Dataiku for Enterprise AI

What do you like best about the product?

What I like most about Dataiku is how much faster it lets me move on marketing data projects for clients. In my day-to-day work as a digital marketing consultant, I often have to bring together data from multiple sources—CRM exports, campaign performance reports, website analytics, sales data, and sometimes offline datasets. Dataiku gives me a structured environment to clean, connect, and analyze everything in one place, without having to rebuild the entire process from scratch each time.

I use the visual workflows regularly because they make the end-to-end process far more transparent. Rather than working only in spreadsheets or in isolated scripts, I can see every step of the data preparation flow and explain it clearly to clients or internal teams. This is especially helpful when I need to show exactly how a lead scoring model, a customer segmentation analysis, or a campaign performance dataset was created.

Another thing I really value is the balance between no-code and code options. For everyday consulting work, it’s practical: I can move quickly with visual recipes for common tasks, and then go deeper with SQL or Python when the analysis needs more flexibility. That saves time and makes it easier to adapt the workflow to the complexity of each project.

Dataiku also improves collaboration with non-technical stakeholders. When I’m working with marketing managers or sales teams, they don’t always need the technical details, but they do need to trust the output. Having a clear, documented workflow makes conversations smoother and helps translate analysis into concrete marketing decisions.

Overall, the biggest benefit for me is that Dataiku turns complex data preparation and analysis into a repeatable consulting workflow. It helps me spend less time on manual data cleaning and more time interpreting results, spotting opportunities, and recommending actions to improve campaign performance, customer targeting, and ROI.

What do you dislike about the product?

What I dislike about Dataiku is that it can feel a bit heavy at the beginning, especially if the team is not already familiar with data workflows, data preparation logic or machine learning concepts. As a consultant, I can usually navigate the platform quite well, but when I involve clients or marketing teams who are less technical, there is sometimes a learning curve before they feel comfortable using it independently.

The pricing can also be a limitation, especially for smaller clients or companies that are still at an early stage in their data maturity. Dataiku can deliver strong value when it is used regularly across multiple projects, teams and data sources, but for a smaller marketing team that only needs occasional analysis, it may feel like a significant investment. The ROI is much clearer when the company is ready to operationalize data workflows, not just run one-off reports.

In terms of onboarding, I think the platform requires a structured introduction to get the most out of it. There are many features, which is a strength, but it can also be overwhelming at first. For some clients, I need to spend extra time explaining not only how the tool works, but also how to think in terms of reusable data pipelines instead of simple spreadsheet-based analysis.

Regarding AI and machine learning, the capabilities are powerful, but they still require good data quality and a clear business objective. Dataiku can help a lot with automation and predictive models, but it does not replace the strategic work of defining the right question, selecting the right variables and interpreting the results correctly. In my daily work, I still need to guide clients carefully so they do not treat AI outputs as automatic answers without proper validation.

So overall, my main dislike is not about a single missing feature, but about the complexity that comes with such a complete platform. It is very useful, but it needs the right level of adoption, training and business commitment to fully justify the investment.

What problems is the product solving and how is that benefiting you?

Dataiku helps me solve one of the main problems I face as a digital marketing consultant: fragmented data. In many client projects, marketing data is spread across different platforms, spreadsheets, CRM systems, advertising accounts and analytics tools. Before using a structured platform like Dataiku, a lot of time could be lost just cleaning files, matching columns, checking inconsistencies and preparing the data before any real analysis could begin.

With Dataiku, I can create more repeatable workflows for tasks like campaign performance analysis, customer segmentation, lead scoring, churn analysis and ROI reporting. This benefits me because I do not have to start from zero every time a client sends updated data. Once the workflow is built, I can refresh the inputs, review the outputs and focus more on insights and recommendations.

It also helps me reduce manual errors. When working only with spreadsheets, it is easy to lose track of formulas, versions or manual changes. Dataiku makes the process more structured and transparent, so I can better control how the data is transformed and explain the logic behind the final results to clients.

Another important benefit is that it helps me move from simple reporting to more advanced decision support. Instead of only showing what happened in a campaign, I can help clients understand patterns, identify high-value segments, predict possible outcomes and prioritize marketing actions more effectively.

Overall, Dataiku saves time, improves the reliability of my analysis and helps me deliver more strategic value. It allows me to spend less energy on repetitive data preparation and more time advising clients on what to do next.

What do you like best about the product?

What I like most about Dataiku is the way it balances ease of use with strong analytics and AI capabilities. It allows business users, analysts, and data scientists to collaborate smoothly within the same platform. The combination of visual workflows and the flexibility to code when needed makes it straightforward to go from experimentation to production more quickly.

What do you dislike about the product?

One thing I dislike about Dataiku is that it can feel bulky and resource-heavy at times.
It tends to save/cache data in many places, which can make workflows harder to manage and slower to navigate.
For hardcore technical teams that prefer lightweight, code-first tooling, it may feel slower and more restrictive than working directly with native engineering stacks.

What problems is the product solving and how is that benefiting you?

Dataiku solves the problem of fragmented data, disconnected teams, and slow AI deployment by bringing data prep, analytics, and machine learning into one platform.
It benefits me by making collaboration between business and technical teams much smoother and reducing the time needed to move projects from experimentation to production.

What do you like best about the product?

Template solutions to speed up implementations

What do you dislike about the product?

The template solutions don't expose the code behind so it's sometimes hard to understand how the features work (documentation is not sufficient)

What problems is the product solving and how is that benefiting you?

Quick prototyping of solutions

What do you like best about the product?

All the integrations you can add in Dataiku are really powerful.

What do you dislike about the product?

Im not a user but i have an interest in dataiku

What problems is the product solving and how is that benefiting you?

As a data scientist, I feel the next update in June 2026 would transform my job and make it much easier and more efficient, especially for doing PoCs.

What do you like best about the product?

Having easy to use recipes with an easy and simple way to setup scenarios

What do you dislike about the product?

It’s not that I dislike this, but I want to have easier tool to use AI with step by step tutorials

What problems is the product solving and how is that benefiting you?

Everything is consolidated into one environment. I have the ability to do so much things in Dataiku

What do you like best about the product?

The simplicity to analyse data, results, and the gain of time compared to doing all that in python in a classic IDE

What do you dislike about the product?

Recently I did not like how difficult and long it was to add input files in managed folders. I could not upload subfolemders for exemple. Also multiple times when I uploaded large number of files, some were not uploaded.

What problems is the product solving and how is that benefiting you?

I work in data science and we do everything in dataiku

What do you like best about the product?

I think the user interface of Dataiku is very user-friendly. Even if you don't have a strong data science or data engineering background, you can still use it by drawing boxes, which makes it accessible for many people. I also like that you can customize your solutions by writing your own code to cater to specific business needs. Additionally, with its fast-paced development, Dataiku regularly updates and upgrades the system to include the latest AI features, which I find awesome. The graphical, no-code environment significantly reduces my development life cycle, saving at least 50% of my time. It also makes interaction with end users easy because they can access our development environment to see progress and give quick feedback.

What do you dislike about the product?

So first of all, I think I got some limitation that you to be honest with you, because let's say, if you want to display and visualize a large dataset, it always has some limitation. And, also, I find out the dashboard in built by the API is not super fancy and super user friendly. Comparing to Power BI or the other visualization tools like Tableau, I think that's something that you can improve as well. Other main pinpoint for us is about the deployment. Because, you need to link to the different development, the requirements, how to deploy our AI solution, particularly to another cloud form. For example, AWS Azure, I think that we need a little bit more support on this.

What problems is the product solving and how is that benefiting you?

Dataiku helps me build AI solutions like multi-agent systems, handling both test images and numerical data. It significantly reduces my development life cycle by 50% and enhances collaboration by allowing quick user feedback, leading to faster project iterations.

What do you like best about the product?

I like that Dataiku makes data analysis more visual and less painful. I appreciate the flexibility of the solutions available, such as the ability to host custom Python webapps, use Python filters, build custom pipelines, and create custom scenarios. The initial setup was super easy after doing the trainings.

What do you dislike about the product?

Webapp code management is challenging because it involves working with one big file, and the limited Python API calls are restrictive.

What problems is the product solving and how is that benefiting you?

I find Dataiku makes data analysis more visual and less painful.

What do you like best about the product?

Very easy to use and numerous use cases.

What do you dislike about the product?

I don’t dislike that much - nothing to declare here

What problems is the product solving and how is that benefiting you?

Data transformation, reconciliation, machine learning

What do you like best about the product?

I do enjoy greatly the visual recipes and ease of use

What do you dislike about the product?

I dislike the fact that insights sometimes are just a snapshot in time, not re-usable

What problems is the product solving and how is that benefiting you?

It is solving data and analytics problems