ClicData is a 100% cloud-based business intelligence platform that allows users to connect, process, blend, visualize and share data from a single place. As an automated platform, users are able to rely on the latest version of company data, to ensure users make the right decisions. Hundreds of data connectors ClicData has connectors that allow users to pull data automatically from hundreds of business applications and databases. Data warehousing and ETL…
$79
per month
Looker
Score 8.2 out of 10
N/A
Looker is a BI application with an analytics-oriented application server that sits on top of relational data stores. It includes an end-user interface for exploring data, a reusable development paradigm for data discovery, and an API for supporting data in other systems.
N/A
Pricing
ClicData
Looker
Editions & Modules
Premium
$79
per month
Team
$269
per month
Business
$525
per month
Enterprise
Custom Quote
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Offerings
Pricing Offerings
ClicData
Looker
Free Trial
Yes
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
Yes
Yes
Entry-level Setup Fee
$71 data volume, type of data source and level of automation
Required
Additional Details
All plans include multiple users licenses but dashboards can be shared with external users via live links, no license needed.
Plans and pricing vary based on the data connectors, refreshes, and the level of automation needed.
A few hours of Expert Service are including in all plans but more hours can be purchased.
A white label option is also available for consultants and agencies.
We used ClicData dashboards in a somewhat unorthodox way: we used "Indicators", or icons within tables to represent data, and the results looked great. The ability to integrate 3rd party HTML into dashboards was valuable (although Clic Data correctly warns that response time may suffer). The ability to start small and release dashboards incrementally suited our needs very well. We also valued the responsive account team getting us quickly pointed in the right direction.
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).
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
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
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.
Like I mentioned before, the ease of use means we not only get very quick results and beautiful dashboards in minutes, it means my team is actually using it because they aren't frustrated in the learning curve or wasting time trying to accomplish simple tasks.
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.
We've only needed support a couple times in the beginning as we were learning the system and how to display our data in interesting ways. Every time we reached out we got very detailed responses and were connected to someone that you could tell was very interested in making sure we were happy with how things were going.
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.
ClicData was the most flexible. It is cloud-based and even has WebService access. You can publish your data for access from other applications. The sheer number of data connectors is amazing. The ability to not only create dashboards, but you can schedule them to be delivered to specific recipients on a scheduled basis via PDF or a static HTML page.
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.
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.
