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
Qlik Analytics Platform
Score 7.1 out of 10
N/A
The Qlik Analytics Platform (QAP) is a developer platform for building custom analytic applications based on rich frontend and backend APIs. It gives full API access to the Qlik associative engine to build rich data-driven analytic applications, for example when building web applications for extranet and Internet deployment.
We haven't had a proper benchmarking done. We might consider looking at other options, but this thing is deeply integrated into our platform, so not anytime soon.
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).
Qlik Analytics Platform has helped us track turnaround time as well as sales volumes. We can display metrics in a multitude of ways and can then slice it up in even more ways by adding filters. It is nice that you can filter down the data by essentially "Qliking" anywhere to find insights. Qlik Analytics Platform has been extremely beneficial in reducing manual reports. We are able to automate numerous reports in addition to setting up alerts, through Nprinting, that will notify users when certain metrics are hit. For example, if our turnaround time exceeds a certain number, we can alert business stakeholders so they can take a closer look. We can also send notifications to sales representatives when their territories are underperforming.
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.
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.
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.
Customer support is provided through resellers, and finding the right reseller for your context can be a challenge. However our support from our last two resellers/partners has been largely very good. The online community is strong, though obviously doesn't replace having local technical experts working with the product and managing it.
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.
Qlik is a platform with more scaling ability and functionality compared to PowerBI. PowerBI is the prior step before passing to Qlik, however, Qlik provides more functions and abilities to the business domain unit with better management capabilities for the IT team that set it up. In the end, we implemented Qlik for functionality and scalability.
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.
