Jedox vs. TensorFlow

Best suited for financial consolidation and / or as a highly customized and compact EPM / BI solution (up to 100 CCU) with individual workflows, planning and reporting functionalities, with moderate number of users (no restrictions for any industry, all industries are covered well). It also has advanced reporting & data analysis requirements and provides an integration and reporting layer of imported data from different external systems (via ETL). It can help with migrating your legacy Excel-based business models to the Web. It is not well suited for Enterprise BI applications with expecting >500 CCU (users at the same time working with the system) - this may cause serious performance issues, as all data is kept in RAM. Jedox is also less suited for applications with heavy document management requirements (document management is not an out of the box functionality in Jedox and rather requires custom development through custom widgets etc.).

TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).

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• Using the Excel add-in means that our team was almost instantly productive, being able to use a familiar interface to present and manipulate data.
• Allows us to quickly do analysis and comparison of current and historic data.
• Really good to produce meaningful management reporting that can be adapted responsively to business requirements.
• Implementing Jedox in our budget cycle dramatically reduced the time and complexity of the process, while making it more transparent.

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• A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
• Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
• Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.

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• Diversity. Jedox can be applied to many different use cases from small to large deployments and from budgeting to enterprise class BI solutions. But rarely is one tool able to fulfill all of these requirements in one organisation. This value proposition can be complicated for prospective users.
• Awareness. Jedox punches above its weight in capability and scalability, but not enough people have heard about it and therefore procurement processes can be drawn out as a result.

• RNNs are still a bit lacking, compared to Theano.
• Cannot handle sequence inputs
• Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.

For a period of 5 years all our customers using Jedox have renewed their licenses.

To me Jedox deserves 10/10 because it is a consistent one-in-all platform with a modern look and feel. It is intuitive to use and allows you to make intuitive applications integrating traditional business intelligence with performance management functionality. It certainly has a short
learning curve, especially for those that are familiar with MS Excel. An example: I've lost count but Jedox it is available in more than 25 languages. Another: Jedox does not require programming skills... it is developed to be used by the business.

Support of multiple components and ease of development.

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Jedox has
very few bugs. Reports are available through an Excel add-in, the web and/or
mobile device (IOS/Android). In my opinion, availability also means high
performance, not having to wait for the system to give you the required reports,
analysis, dashboards instantly.

Jedox is the fastest OLAP tool on the market. It can use GPU cards to increase performance even more

Jedox support in general is a professional and fast responding team. An easy-to-use ticketing system is in place. Bug-related questions are solved fast (responses come usually in a few hours after the question), but some questions / tickets, that are not Jedox-related bugs (for example some advanced questions about Jedox functionality), may be forwarded to Application Management team for further processing and then it may take several days or even weeks to get a response here -> there is room for improvement here.

Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.

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The implementation of SSO, SAML Authentication, HTTPS, Server splitting (Frontend / Backend servers) could be more standardized and made more user friendly to set up (e.g. via setup guide). Otherwise the implementation of Jedox is quick and simple when compared to other similar technologies.

Use of cloud for better execution power is recommended.

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Calumo is similar product to Jedox. I have used it extensively in my previous role. It was a major contender when we evaluated a BI platform for NIDA. Calumo is a great product as well and it was a very close call. Where we found Jedox to be a better fit for NIDA was the ability to prepare dynamic reports with ease without the need to learn MDX which was used extensively by Calumo to make dynamic reports which expand or shrink based on the underlying data. Another major benefit we saw in Jedox was the whole ETL process could be managed within Jedox instead of doing it in SQL server which negates having a dedicated SQL specialist role when the scale expands.

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Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice

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Scalability is often another word for speed. Given enough data, enough users or enough calculations, the tool becomes slower and slower. You will find that Jedox has a very high performance that can even be increased by the use of grafical cards. Other thaen that it does not only offer BI (looking back based on historical ERP data) but also allows you to look forward through integrated budgetting, planning, forecasting, workflow and collaboration. Not easy to find a tool that can support so much business functionality. So, also pretty scalable in that respect.

• Financial budgeting and Forecasting are done in a centralized fashion in Jedox now instead of a decentralized excel based approach. A lot of cost savings and improved reliability
• Easy to use self-help Dashboards and detailed reports
• Web-based reporting instead of excel based

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• Learning is s bit difficult takes lot of time.
• Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
• Once you have learned this, it make your job very easy of getting the good result.

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