Dataiku vs. IBM Cognos Analytics

I would recommend it because it's an amazing tool for different levels of users. From Business Analysts to Data Scientists to Managers, various employees can make use of this tool to make data-driven decisions. I'm not sure about where it would be less appropriate as I'm using it as Data Scientist and so far it pretty much caters to my need.

Incentivized

I use predictive analytics techniques, which can help me predict my future sales based on collected data, giving me insight into my market's trends.This market data can be analyzed, giving me the opportunity to gain in-depth insight into my market's competition and positioning it competitively, aided by developing strategies to improve my marketing approach.

Incentivized

• Very intuitive and easy to use UI, making a lot of types of users can collaborate with each other easily, by visualizing the same workflow.
• Many building blocks can be reused immediately, avoid a lot of non-standard boiler plate implementation.
• Data pre-analysis and feature engineering assistance increase the productivity as well as the efficiency of data scientists.
• Many data connectors support wide range of data storage, from SQL, TeraData, Hadoop Hive, etc.
• Support from research till final MaaS solution deployment.

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

• Its community support is very limited at the moment
• Complex to integrate with automation tools such as Blue Prism

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

It took my BI team one year to become productive at developing useful content on the IBM Cognos platform. After this year, the reports being developed for a client were stale and no longer relative to the ever changing needs of the business client. Given the same opportunity, I would select a platform that allows the team to quickly produce BI content. Fail fast and recover quickly!

As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

Incentivized

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.

The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.

Incentivized

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

The implementation was handled very well. The initial implementation exposed a lot of disagreement between our campuses and departments as to how we define data. This was not entirely unexpected, but I thought that we did a nice job as a team to work through some of these challenges.

Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

Incentivized

Power BI is stronger for quick ad-hoc analysis and dashboards, but IBM Cognos Analytics is better when consistency, precision, and mass distribution matter. Tableau is best for interactive analysis, while IBM Cognos Analytics is better for standardized, repeatable enterprise reporting. Sigma shines for customizable dashboards and drill-down analysis while IBM Cognos Analytics holds an edge in data discovery and visualization.

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.

• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

Incentivized

• The platform can be pretty pricey.
• Convoluted contracts and they fine you for any breach during audits.
• Things are changed between releases without any warning and break.
• It is second to none at being able to customize list and crosstab type reports.
• You can easily set up a report to be emailed out every day and even have it export the report to a shared folder.
• On-prem environments can be scaled up pretty much as much as you would like.

Incentivized