Data Quality in Dataiku

Harnessing the potential of your data starts with ensuring its quality. With data quality features in Dataiku, have confidence that your insights are built on a solid foundation. Track, verify, and fix data quality so that you can deliver powerful (and trusted) insights.


Operationalize Data Quality Rules

Operationalize your approach to data quality organization-wide. With data quality rules in Dataiku, anyone from data engineers to analysts can quickly identify data quality issues in one central analytics and AI platform.

See Data Quality Rules in Action

Visualize Data Quality With Powerful Dashboards & Views

Getting a full view of data quality status across projects is important to getting a full picture of your data. With dashboard views at the dataset, project, and instance levels, easily track data quality across the organization.


Discover Data Quality Attributes With Ease

With Dataiku, you gain a visual understanding of your data quality issues. Each column in a dataset contains a data quality bar for quick visual understanding along with an analyze option that automatically populates relevant details such as count, top values, and percent valid or empty.


Improve Data Quality With Robust Exploratory Data Analysis

Proactively identify and rectify data flaws to ensure improved machine learning models, reliable algorithms, and informed business strategies. With statistical techniques such as univariate and bivariate analysis, hypothesis testing, and what-if analysis, understand the overall structure of your data for better understanding and interpretation.


Unify Data Pipelines in a Centralized Space

The Dataiku flow provides a visual representation of a project’s data pipeline and is the central space where data and domain experts view and analyze data, add recipes to join and transform datasets, and build predictive elements.You can also see data quality status and even compute data quality rules directly from the flow.


Leverage Powerful, Reusable Recipes

Easy-to-use visual interfaces allow analysts and others to join datasets, group, aggregate, clean, transform, and enrich data — all with a few clicks. You can even use the power of Generative AI to automate transformations with simple instructions. This means that you have a common framework for transformations to prevent inadvertent data quality issues.


Centralize Trusted Data & Foster Collaboration

Consolidate data and analytical capabilities for seamless access and collaboration. Transform reliable data into actionable insights for informed decision making, and view data quality status across datasets. Enable effortless sharing and consumption of data projects for improved collaboration in respect of predefined control access.

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