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
TARGIT
Score 7.4 out of 10
N/A
N/A
$53
Pricing
Looker
TARGIT
Editions & Modules
No answers on this topic
Viewer
$53
Standard User
$105
Developer
$1,500
Business Intelligence Accelerator
$2,375
ANTserver
$8,000
Offerings
Pricing Offerings
Looker
TARGIT
Free Trial
Yes
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
Yes
No
Entry-level Setup Fee
Required
No setup fee
Additional Details
Must contact sales team for pricing.
All prices given are starting points; see vendor for more details.
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).
Targit is very strong when the data is presented to the system. Because of this, we are able to report on almost all of the information that is valuable to us. When we have questions or ideas on how to better leverage Targit, they are great to work with. We track our sales progress/process in Targit and AP tracks things such as outstanding invoices, open invoices, etc
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
Probably one of our biggest complaints from the user community is the inability for the user to see the details behind the data. I know with the tool you can write reporting services reports that would allow users to see the detail by right clicking on a measure and selecting Actions, but for a very lean IT shop there simply has not been time to build these Actions for every possible scenario a user might want to see detailed reporting on.
Another complaint I've heard is the inability for the user to know exactly what the data represents from the source. If Targit had maybe some type of capability to hover over a measure or dimension and see a pop up of a more detailed description this might help resolve that issue.
The most difficult concepts to teach our users is how and when to use comparisons and the syntax for calculations. Creating comparisons has become much simpler with the 2k11 release but it is still difficult for my users.
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.
It is very easy to use and I am proficient at this point to be able to figure out how to run most of the reports I need. I have had the time to "play around" in TARGIT and really teach myself about the visibility settings and other aspects - though other users have needed help with some of these aspects - but once you conceptually understand how to build the reports, it is very simple from a power user perspective.
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.
Everything on the back end needs to be correct, which sometimes it may not be set up or missing some variables. Some of the more difficult parts of Targit require a lot more attention and object creation to work properly
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.
Everyone I've dealt with at Targit genuinely cares about me and my success, and they respond to questions quickly and accurately. There is literally nothing more I could ask for from their support.
Make sure all of the back end work is done prior to getting Targit set up. Every variable you may need for calculations needs to be correct and every item needs to be uniform so it is read properly.
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.
It really met our business requirements when we were searching for a reporting/Bi tool and the flexible business model of being able to introduce additional functionally as your organization grows and requires it made the investment decision the right one for us. The functionality available in comparison to the investment is well worth it.
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.
