Overall experience is great because it is a good platform for Data Scientists, Data Analysts and Researchers. It has ML and AI functionalities.

The flow in the Dataiku describes the process flow of the project easily. Scenarios and triggers are there for the automation process. Modeling is easier and shows the validations along with the results. Basic Visualizations can also be done in Dataiku.

Sometimes it has complexity in creating web applications and to integrate with other tools.

As a non - data scientist, i was curious to see how DSS could help me with the data preparation (cleaning and combining data), feature engineering and predictive modelling phases of a data analysis project My goal was to make 2 submissions on Kaggle challenges in under 1 hour and without 1 line of code using the Data Science Studio (Titanic and Otto Product Classification datasets). First, I was really impressed with the overall ease of use and ergonomy of the studio. Building "recipes" for data preparation mostly uses visual processors and the operations are visible directly on a sample of the data, facilitating validation of preparation steps. In a train / test scenario, i especially enjoyed being able to replicate my recipes on both datasets very easily. I used the Data Visualization tool to build a few exploratory charts, which can be done quite easily, though it is not as powerful as specialized tools (namely Tableau or Qlik). For the machine learning part, I restricted myself to visual machine learning in the studio, which already packs the most common algorithms (random forest, logistic, svm, gradient-boosting...). I found the ability to benchmark and compare algorithms performance quickly a great time saver, allowing me to reach a first score in under half an hour on each dataset. Once I chose the best model, I only needed a few clicks to use the model to prepare and score the Test Dataset and make my submissions. Both times I was in the lower half of the rankings but above Kaggle algorithmic benchmarks. For "real" Data Scientists and engineers, the Studio allows them to go much further by building recipes and models in R, Python, SQL, Hive, Pig etc...but even as a business analyst, I felt empowered by the software that enabled me to prepare, analyse and build simple predictive models with my data.

The breadth of choices that one gets with the product is esstial for anyone transioning to get more data science enablement. This platform is great to have a streamlined and operational portfolio of all your data requirements. There are outages that might occur once in a while but the quick turnaround time ensures that work is not compromised

The ease of onboarding people onto the platfrom is seemless. In addition, Dataiku provides their own modules that help in getting accustomed to the product. The width of options that you get for analysis is also vast and might be a one stop solution for all data science needs

The least enjoyable expereince of this product has to be the outages that become frequent with each version revision.

Overall the software is good and can be used for repetitive tasks with high accuracy. Only downside is the new data source connection and editing after the workflow is created

Data flows Automation is easy and efficient Data Discovery

Editing or connecting to a new data source is difficult Low visibility inside the flows Sometimes slow to work due to heavy workflow

Whether the project requires data accumulation, or preprocessing, or data manipulation, or extracting business insights from data, Dataiku is the go-to platform. It covers end-to-end project execution and deployment along with providing API support.

time saving, useful for both coders and non-coders, allows for multi-user collaboration and monitoring, creating flowcharts help in maintaining granularity

It is in the early phase of its launch, 8 years old precisely. Hence, the customer support is not as widespread as other customer supports like stackoverflow, etc.

We had used Dataiku for first, in-house data analyses. This allowed us to assess the added value of predictive data analysis ("AI") in particular.

Getting started with the software is easy and the online tutorials are quite comprehensive. Even beginners relatively unfamiliar with the subject (similar to "Citizen Data Scientist") can get a good start with the tool. This is made possible primarily by the easy-to-understand graphical interface. The selection of machine learning algorithms met our requirements.

Dataiku is not yet so widely used. As a result, it is often more difficult to get help for specific problems or errors.

User-friendly interface makes it easy for non-technical users to build their own flows / infrastructure and automate processes. Also supports multiple languages.

Certain advanced functionalities within DI may still require a solid understanding of data science concepts and programming skills.

Truly makes your life and the one of your team easier! The biggest plus to this program is how over-viewable and de-cluttered. Biggest problem I used to face is having files all over the place, and reports all over the place. Now they can be made more quickly than ever before.

Personally, the amount of options can be overwhelming. Luckily, Dataiku does offer learning videos on how to use the platform, which I would highly recommend everyone to watch before diving into this platform. However, as mentioned before, it is easy to get lost in the amount of options there are available.

It helps me explore various anaylitical domains. Makes life easy and provides accurate results

Not much resources to learn from about the software

Easier way for data and business teams collaboration aiming to build data science applications

Overall even at the high cost I would recommend this to enable business users since the value proposition is very good. Perhaps no other tool in the market that makes data analytics so accessible

Intuitive UI for business users to interact with data in an excel like fashion and still be able to run advanced analytics on the data sets. Collaboration is effective.

It is expensive for IT to implement. They have packaged easily open source available code but have put in a user friendly UI. Data processing charges are high compared to competition.

ＧＵＩでパイプラインを構築でき、かつノードのグループ化を行うことができるので、画面１つでパイプラインの構築や改修ができる。

ダッシュボード機能があまり充実しておらず、特に動的ダッシュボードがサポートされていない点が惜しい。このため、例えば新しい特徴量を追加しようとしたときは、事前に別のツールでデータ分析を行った上、このツールに組み込む必要があり、複数のツールを使う必要がある。