Likelihood to Recommend During the project development phase it becomes easier to visualize the code development in almost real time with charts and drop down and drag and drop features makes it really easy for the effective development process during different phases of the project. Dashboard itself is of great help with features like drag and drop and visualize the artifacts in real time as we are developing those. It wouldn't be very effective with high massive coding and huge coding projects as we may encounter performance issues.
Read full review 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).
Read full review Pros Easy to learn and apply in real time projects. Multiple query can be combined to generate simple and easy to understand reports. Mobile access feature helps to integrate with multiple devices. Read full review 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 Read full review Cons How to do guide and there is definitely more training opportunity that company can offer Performance improvements can make the tool more efficient Can be more open to accept the external files Read full review 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 Read full review Likelihood to Renew 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.
Read full review Usability 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.
Read full review Support Rating 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.
Read full review Alternatives Considered Lower costs—reduces maintenance due to complete report coverage and a zero-footprint environment. Ability to create complex, multi-page layouts using different data sources. Multiple export formats—Excel, Portable Document Format (PDF), Extensible Markup Language (XML), Hypertext Markup Language (HTML), and Comma Separated Value (CSV).
Read full review 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.
Read full review Return on Investment Effectively solves the project blockers by mitigating the integration challanges Monetary impact is not negatively impacted on the projects budgets Great ROI and positive impact on the business because of breath of features and great dashboard features Read full review 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. Read full review ScreenShots