Dataiku Customer Reviews 2026 | Machine Learning Platforms

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Dataiku

Composite Score

8.2 /10

CX Score

8.5 /10

33 Reviews Write a Review

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What is Dataiku?

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way. With Dataiku, everyone can get involved in data and AI projects on a single platform for design and production that delivers use cases in days, not months. No matter where they sit, they work in a safe and governed way that helps manage risk and create trust to drive high-quality outputs and value for your business.

Company Details

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Awards & Recognition

Dataiku won the following awards in the Machine Learning Platforms category

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3 Top Rated Capabilities

12 Top Rated Features

Latest Award, 2026

Strategy and Innovation
Service Experience
Conflict Resolution

2025

2 Top Rated Capabilities

6 Top Rated Features

2025

Strategy and Innovation
Service Experience
Product Impact
Conflict Resolution

2024

5 Top Rated Capabilities

8 Top Rated Features

2024

2023 Emotional Footprint Champion

2023

Strategy and Innovation
Service Experience
Product Impact
Contract Negotiation
Conflict Resolution

2023

2023 Data Quadrant Champion

2023

4 Top Rated Capabilities

3 Top Rated Features

2023

2022 Emotional Footprint Champion

2022

Strategy and Innovation
Service Experience
Product Impact
Contract Negotiation
Conflict Resolution

2022

2022 Data Quadrant Champion

2022

4 Top Rated Capabilities

6 Top Rated Features

2022

Dataiku Ratings

Real user data aggregated to summarize the product performance and customer experience.
Download the entire Product Scorecard to access more information on Dataiku.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

88 Likeliness to Recommend

1
Since last award

98 Plan to Renew

1
Since last award

80 Satisfaction of Cost Relative to Value

1
Since last award

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Emotional Footprint Overview

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+97 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love Dataiku?

0% Negative

2% Neutral

98% Positive

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

Feature Ratings

Average 84

Data Pre-Processing

Model Training

Feature Engineering

Data Labeling

Openness and Flexibility

Data Exploration and Visualization

Ensembling

Algorithm Diversity

Data Ingestion

Model Tuning

Performance and Scalability

Vendor Capability Ratings

Average 80

Ease of Data Integration

Usability and Intuitiveness

Vendor Support

Breadth of Features

Quality of Features

Business Value Created

Ease of Implementation

Ease of Customization

Ease of IT Administration

Availability and Quality of Training

Product Strategy and Rate of Improvement

Also Featured in...

Data Preparation for Analytics

Score 8.3

View Dataiku in this category (opens in a new tab)

Dataiku Reviews

Makhlouf O.

Role: Operations
Industry: Banking
Involvement: IT Leader or Manager

Validated Review

Verified Reviewer

Submitted Mar 2022

Easy to use, friendly user

Likeliness to Recommend

8 /10

What differentiates Dataiku from other similar products?

User Friendly Interoperability Uses all open sources big data or local data langages

What is your favorite aspect of this product?

Easy to develop and industrialize projetcs

What do you dislike most about this product?

black box effect in some behaviours.

What recommendations would you give to someone considering this product?

take poc firstly, test all perfomances, and requierement according to environnement

Pros

Helps Innovate
Continually Improving Product
Reliable
Enables Productivity

Dataiku

What is Dataiku?

Company Details

Need Assistance?

Awards & Recognition

2023 Emotional Footprint Champion

2023 Data Quadrant Champion

2022 Emotional Footprint Champion

2022 Data Quadrant Champion

Dataiku Ratings

88 Likeliness to Recommend

1 Since last award

98 Plan to Renew

1 Since last award

80 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+97 Net Emotional Footprint

How much do users love Dataiku?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

Model Training Ability to train a model with a range of data sets that you provide.

Feature Engineering Ability to extract and build features from raw data.

Data Labeling Ability to annotate/label data to create a training data set.

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Ensembling 'Ability to combine ML models for best results.

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

Data Ingestion Ability to access data from various sources on-prem and in the cloud and of various type: structured, unstructured, and semi-structured, real-time, etc.

Model Tuning Ability to fine-tune models based on monitoring feedback within the platform.

Performance and Scalability Systems performance (time) and scalability end-to-end: from data load and processing to model creation and deployment.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Also Featured in...

Data Preparation for Analytics

Dataiku Reviews

Makhlouf O.

Easy to use, friendly user

Likeliness to Recommend

Pros

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Feature Ratings

Data Pre-Processing

Model Training

Feature Engineering

Data Labeling

Openness and Flexibility

Data Exploration and Visualization

Ensembling

Algorithm Diversity

Data Ingestion

Model Tuning

Performance and Scalability

Vendor Capability Ratings

Ease of Data Integration

Usability and Intuitiveness

Vendor Support

Breadth of Features

Quality of Features

Business Value Created

Ease of Implementation

Ease of Customization

Ease of IT Administration

Availability and Quality of Training

Product Strategy and Rate of Improvement