Keen IO vs. Looker

To run more visual, analytical models, it is great. Leave the statistics to your other programs.

Incentivized

Quick dashboards from Google Sheets - Easier to do the graphs than in Google Sheets - Operational dashboards to be used in the day-to-day work - It is good both for retrospective data and to do a pulse check of the current status - Better for not giant amounts of data and not multiple data sources. - If you need a lot of graphs to be displayed on the same page, it can be a bit glitchy during configuration (then the use works fine).

Incentivized

• It has been great for configuring and representing data trends for our own company as well as our clients.
• It is very helpful for viewing and understanding trends within our own datasets without being too overwhelming
• The user interface is very streamlined and doesn't take too long to learn so there isn't a lot of time wasted.

Incentivized

• Filtering - you can filter across different dimensions and metrics to get a more specific "cut" of data
• Refreshing - data automatically ingests into Looker which allows reports to be updated and backfilled in real time
• Conditional Reporting - you can leverage Looker's reporting features to flag when a given metric or KPI falls below or above a specified threshold. For example, if you had a daily sales benchmark in a SAAS organization, you could use Looker to flag whenever daily sales falls above or below the benchmark

Incentivized

• Cost is a little much for something we could do in house, given, it would take us much longer
• Would be better to be more compatible with other analytical software
• Could improve on code editing capabilities

Incentivized

• Looker is less graphical or pictorial which makes it less attractive
• Consumes a lot of memory when there are multiple rows and columns, impacts performance too
• At times when we download huge chunks of raw data from Looker dashbords, the time taken to prepare the file is enormous - The user fails to understand if Looker has frozen or if the data is getting prepared in the background for downloading. In turn, user ends up triggering multiple downloads

Incentivized

We've been very happy with Looker so far, and all teams in the organization are starting to see its value, and use it on a frequent basis. It has quickly become our accessible "source of truth" for all data/metrics.

Incentivized

Looker is relatively easy to use, even as it is set up. The customers for the front-end only have issues with the initial setup for looker ml creations. Other "looks" are relatively easy to set up, depending on the ETL and the data which is coming into Looker on a regular basis.

Incentivized

Never had to work with support for issues. Any questions we had, they would respond promptly and clearly. The one-time setup was easy, by reading documentation. If the feature is not supported, they will add a feature request. In this case, LDAP support was requested over OKTA. They are looking into it.

Incentivized

They have minor similarities, so they are not easily comparable. Keen is one of a kind and does not have a great alternative. It just needs a little work and developers will be addicted to it.

Incentivized

Looker is an off-the-shelf, free tool for Google business users. Other than the internal cost of time to build, we had no costs to set up what we needed to do. Knowledge sharing internally and using templates greatly reduced this cost, making the overall cost very low.

Incentivized

• Allowing others to self-serve their own analytics and connect it to Looker simply and easily has helped unblock the central data team so they can instead focus on validated dashboards whilst stakeholders manage their day-to-day analysis themselves. Countless engineering hours have been freed up by not having to manage every user permission for each BI tool; we have a BYOBI approach; Bring Your Own BI
• Creation and management of a semantic layer (LookML =Looker Modeling Language ) allows peoples sandboxes and production databases to become clutter free. Minor adjustments, conditional fields, and even some modelling can all be done in LookML which doesn't need oversight or governance from the central data team.
• LookML, specifying drilldown fields and their sub-queries, as well as generally creating dynamic parameters with Liquid are all great features, but can have a steep learning curve. it may take some time to understand how to create this middle layer correctly, or even pose a risk of inheriting complex code from another source which can be unmaintainable if it becomes too big. Some level of governance is recommended if Looker is used by a large number of editors.

Incentivized