Dundas BI is a business intelligence and data visualization software that includes customizable dashboards, reporting, and visual data analytics. Dundas BI can be integrated into users’ existing business applications and its visualization and reporting tools can be customized to their needs.
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Looker
Score 8.2 out of 10
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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.
Dundas and Sisense are very similar. Both are very powerful and flexible with an eye toward further innovation. I expect both will do well as we enter a major transition period for BI.
Periscope Data and Dundas differ substantially and seem to have different paths forward. …
For all the scenarios I have so far worked on or I am currently working on, Dundas BI has proved to be more than adequate and apt to handle all of those. It is a very easy-to-use tool with quick shortcuts enabling you to prepare ad-hoc reports or dashboards in a matter of minutes.
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).
Project organization from Development to Production, you get a production and development license but I think the best way to do it is with DEV and Prod project in the Production box. Use the development box for testing updates and really crazy things. With the Dev and Prod projects on the same box, you just publish from Dev to Prod and you are done. Users only have access to the Prod projects so no one can mess up what you are working on.
Security - If you have a hierarchy (subsidiaries, divisions, department, teams) and you want each group to see only their data, then Security hierarchies are for you!
Dependent filters! What's this you ask? Here is an example of how it can be used, in your company you have departments and who works for what department is in your database. You make a dashboard that has a department filter (only show these departments), a managers filter, and employee filter. Not every manager or employee is in multiple departments usually only one. With dependent filters you can say that the manager and employee filter are dependent on what is selected in the departments filter so when you go to filter them they only show the managers or employees that are part of that department, and you can even it do so employees are not only dependent on department but on manager as well. Then it gets even better as it can be done in reverse as well so when you select a manager then go to the department it only shows the departments he works for (there are better situations where this is more useful).
It is scriptable! From calculate columns, null replacements, button actions, load actions, hover over events there a way to do what you want.
They are constantly improving and listens to your suggestions.
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
Not too many cons for how we use the application. It really is easy and powerful. Very powerful.
Licensing is one thing that could be looked into. It is simple, but a little confusing. For example, if I get a license today, but a new release comes out tomorrow, it seems that the license doesn't work with the new release. Maybe that is by design, but it would be nice to clearly understand.
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.
We are still in the implementation phase, but so far we are finding it to be easy to use and learn. The eLearning courses that they have made available for free, as well as User Forums and other training videos have made even difficult concepts easier to understand.
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 have bi-weekly calls with our Success Manager, as well as access to support as needed. Any question that I have had, multiple people have been willing and able to jump on a call to talk me through it, or send an email with the solution
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.
Per dollar spent, it offers the widest range of features of the tools that we evaluated. It offers lots of options for how to configure your environment, though they are not always intuitive to figure out. Having an ETL layer was a must have for us, as well as the ability to host to secure HIPAA compliance. It is not a replacement for ad hoc reporting, but does a great job of creating parameterized reports and dashboards that look great.
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.
