Hashboard vs. Metabase

Glean.io is exceptionally well for creating automation data visualisation dashboards. Exploring the data is again highly effective. Visualisations are easily customisable to best suit the requirements of the team. Collaborating and commenting around the dashboards is very smooth. If there are any changes in the system or any feature requests, the support team takes some time to respond

Incentivized

Metabase is an easy tool to use if you are interested in collecting and aggregating data from multiple platforms. It is also easy to set up and start receiving the data results as a report. It is also easy to integrate with other tools that generate visual reports and take the necessary actions based on the data details.

Incentivized

• Workflow is easy and clear due to clean and state of the art interface
• Model creation is easy
• Possibility for custom SQL code for database connection is very helpful

Incentivized

• We're currently using it to fetch reports for our products, such as daily sales, pipeline, visits, and attendance reports.
• Data representation from database directly.

Incentivized

• Querying has to be done by users only. There can be ways to automate querying using templates
• Not able to clearly check the query run time
• The customer support team can be little more proactive while communicating any changes and resolving any queries

Incentivized

• Report Types
• Updates
• Colour/Branding

Incentivized

Its generally quite easy to use but some SQL is definitely important

Incentivized

For simple use cases, Glean.io is a lightweight alternative to SAP Analytics Cloud. Provisioning, testing, and documentation are easier and less intimidating in the Glean.io case. Thus, it is easy to explore without firing up the sales machinery of the big corporates. Thus, the process of looking into and testing it out was way more convenient. Further, the data ops features were new to use and seem to be unique with Glean.io.

Incentivized

It does not stack up against Metabase, they complete each other.

Incentivized

• Optimised the DB management team by 30%
• Reduce in data visualisation dashboards TAT by 50%
• Increase in revenues and savings by 20%

Incentivized

• It is serving us whatever we're looking for, and we've recommended many organizations to implement it if they want better data analytics as it provides better functionality than we will build.
• As a negative it gets difficult to get control over data to be fetch initially.

Incentivized