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A partner worth its weight in gold
What do you like best about the product?
As someone steering data strategy in wealth management, where client trust and regulatory rigor are non-negotiable, finding a platform that balances innovation with governance is paramount. After 18 months of using Dataiku across our global teams, here’s my candid take.
Likes
1. Collaboration That Bridges Silos
Dataiku’s unified environment has been transformative for breaking down walls between our quants, business analysts, and risk teams. For instance, building client segmentation models used to take weeks of back-and-forth. Now, data scientists prototype in Python while business analysts tweak logic visually, accelerating time-to-insight. One standout moment: A high-net-worth portfolio risk tool was co-developed by our quant team and advisors in half the usual time, thanks to shared workflows.
2. End-to-End Governance
In wealth management, audit trails are lifeblood. Dataiku’s granular permissions and data lineage tracking (who did what, when) have made SOX and GDPR audits less painful. We recently traced a model’s decision logic back through six months of iterations during a regulatory review—without breaking a sweat.
3. Flexibility for Hybrid Use Cases
Whether it’s batch-processing historical portfolio performance or real-time dashboards for advisors, Dataiku handles both gracefully. The integration with Snowflake and Tableau streamlined our migration to cloud-native analytics, while plugins for Bloomberg APIs let us pull market data without custom coding.
Likes
1. Collaboration That Bridges Silos
Dataiku’s unified environment has been transformative for breaking down walls between our quants, business analysts, and risk teams. For instance, building client segmentation models used to take weeks of back-and-forth. Now, data scientists prototype in Python while business analysts tweak logic visually, accelerating time-to-insight. One standout moment: A high-net-worth portfolio risk tool was co-developed by our quant team and advisors in half the usual time, thanks to shared workflows.
2. End-to-End Governance
In wealth management, audit trails are lifeblood. Dataiku’s granular permissions and data lineage tracking (who did what, when) have made SOX and GDPR audits less painful. We recently traced a model’s decision logic back through six months of iterations during a regulatory review—without breaking a sweat.
3. Flexibility for Hybrid Use Cases
Whether it’s batch-processing historical portfolio performance or real-time dashboards for advisors, Dataiku handles both gracefully. The integration with Snowflake and Tableau streamlined our migration to cloud-native analytics, while plugins for Bloomberg APIs let us pull market data without custom coding.
What do you dislike about the product?
Dislikes**
1. Learning Curve for Non-Tech Stakeholders
While analysts love the visual interface, our senior advisors initially struggled to embrace self-service dashboards. We’ve sunk hours into training, and even now, some revert to “just email me the PDF.” Dataiku’s business user onboarding feels half-baked compared to Power BI.
2. Real-Time Analytics Gaps
For high-frequency trading scenario, Dataiku’s real-time capabilities lag. We had to bolt on Apache Kafka for live bond pricing alerts—a costly workaround.
3. Performance Hiccups at Scale
A European client’s portfolio—10+ years of hourly trades across 20k assets—brought Dataiku to its knees. We ended up pre-aggregating data in Snowflake, which defeated the purpose of “in-platform” big data tools.
1. Learning Curve for Non-Tech Stakeholders
While analysts love the visual interface, our senior advisors initially struggled to embrace self-service dashboards. We’ve sunk hours into training, and even now, some revert to “just email me the PDF.” Dataiku’s business user onboarding feels half-baked compared to Power BI.
2. Real-Time Analytics Gaps
For high-frequency trading scenario, Dataiku’s real-time capabilities lag. We had to bolt on Apache Kafka for live bond pricing alerts—a costly workaround.
3. Performance Hiccups at Scale
A European client’s portfolio—10+ years of hourly trades across 20k assets—brought Dataiku to its knees. We ended up pre-aggregating data in Snowflake, which defeated the purpose of “in-platform” big data tools.
What problems is the product solving and how is that benefiting you?
Dataiku isn’t perfect, but it’s the closest we’ve found to a Swiss Army knife for wealth management’s unique demands. The collaboration and governance features alone justify the investment, though I wish the pricing and real-time gaps were addressed. For firms ready to invest in training and hybrid architectures, it’s a powerhouse. Just don’t expect it to replace your entire stack overnight.
Would I recommend it? Absolutely—but with a caveat: Treat it as a marathon partner, not a sprinting miracle worker.
Would I recommend it? Absolutely—but with a caveat: Treat it as a marathon partner, not a sprinting miracle worker.
One-stop shop for all analytics and ML needs
What do you like best about the product?
I love how Dataiku allows for such seamless creation and implementation of ML models. It offers the ability to seamlessly collaborate and integrate with workflow with colleagues.
What do you dislike about the product?
Dataiku has a decent learning curve if you are coming from a non-technical background.
What problems is the product solving and how is that benefiting you?
Dataiku is allowing me to solve data analytics problems that maybe take a few minutes in Python in mere seconds. It saves me a considerable amount of time in the long run.
Very user friendly
What do you like best about the product?
I think super friendly. And best part about it it’s all in one. You do not have to navigate to multiple applications to from building the model or creating a flow.
What do you dislike about the product?
So far I don’t have any complaints about Dataiku it’s working fine for me.
What problems is the product solving and how is that benefiting you?
It’s providing me a platform where I perform all the data curation as well as analysis of data in single platform. And provide insights to stakeholders.
Great overall experience
What do you like best about the product?
The ease of use low code no code so all users can use
What do you dislike about the product?
Explanation on new capabilities because new ones come out
What problems is the product solving and how is that benefiting you?
Helping different teams connect on different projects regardless of level
Easy and flexible tool for any type of user
What do you like best about the product?
Dataiku is a user-friendly and powerful data science platform that enables both technical and non-technical users to build, deploy, and manage AI projects collaboratively. With visual workflows, AutoML, and support for Python, R, and SQL, it balances simplicity with flexibility. Its strong integration, governance, and MLOps features make it ideal for scaling data initiatives across teams and industries. Great for accelerating data-driven decision-making.
What do you dislike about the product?
None.
Sometimes tool gets stuck in running complex jobs
Sometimes tool gets stuck in running complex jobs
What problems is the product solving and how is that benefiting you?
Quick analytical insights to daily business decisions
Dataiku user experience
What do you like best about the product?
Very user friendly, and has a lot of capabilities for data accessibility, data quality, test and create POCs
What do you dislike about the product?
We don't have latest version at the moment, so I wish it was easier to get access to plug-ins in a test environment , to see different capabilities.
What problems is the product solving and how is that benefiting you?
Ability to easily create analysis, ML models, and being one stop location as end user to access all the enterprise data
Its UI allows me to navigate easily
What do you like best about the product?
Allows us to reduce time in model demployment
What do you dislike about the product?
When will it provide a full suite of model risk mgmt products
What problems is the product solving and how is that benefiting you?
Reduce time to market
Great tool for performing Everyday Data Science & AI
What do you like best about the product?
The features and capabilities of the Dataiku. I find Dataiku as a one stop shop for doing everyday analytics, data science and AI.
What do you dislike about the product?
The infrastructure setup to host Dataiku stack on-premise was very painful. It took over 2 months just to setup the infrastructure to get Dataiku running on our cloud.
What problems is the product solving and how is that benefiting you?
1) It provides access to some of the most challenging to implement ML algorithms with just a click.
2) Helps fast track our journey through Data Science and ML use cases.
3) The low code and no code capabilities helps non programmers to get started with ML development and deployment quickly and efficiently.
2) Helps fast track our journey through Data Science and ML use cases.
3) The low code and no code capabilities helps non programmers to get started with ML development and deployment quickly and efficiently.
Great tool for anyone
What do you like best about the product?
You can do a lot with just drag-and-drop components, easy to use for people from non technical background. You can bring in your own models, connect to huge data sources, and even integrate with most of the tools.
What do you dislike about the product?
For large datasets, processing might slow down if it's not well-optimized or errors in joins can lead to recipes running forever. Could prompt some warning to handle such cases
What problems is the product solving and how is that benefiting you?
Dataiku solves the problem of siloed workflows by uniting data prep, modeling, and deployment in one platform. For me, that means I can prototype a model, share it with stakeholders, and automate retraining
Dataiku user
What do you like best about the product?
It’s for beginner to someone who has been in the It industry for several year. The AI features and what we really value and they know what their customers want.
What do you dislike about the product?
The ease of use and the feature rich interface.
What problems is the product solving and how is that benefiting you?
Data wrangling and ML operations.
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