We are a consulting firm for BFSI customers for the FSI value chain use cases, which is what we use Dataiku for, based on the problem statement the customer comes up with.
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Unified data projects have accelerated development and simplified architecture for higher ROI
What is our primary use case?
What is most valuable?
Dataiku is a complete platform to build ETL and data pipeline and deploy it, which I appreciate. It gives the complete solution to the customer and it is easy to use. Although it is expensive, the ease of use and the higher ROI for the customer make it worthwhile.
Dataiku's role in enhancing collaboration within the teams is good.
What needs improvement?
I do not see anything that I would improve or enhance in Dataiku at this time; overall, it is a good tool to incorporate and to suggest to customers.
Currently, I do not see anything specific that I would include or any functionality that requires enhancement. Dataiku gives the complete picture of the AI universe, and we have not faced any glitches, so I do not have recommendations or suggestions for improvement.
All products have room for improvement, and I would like to see their pricing simplified, as it is somewhat complex. In terms of documentation, there is substantial documentation available. Customer support is good, the product is scalable, and it provides flexibility to develop. Someone who needs to do coding can do it, and someone who does not know coding can also build solutions, but the pricing is complex, which I believe should be improved.
For how long have I used the solution?
I have been using Dataiku for the last two years, but in data science, my experience spans more than ten years.
What do I think about the stability of the solution?
I have not experienced downtimes, crashes, or glitches with Dataiku in the project that we implemented.
What do I think about the scalability of the solution?
Dataiku appears scalable, and in the project that we implemented, we did not face any scalability issues.
How are customer service and support?
I am aware of the tech support and customer service team of Dataiku.
As a partner with Dataiku, my experience with them is good; they are supportive, and when we contact them, we receive a quick response.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
Before Dataiku, I used R, R Shiny, and Python for data science, but there is no comparison between Dataiku and these tools; these are standard typical tools used to build data science projects.
How was the initial setup?
The initial setup and deployment of Dataiku is straightforward because it provides a web-based interface; it does not come with complexity for the setup.
What was our ROI?
In terms of ROI, the use of Dataiku simplifies the architecture of customers, which helps them to decommission some of their existing tools; this faster development, along with simplified architecture, gives them the ROI they need.
Which other solutions did I evaluate?
I compared Dataiku with SAS and Alteryx, and Dataiku was better than them.
I have not conducted a detailed comparison of Dataiku with Alteryx, but when it comes to governance, Dataiku surpasses Alteryx, especially in terms of compliance and governance-related features it provides for the model; I see that as the gap in Alteryx.
What other advice do I have?
Dataiku's data source integration flexibility has not benefited our data projects significantly because customers primarily use Databricks and Snowflakes, but for data science, AI, ML, and GenAI projects, it provides nice compatibility.
Dataiku is mainly used to connect the teams together and it helps to document the project details. Usually in companies, I do not see customers use many chatting tools or the tools that Dataiku provides. They are still dependent on more in-house tools that the company provides. However, because it is a platform, different teams and different verticals can come into one place and build the project. This is where I think communication is better, and they have shared data sources, which makes it more communicable among teams across verticals.
I cannot share many details on the valuable insights I have derived from using the machine learning capabilities in Dataiku because that is based more on the customer's use cases, but the value Dataiku provides is more on faster development and better ROI.
Given my experience with Dataiku, we present our sales pitch for Dataiku to customers, considering it gives the full picture or the full platform to build AI, ML, and GenAI-based applications. It is one platform where you can build entire data and AI projects or solutions. I would rate this review as a nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Has enabled reliable data pipeline creation and supports rule-based alerts for quality monitoring
What is our primary use case?
My main use cases in Dataiku include ensuring a strong data pipeline ingestion. We have people from data management, so we need to take care of the pipeline, their data quality, data drifting, all these things. We are taking care of it with the Dataiku rule-based alert systems we have created.
What is most valuable?
The best feature in Dataiku is that once the data is connected in the underneath layer, it flows exceptionally smoothly if you know how to tweak it. If you don't know, then it will create a mess. If you know how to tweak it and make the data according to your requirement, then it will be good. If you don't know and are trying to learn on the production, then it is a disaster.
I have used Dataiku's AutoML tools. The AutoML tools have helped me on the fly, as you can apply the machine learning models. They are continuously reading your data and then creating the feature enablement. The moment feature enablement has happened, then you can do the model registry on the fly. Those model registries can trigger your new data. Imagine whatever the data test and train that is passed. Your operational data which is coming new every day, then that feature is enabled and it will give the reasonable amount of prediction and reasonable amount of value on the column so that you can utilize those. You can consume those in the application layer.
Dataiku's data source integration flexibility is completely up to the requirement. We are not using it for ourselves. We are using it for business teams, and they are sending the requirement and we are ingesting according to their requirement. The important thing is, imagine raw data is coming A, but they need A plus B plus C multiply by D. All those kinds of enablement we are doing with the help of Dataiku.
Our source system, the core system, is continuously throwing the raw data on the landing layer. Then from the landing layer, we are converting those raw data and making it as a consumption layer, consumable data. With the help of this, we are doing it.
What needs improvement?
In terms of enhancing collaboration within my team, I would not say Dataiku is the best one because it's so expensive. We are not able to provide it to everyone. There are very few people who have the developer license and are using it. Once the data pipeline is created, then we are directly handing over that data pipeline to our user on the ingestion layer. It is not a very cost-effective solution, I must say, though it is good for developing purposes only.
Pricing can be improved.
For how long have I used the solution?
I have been using this product for four years.
What do I think about the stability of the solution?
In my opinion, Dataiku is stable because we know how to use it. There are many unstable things happening, so it's not that only the application is stable or unstable. Even so many other things, we are facing challenges. I cannot only blame one thing.
In terms of stabilization, if my data has no outlier creation in the raw data, then it is quite stable. I would rate it a seven.
How are customer service and support?
For support, I haven't created any support tickets, so I really don't know about it, but it is quite good.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup started with HANA. Then they introduced Databricks. When Databricks got live, then they started giving this license for Dataiku. We got the Dataiku license and learning. Everything went smoothly. Now Databricks is replaced by Snowflake. Even on Snowflake, we can do many things.
What was our ROI?
It is hard to say if I've seen a return on investment in Dataiku because we are far away from the monetization of the data. There are other teams who are taking care of the monetization. We are not from resource management, so it becomes very hard for us to calculate the ROIC on this at each and every application level. We are not using only Dataiku, we are using many other products.
Which other solutions did I evaluate?
In my opinion, it is good, not bad. I must say because I'm using many other tools as for a data operating model. It is much better than other tools because it has a clickable solution. Most of our data citizens who really don't know the coding thing can easily do things with the help of the mouse. Most of the things are working fine, so there is nothing to complain about.
What other advice do I have?
Overall, Dataiku is really good. I would rate it an 8 out of 10.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Intuitive Visual Interface, Powerful pipelines, but Needs Better History Management
What do you like best about the product?
I really appreciate how the graphical user interface handles paths and threads. It allows you to manage all your code and datasets visually, and everything is automatically aligned, which makes the experience very soothing to use.
What do you dislike about the product?
There isn't anything in particular that I dislike about Dataiku. However, one area for improvement would be better management of the history and recent code I've worked on. It would be helpful if this information were more easily accessible and visually highlighted.
What problems is the product solving and how is that benefiting you?
Managing large datasets was my primary challenge. Having access to a unified portal for both geospatial and other data, along with the required processing power, has been crucial for achieving my objectives as a data scientist. The ability to test various machine learning models in one shot is simply revolutionary. I cannot imagine ever going back to working on my PC for these tasks.
Dataiku : Making your Data Science work easy
What do you like best about the product?
I find the platform very easy to use, which makes it great for quickly prototyping and getting your MVP out as soon as possible. It's also simple to plug and play, which really speeds up the process.
What do you dislike about the product?
I find the documentation somewhat incomplete, with few tutorials available. It can be a struggle to find solutions when I need help.
What problems is the product solving and how is that benefiting you?
Both MVP and end-to-end approaches allow for rapid use case development, but when it comes to building large-scale, scalable solutions with real impact, the process can be more challenging.
Dataiku for Data Science/AI projects
What do you like best about the product?
SImple to use & scale. Flexibity & integrated well into the any infra.
What do you dislike about the product?
The main drawbacks of Dataiku is cost, scalability limitations, integration complexity, performance issues, and the need for user training.
What problems is the product solving and how is that benefiting you?
Dataiku addressed critical issues in data quality, operational efficiency, analytics collaboration, AI scalability, compliance, and business-user empowerment, serving as a unified platform for enterprise data innovation and value generationtion.
A Powerful Platform for End-to-End Data Science & Collaboration
What do you like best about the product?
Dataiku is excellent for managing the entire data pipeline from data preparation to machine learning and deployment. The best part is it easy to implement. The best part is how it allows both technical and non-technical users to collaborate on the same platform. Visual workflows make it easy to build projects without heavy coding, while advanced users can still dive deep with Python, R, or SQL. The integration with cloud platforms and version control is also very smooth.
What do you dislike about the product?
The platform can feel heavy for smaller projects, and the initial learning curve is a bit steep for beginners. Also, the licensing costs can be high for small companies or startups.
What problems is the product solving and how is that benefiting you?
For me, Dataiku mainly solves the problem of collaboration between technical and non-technical teams. Earlier, a lot of time used to get wasted when data scientists, analysts, and business teams worked separately and had to constantly exchange files and reports. With Dataiku, we can all work on the same platform data cleaning, model building, and visualization happen in one place. It also saves me from doing repetitive manual tasks since a lot of workflows can be automated. Overall, it has made our data projects faster, more transparent, and easier to manage.
Great website and great platform!!
What do you like best about the product?
It brings together data people analysts, engineers, scientists on one platform
What do you dislike about the product?
honestly, is that it can feel a bit heavy and slow, especially on large projects with a lot of visual recipes or datasets.
What problems is the product solving and how is that benefiting you?
Collaboration gaps, i think it brings data scientists, analysts, engineers, and business users into one shared workspace
Emerging player in Data Science
What do you like best about the product?
One of the best feature in Dataiku is No code feature which can help resources who are not comfortable in coding. It supports Python/R libraries and workflow playbook.
What do you dislike about the product?
Still expensive solution for implementation.
What problems is the product solving and how is that benefiting you?
Generally Dataiku helped us to build Data Science projects and predictive analysis to build some KPI's.
Experience using Dataiku
What do you like best about the product?
I like how intuitive is to use Dataiku, there are many features that reminds me of a blend of SQL, excel, and python.
What do you dislike about the product?
It can be difficult if you wanna implement advanced tools such as python in the flow.
What problems is the product solving and how is that benefiting you?
Analyzing fraudulent transactions, uncovering hidden patterns.
Dataiku in my company
What do you like best about the product?
It makes our lives easier and it helps us detect opportunities and risks.
What do you dislike about the product?
Lack of information when getting new updates
What problems is the product solving and how is that benefiting you?
It's helping us solve optimization problems as well as demand prediction in the short and long term.
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