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.
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NinjaCat
Score 7.8 out of 10
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NinjaCat is a reporting solution for digital marketing agencies. It allows users to track the spend and performance of Google AdWords, Bing Ads, Analytics, Facebook, and DoubleClick with call tracking in one interface, allowing users to monitor campaigns and deliver reports to demonstrate return on marketing investments. NinjaCat users can connect, clean, and transform marketing data in NinjaCat then ship it to any of the leading data warehouses via its API or code free replication…
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).
NinjaCat has been great for getting clients quickly connected to popular advertising platforms like Snapchat, Facebook, and Google. It can blend data together easily to make overall data easy to access. The template designer is easy to use (though careful there is no auto save or undo button) and you can have a client ready report done easily with one of their premade templates or customize it for your needs. The fact that I can build any metric or pull in about any information with the Google Sheet data feature makes the product far and away one of the best products I've had the pleasure of using.
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
It automates Client Reporting for Digital Advertising -- it's time-consuming to set up, but once it is, you don't have to do very much after that.
Adding Data Sources to your reporting. NinjaCat integrates with most digital advertising platforms, in particular, Google Ads and Google Analytics.
It Manages Client Reporting in one place -- there's no need to send reports from different platforms and stitch the information together. NinjaCat pulls all the data and allows you to customize reports for your clients and send them concise data in an appealing format.
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.
The platform is in-depth and thorough. The support team is amazing. The ability to do automations and data visualizations are fantastic. However, it does lack integrations fore taking data to the next step. Other platforms let you integrate Python and R scripts for organizing data or even forecasting. If NinjaCat were to add this, I think it would be a 10/10 and best product on the market
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.
Google Analytics is raw data. Reporting has improved, but it is limited to the data available in Google Analytics Only. NinjaCat combines data across all the digital platforms you use. Acquisio and Kenshoo have similar capabilities on the reporting side but are cost prohibitive due to their pricing structure. You can't just us the reporting features of either of these 2 platforms as a stand-alone feature. NinjaCat does one thing and does it well -- Automated Client Reporting of digital marketing and advertising.
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.
