IBM Cognos is a full-featured business intelligence suite by IBM, designed for larger deployments. It comprises Query Studio, Reporting Studio, Analysis Studio and Event Studio, and Cognos Administration along with tools for Microsoft Office integration, full-text search, and dashboards.
$10
per month per user
Looker
Score 8.2 out of 10
N/A
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.
We find Looker much easier to develop and manage. The LookML central data repository is far more robust and easy to use. The fact that Looker manages the server for you, means you never have any downtime. The other products have you managing all sorts of servers, which is a lot …
Our first and most basic scenario was to automatize the creation and publication/sharing of business reports that used to be created manually by employees throughout our organization. Using Cognos for that first use case worked well. The advanced analytics functionalities we used on the aggregated data were also as expected. However, the user interface is not always intuitive, and there is a steep learning curve, so I would recommend Cognos only to large organizations where there is a need for the most advanced functionalities (AI analysis/prediction).
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).
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
Sometimes there might be performance issues when dealing with large and complex data.
Although IBM provides full documentation but sometimes it's difficult to find answers to questions and connect with their customer support.
It relies on external tools for data cleaning, transformation and also for some integration tasks. It can also improve on providing wider range of data sources for integration.
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
For an existing solution, renewing licenses does provide a good return on investment. Additionally, while rolling out scorecards and dashboards with little adhoc capabilities, to end users, cognos is very easily scalable. It also allows to create a solution that has a mix of OLAP and relational data-sources, which is a limitation with other tools. Synchronizing with existing security setup is easy too.
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 have a strong user base (3500 users) that are highly utilizing this tool. Basic users are able to consume content within the applied security model. We have a set of advanced users that really push the limits of Cognos with Report and Query Studio. These users have created a lot of personal content and stored it in 'My Reports'. Users enjoy this flexibility.
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.
Reports can typically be viewed through any browser that can access the server, so the availability is ultimately up to what the company utilizing it is comfortable with allowing, though report development tends to be more picky about browsers and settings as mentioned above. It also has an optional iPad app and general mobile browsing support, but dashboards lack the mobile compatibility. What keeps it from getting a higher score is the desktop tools that are vital to the development process. The compatibility with only Windows when the server has a wide range of compatibility can be a real sore point for a company that outfits its employees exclusively with Mac or Linux machines. Of course, if they are planning on outsourcing the development anyways, it's a rather moot point
Overall no major complaints but it doesn't handle DMR (Dimensionally Modeled for Relational) very well. DMR modelling is a capability that IBM Cognos Framework Manager provides allowing you to specify dimensional information for relational metadata and allows for OLAP-style queries. However, the capability is not very efficient and, for example, if I'm using only 2 columns on a 20-column model, the software is not smart enough to exclude 18 columns and the query side gets progressively larger and larger until it's effectively unusable.
Why is their web application not working as fast as you think it should? They never know, and it is always a a bunch of shots in the dark to find out. Trying to download software from them is like trying to find a book at the library before computers were invented.
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.
Onsite training provided by IBM Cognos was effective and as expected. They did not perform training with our data which was a bit difficult for our end-users.
The online courses they offer are thorough and presented in such a way that someone who isn't already familiar with the general design methodologies used in this field will be capable of making a good design. The training environments are provided as a fully self contained virtual machine with everything needed already to create the environments. We've had some persisting issues with the environments becoming unavailable, but support has been responsive when these issues arise and straightening them out for us
Make sure that any custom tables that you have, are built into your metadata packages. You can still access them via SQL queries in Cognos, but it is much easier to have them as a part of the available metadata packages.
In the past Management had used Excel and Workiva capabilities to create the reporting dashboards that were being used to make decisions. Since switching to IBM Cognos Analytics the Company has been much more efficient and decision making has been streamlined. IBM Cognos Analytics was chosen due to its reputation and data visualization capabilities and neither have been wrong.
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.
The Cognos architecture is well suited for scalability. However, the architecture must be designed with scalability in mind from day one of the implementation. We recently upgraded from 10.1 to 10.2.1 and took the opportunity to revamp our architecture. It is now poised for future growth 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.
