Dataiku Trial

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.

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.

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.

I have been using Dataiku for the last two years, but in data science, my experience spans more than ten years.

I have not experienced downtimes, crashes, or glitches with Dataiku in the project that we implemented.

Dataiku appears scalable, and in the project that we implemented, we did not face any scalability issues.

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.

Positive

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.

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.

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.

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.

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.

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.

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.

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.

I have been using this product for four years.

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.

For support, I haven't created any support tickets, so I really don't know about it, but it is quite good.

Positive

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.

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.

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.

Overall, Dataiku is really good. I would rate it an 8 out of 10.