Altair SLC vs. IBM Cognos Analytics

WPS Analytics allowed our company's business users to keep using their existing programs while getting similar results with much less learning time. It's a great accomplishment when software for business users is changed—excellent relationships with providers and other salespeople. As a SAS user, I needed an interpreter for the SAS language, and WPS has more packages and a lower price than any other company I could find, so I decided to go with
it.

Incentivized

With the help of IBM Cognos, the sales division can analyze sales performance, sales trends in top-performing areas, etc. It also helps in financial planning, like forecasting, budgeting, reporting, and variance analysis. It also helps increase supply chain performance by analyzing it. It should be easy to use for small-scale data analysis. MS Excel is very useful for small-scale data analysis.

Incentivized

• WPS Analytics is powered with high performance data processing engine.
• Reusable data models save finances and time.
• Self-service data analyzation portals.

Incentivized

• Data Visualization: Plenty of options exist for multiple use cases, and dashboards are easy to implement and customize.
• Integration with IBM Watson: makes it easy to use Watson AI features (NLP etc.) on your data.
• Its advanced analytics functionalities with powerful pattern detection/prediction models.

Incentivized

• Currently, I barely encounter challenges when using WPS Analytics although I don't use it on a daily basis. There's no functionality or feature that needs more improvement.

Incentivized

• API integration is not upto the mark with very limited options.
• Laptops get overheated when the tool is used from moderate to heavy use. Also, there is a lag in the tool times.
• Licensing & Maintenance can go from cheap to expensive depending on the scope.
• Lot of scope to improve the customer support & its not upto the industry standards.

Incentivized

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 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.

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.

Incentivized

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.

I use a lot of data discovery and visualization tools at workstations to help me gain deep insights into data. WPS Analytics doesn't vary much from all the DVD tools that I have listed, I always go for the best.

Incentivized

Cognos Analytics provides wide range for reporting, data visualization, and self service analytics. Cognos has strong security and governance features. Sigma Computing is purely cloud native approach and has spreadsheet like interface and doesn't provide many customization options for reporting and dashboards. Cognos can smoothly integrate with IBM products and other third party data sources whereas Sigma Computing provides integration with cloud data warehouses and data lakes

Incentivized

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.

• Using machine learning and natural language to analyze data saves time and effort.
• Standard reporting and business intelligence help us reduce workload and make data-driven decisions.
• Drag-and-drop workflow saves time when different languages like SQL and SAS make it easier to code and prepare data.

Incentivized

• Easier access to data and enhanced visualization models that allow the users to look at data in new and creative ways
• More efficiency in creating Management reports that Management can use to make decisions
• Enhanced collaboration: Rather than having 3-4 analysts work on individual reports they can come together and collaborate on one final product

Incentivized