, enabling users to quickly find specific metrics or related information. Users can search by keyword, browse categories, ownership, or apply filters to refine their search results.
3. Data Lineage and Impact Analysis: The glossary is integrated with data lineage and impact analysis features. Users can understand how terms are used within reports, dashboards and other data assets, and trace their impact across the data ecosystem.
4. Collaboration and Annotation: Users can collaborate by adding comments, annotations, or additional context to term definitions. This encourages knowledge sharing, discussions, and alignment on the understanding of terms.
What do you dislike about the product?
1) Pricing: Atlan's pricing structure may not be suitable for low budget companies, especially for small organizations or startups with limited resources. Will enterprise pricing plan in the pipeline?
2) Learning Curve: It might take some time for users to become proficient in navigating and utilizing all the functionalities effectively.
2) Learning Curve: It might take some time for users to become proficient in navigating and utilizing all the functionalities effectively.
What problems is the product solving and how is that benefiting you?
During our implementation of the Notion glossary, we gained valuable insights and underwent a significant learning experience. However, we encountered a set of challenges that have led us to make the decision of transitioning to a more suitable tool capable of effectively serving the entire organization. Below are the key learnings we derived from this process:
1. Inadequate understanding and communication
In many cases, there is a lack of sufficient understanding regarding critical topics, particularly when it involves metrics that are defined differently across various teams. Let's consider the example of DIFM. Due to breakdowns in communication, aligning the calculations for different perspectives of net revenue took considerable time and effort.
1. Operational complexity
Given the unique nature of our business, certain metrics can be challenging to grasp, such as revenue recognition at different points in time. Additionally, there may be high entry barriers for one department to understand the metrics used by another department. Moreover, when our business model undergoes changes, there tends to be a multitude of news and updates that colleagues outside the core team or channel might not be aware of, resulting in a lack of context.
1. Lack of ownership
Colleagues often express a desire to learn more about specific metrics, but struggle to identify the appropriate team or location to access the relevant information. Complicating matters further, the search experience in Notion is suboptimal. For instance, when using a table (database), users are required to load the entire table to perform a search or add filters to narrow down their search. This limits the efficiency and ease of finding the desired metrics and related information.
1. Inadequate understanding and communication
In many cases, there is a lack of sufficient understanding regarding critical topics, particularly when it involves metrics that are defined differently across various teams. Let's consider the example of DIFM. Due to breakdowns in communication, aligning the calculations for different perspectives of net revenue took considerable time and effort.
1. Operational complexity
Given the unique nature of our business, certain metrics can be challenging to grasp, such as revenue recognition at different points in time. Additionally, there may be high entry barriers for one department to understand the metrics used by another department. Moreover, when our business model undergoes changes, there tends to be a multitude of news and updates that colleagues outside the core team or channel might not be aware of, resulting in a lack of context.
1. Lack of ownership
Colleagues often express a desire to learn more about specific metrics, but struggle to identify the appropriate team or location to access the relevant information. Complicating matters further, the search experience in Notion is suboptimal. For instance, when using a table (database), users are required to load the entire table to perform a search or add filters to narrow down their search. This limits the efficiency and ease of finding the desired metrics and related information.
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Catalysts for metadata evangelism
What do you like best about the product?
Product value proposition in its extensibility to meet our evolving metadata ingestion, enrichment, and publishing requirements with relatively short time, is appealing.
Working with results oriented, adaptable customer success team is a fantastic experience. They are enthusiastic advocates and experts who recognize the benefits of metadata in data governance, data integration, data quality, and overall data management.
Working with results oriented, adaptable customer success team is a fantastic experience. They are enthusiastic advocates and experts who recognize the benefits of metadata in data governance, data integration, data quality, and overall data management.
What do you dislike about the product?
Some users reported page loading, quality of search results issues that have been surfaced to Atlan team.
For Platform adminitrators of a multitenant data platform, ability to create admin of admin role, and ability to preview assuming roles are sorely missed.
For Platform adminitrators of a multitenant data platform, ability to create admin of admin role, and ability to preview assuming roles are sorely missed.
What problems is the product solving and how is that benefiting you?
Integrating data discovery capability with associated platform capabilities for Data Quality, Metadata Capture, User Access among others.
Easy to use and implement
What do you like best about the product?
The platform is user-friendly and seamlessly integrates with our current application ecosystem. It also minimizes the amount of time our dev team needs to comprehend the context of their data resources.
What do you dislike about the product?
I think they can work on improving the website's speed but it's a super helpful tool anyway and does what it says. Nothing much to dislike about Atlan.
What problems is the product solving and how is that benefiting you?
Atlan facilitated the democratization of our data, enabling us to distribute it across the organization. Numerous requests from various business teams are received by our data team. With the aid of Atlan, we empowered them to attain data independence by granting access to the data.
Atlan is a modern data catalog that perfectly aligned with our vision of data governance
What do you like best about the product?
The product is excellent, and Atlan is always looking for improvement by hearing customer feedback.
What do you dislike about the product?
Sometimes the platform can be slow when you are navigating a lot.
What problems is the product solving and how is that benefiting you?
We use Atlan to centralize our metadata and generate context about company-relevant data assets. This allows collaboration between data consumers and data owners.
Atlan has been a great asset for our data teams
What do you like best about the product?
1. Ease of use
2. Data Lineage
3. Integrations with the products we use today
2. Data Lineage
3. Integrations with the products we use today
What do you dislike about the product?
Setup for on-premise data sources can be a challenge.
The classification propagation has been a challenge, but is improving.
The classification propagation has been a challenge, but is improving.
What problems is the product solving and how is that benefiting you?
Data Discovery and democratization
Atlan is a great tool with fantastic customer support
What do you like best about the product?
Our data engineering team have enjoyed the ease of setup with Atlan as dbt documentation work they had already done easily surfaced to Atlan to populate the asset profile fields. Also, the asset fields automatically populated by Atlan have been very helpful in our setup process, making the information in the tool already useful for our working processes. In addition, the customer support from Atlan has been phenomenal, providing support and resources for every step of the way.
What do you dislike about the product?
We had some technical issues during our setup, but Atlan support has been quick to troubleshoot and offer solutions as we've worked through any problems that have come up.
What problems is the product solving and how is that benefiting you?
- Providing greater access to, and trust in, data at our organization
- Enabling self-serve analytics for data consumers at our organization
- Decreasing dependencies on data personnel for information about data assets
- Enabling automation around data information at our organization
- Enabling self-serve analytics for data consumers at our organization
- Decreasing dependencies on data personnel for information about data assets
- Enabling automation around data information at our organization
improvements in our data governance solutions
What do you like best about the product?
post-sale follow-up and features like access control, search engine, and lineage
What do you dislike about the product?
some pending integrations like airflow or bug detections,
What problems is the product solving and how is that benefiting you?
build data products with more trust and improve the search time of a data asset
Great enrichment tool
What do you like best about the product?
Tracking lineage down to the column level and the automatic enrichment capabilities of the Playbooks feature and/or API
What do you dislike about the product?
I wish there was an easier way to track and debug the progress of the workflows.
What problems is the product solving and how is that benefiting you?
Atlan is helping us democratize and enrich our metadata.
Super Helpful for a Mobile Gaming Company That Collects TONS of Data!
What do you like best about the product?
As a mobile gaming company, we collect TONS of data. We were mainly attracted to Atlan because we needed a "source of truth" for all our data sources and a data lineage tool. Atlan has made it easy to document our rapidly growing database and reports, as well as visualize how each asset connects. Atlan has also made it possible for our non-analytical teams to search for Tableau reports and query our database without SQL coding skills.
We especially love how easy it was to get Atlan up and running. It integrated with all our existing tools (Slack, Jira, Tableau, Snowflake, etc.), and the Atlan team has been with us at every step. The customer service has been phenomenal -- even post-set-up. Shoutout to Steven for creating personalized tutorial videos for us! Thank you!
We especially love how easy it was to get Atlan up and running. It integrated with all our existing tools (Slack, Jira, Tableau, Snowflake, etc.), and the Atlan team has been with us at every step. The customer service has been phenomenal -- even post-set-up. Shoutout to Steven for creating personalized tutorial videos for us! Thank you!
What do you dislike about the product?
Just a few bugs here and there, but Atlan's support team has been extremely responsive and helpful!
What problems is the product solving and how is that benefiting you?
Data governance, data lineage, documentation. Atlan has saved our analytics team so much time and energy by acting as a "source of truth"! We're able to focus on our own tasks rather than performing simple queries for other teams.
Collaborative Metadata Management with Atlan and dbt: My Experience as a Data Engineer
What do you like best about the product?
As a data engineer, I've been particularly impressed by Atlan's ability to easily integrate metadata generated by dbt and custom metadata. The integration with GitHub has also been beneficial, allowing for seamless collaboration between team members on data projects. I've found that the visual representation of lineage has been precious in understanding how data flows through the system, and seeing the complete lineage at a glance is a significant time-saver. Additionally, I've had a very positive experience with the integration between Atlan and dbt, which has been reliable and straightforward to use. Overall, Atlan has made collaborative metadata management a breeze, and I would highly recommend it to other data engineers looking to streamline their data management processes.
What do you dislike about the product?
One area that could be improved with Atlan is the website speed. Unfortunately, I've found that the majority of the time, the website is quite slow. This can be frustrating when trying to complete tasks efficiently, and I hope that Atlan can address this issue in the future to improve the user experience.
What problems is the product solving and how is that benefiting you?
Atlan has been a tremendous asset in my work as a data engineer, providing several key benefits that have helped streamline my workflow. One of the most significant advantages of using Atlan is that it makes it much easier to share information about the tables generated with anyone, even those who may not have a deep understanding of data structures. Additionally, the custom metadata feature has been invaluable in showcasing when tables are updated and the frequency of these updates. Finally, being able to quickly identify the resources affected in any workflow has saved me significant time and effort when troubleshooting. Overall, Atlan's collaborative metadata management has made my work more efficient and effective, allowing me to focus on other important tasks.
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