Dataiku vs SAP Predictive Analytics [EOL] comparison

Dataiku and SAP are both solutions in the Data Science Platforms category. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

Dataiku

Read 21 Dataiku reviews

7,664 Views
7,511 Comparison Views

100% willing to recommend

SAP Predictive Analytics [EOL]

Read 3 SAP Predictive Analytics [EOL] reviews

100% willing to recommend

Dataiku

SAP Predictive Analytics [EOL]

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Executive SummaryUpdated on Apr 15, 2026

SAP Predictive Analytics [EOL] and Dataiku compete in predictive analytics and machine learning. Dataiku appears to have an upper hand due to its extensive tool selection and collaborative capabilities, despite its potentially higher costs.

Features: SAP Predictive Analytics [EOL] offers seamless integration with SAP business environments, predictive modeling, and automation capabilities. Dataiku provides an extensive suite for data preparation, machine learning, and end-to-end project flow management. It features a collaborative interface and repeatable data processes, offering a robust solution for diverse data projects.

Ease of Deployment and Customer Service: SAP Predictive Analytics [EOL] benefits from integration simplicity within SAP ecosystems, enabling standardized deployment. Dataiku stands out with flexible deployment options, including cloud solutions, and strong customer support. It ensures seamless collaboration and accessibility across platforms, compared to SAP's focus on SAP environments.

Pricing and ROI: SAP Predictive Analytics [EOL] presents lower initial setup costs for SAP customers, leading to quicker ROI within this user base. Despite potentially higher upfront investment, Dataiku offers compelling ROI through its expansive feature set supporting long-term scalability and productivity across broader use cases.

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Dataiku

Average Rating

8.2

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Science Platforms (2nd)

SAP Predictive Analytics [EOL]

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

Shubhamkumar Gomar

Senior Data Scientist at Deloitte

Visual workflows have streamlined healthcare analytics and have reduced reporting time significantly

In terms of improvement, I cannot comment on the LLMs or the agentic view as I have not used them yet. However, I feel that better documentation is necessary. Dataiku should establish a stronger community since this is proprietary software, where users can share knowledge. Although they have some community interaction, it is often challenging to find assistance when stuck. For example, when I was new to Dataiku and trying to use an external optimization tool such as CPLEX, I struggled with resource directory linking to a project's notebook. Detailed documentation and community discussions could have significantly alleviated these issues for users such as myself.

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Gary Cook

Executive at Empowered Analytics

Enables us to forecast and pull trends and has an easy installation

My rating for SAP Predictive Analytics would be an eight out of ten. If I have to be bold, I'll probably say that we're building away hours, and we are actually putting a lot of the actual predicting stuff back into the warehouse. So running it very bi-directionally. So I'm not sure what its integration features are at the moment, but that's an area we're going to look into in the next month or so.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Dataiku has positively impacted my organization since we use it majorly for our day-to-day work, and it is very helpful in creating and managing ETL pipelines to create a project flow, making it easy to go back to any step and then make edits if some changes occur."

"Cloud-based process run helps in not keeping the systems on while processes are running."

"Technical support is really, really good."

"Compared to Informatica, this tool is extremely easy with its GUI-based functionality and large compatibility with various data sources, and maintenance processes are much more automated than ever, with fewer errors."

"I believe the return on investment looks positive."

"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."

"Using Dataiku has meant that we spend less time on preparing and cleaning data, and we spend less time on blending models together, ultimately meaning that we can spend more time modeling."

"I rate the overall product as eight out of ten."

More Dataiku pros

"The most valuable features are the analytics and reporting."

"I think the features of the actual ability to forecast and pull trends and correlations has been really good."

"SAP Predictive Analytics is better suited for business users because it hides the complexity of the model, whereas Microsoft Azure Machine Learning provides a lot more flexibility for technical professionals to tweak the model."

"I have found that the solution is very stable."

"We always purchase SAP support because it is very good."

Cons

"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code."

"There is room for improvement in terms of allowing for more code-based features."

"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."

"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."

"In terms of enhancing collaboration within my team, I would not say Dataiku is the best one because it's so expensive."

"I think the pricing and licensing of Dataiku is a bit expensive; it could be improved further, and I think they should have a different kind of licensing model as well."

"I would like to have better exclusion of data capability."

"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."

More Dataiku cons

"This solution works for acquired data but not live, real-time data."

"The license fee appears to be prohibitively expensive and overly secretive, leading our clients to opt for cloud-based solutions that only charge for data storage and processing time."

Pricing and Cost Advice

"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."

"Pricing is pretty steep. Dataiku is also not that cheap."

"The pricing is reasonable"

"A free trial version is available for testing out this solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

10%

Computer Software Company

Energy/Utilities Company

Outsourcing Company

19%

Construction Company

16%

Transportation Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	2
Large Enterprise	13

No data available

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?

The licenses are a bit high for companies that are still hesitating to get started with using Dataiku. For my personal projects, I used the thirty-day free trial. Regarding my company, I did not ha...

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What needs improvement with Dataiku Data Science Studio?

I have no suggestions for improvements because it's all good; it just sometimes lags a lot, and I don't know if the server is full or what, but it sometimes takes a lot of time while loading and re...

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What is your primary use case for Dataiku Data Science Studio?

My main use case for Dataiku involves ETL pipelines, mainly for data analysis, and I majorly use SQL queries for that. For ETL pipelines and data analysis, I had to create the output by combining a...

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Also Known As

Dataiku DSS

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

Dataiku

SAP Predictive Analytics [EOL] offered a powerful platform for creating predictive models that supported business decision-making by utilizing historical data to anticipate future trends.

SAP Predictive Analytics [EOL] was designed to integrate with existing SAP environments, allowing businesses to leverage their existing data infrastructure. It provided users with intuitive tools to automate data preparation and model management, simplifying complex analytical processes. Data scientists could efficiently build and deploy predictive models to address specific business questions. SAP emphasized ease of deployment and scalability, ensuring the platform met the needs of data-driven enterprises.

What are the key features?

Automated Model Creation: Streamlines the process of generating predictive models, reducing time and effort.
Data Preparation: Simplifies data cleaning and preparation, ensuring high-quality input for analysis.
Seamless Integration: Easily integrates with SAP environments, enhancing data accessibility.

What benefits should users consider in reviews?

Informed Decision-Making: Provides insights that help businesses make data-informed decisions.
Efficiency in Analysis: Reduces the time needed to develop and deploy predictive models.
Scalability: Supports expanding data needs as businesses grow.

In industries like manufacturing and retail, SAP Predictive Analytics [EOL] helped optimize supply chains and inventory management by forecasting demand trends. Financial sector users implemented it to enhance risk analysis and fraud detection models, providing valuable insights for mitigating potential risks.

SAP

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

mBank

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