Likelihood to Recommend 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).
Read full review Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level
TensorFlow API or
Pytorch Read full review Pros We use the tool for data modeling as it helps in predictive data analysis for complex data, which is very similar to real-life scenarios. Options of customizing & scheduling reports as per our requirements basis. Has mobile application which works seamless. Read full review One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations. It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast. It also provides functionality to develop models on mobile device. Read full review Cons 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. Read full review As it is a kind of wrapper library it won't allow you to modify everything of its backend Unlike other deep learning libraries, it lacks a pre-defined trained model to use Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs Read full review Likelihood to Renew 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.
Read full review Usability 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.
Read full review I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.
Read full review Reliability and Availability 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
Read full review Performance 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.
Read full review Support Rating 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.
Read full review Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.
Read full review In-Person Training 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.
Read full review Online Training 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
Read full review Implementation Rating 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.
Read full review Alternatives Considered 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.
Read full review Keras is good to develop deep learning models. As compared to
TensorFlow , it's easy to write code in Keras. You have more power with
TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to
MATLAB , I will always prefer Keras as it is easy and powerful as well.
Read full review Scalability 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.
Read full review Return on Investment High ROI with well designed solutions. Supported by scalable deployment, robust security model and ability to create valuable content. High ROI where well designed data models can be deployed with a common metadata layer to a variety of users and use cases. High ROI in an environment that includes a variety of vendors and best of breed products within the overall platform Read full review Easy and faster way to develop neural network. It would be much better if it is available in Java. It doesn't allow you to modify the internal things. Read full review ScreenShots