Trash in: Trash out. The old mantra for data quality holds as true today as it did years ago. With the rise of Generative AI and the push for self-service analytics, good data is in more demand than ever before. However, with this increase in demand and democratization, ensuring good data quality has become increasingly challenging. More and more of the governance framework is increasingly automated, and ensuring data quality across all data sources and data products becomes more difficult to handle.
These pressures mean that it’s more important than ever to ensure that data quality is operationalized throughout the data product development process. Without it, delivering value from AI projects becomes much more challenging. In fact, according to a survey done by Dataiku of AI professionals, the top challenge to getting more return on AI (ROAI) is the lack of data quality, while Forrester notes that “Data Quality is the Primary Factor Limiting AI Adoption.”
The Impact of Bad Data Quality: Reduced ROAI
In most companies, data quality issues are proactively identified by data management tools that govern data pipelines for the enterprise. Still, what happens when you have an analyst or data scientist working on creating a data pipeline or preparing data for an ML model, and they aren’t aware of data quality issues? You’ll get broken reports, model performance degradation, or dashboards that start to break down (and also break trust). To ensure good data quality for end data products, data practitioners should have a way to easily test the data quality and fix the issue, all in the same place.
New Data Quality Features in Dataiku
Dataiku recently launched new ways to operationalize and democratize access to data quality to help companies improve their ROAI. With new data quality dashboards and rules, those building analytics and ML solutions can easily identify and resolve data quality issues using the same tools with which they build data products. Learn more about data quality updates in this video.
This is a drastic enhancement to Dataiku’s approach to data quality and we anticipate that it will provide builders of data products:
- Better access to data understanding and data literacy so that they can ensure that analytics and AI solutions, including Generative AI, are accurate and trusted.
- Greater control over data quality and the ability to quickly identify and fix issues proactively.
- A unified view of data quality through comprehensive views at the data set, project, and instance level.
In a new tab available for each dataset, you can quickly set up and search through dozens of rule types, and quickly test them for easy validation. You can manually run rules with the click of a button, or set rules to automatically run after each dataset build.
Easily set up Data Quality Rules for your datasets using dozens of out-of-the-box rule types...
…Or create custom rules using Python or Plugins
Quickly identify issues in your data with a central dashboard. View the status at the dataset, project, and even instance level to quickly gain an understanding of your data across all of your Dataiku projects.
See the current status for computed rules in a single dashboard.
With new data quality views within the flow, you have even more ways to quickly assess data quality, so that you can quickly fix any issues in your data.
Visual cues give you a quick glance at data quality within the flow.
Improve Data Quality With Dataiku
Ultimately, by empowering users to take charge of data quality as part of the analytics lifecycle, you not only have more accurate results, you also increase efficiency across your analytics efforts. By using Dataiku to improve data quality, customers like Bankers’ Bank have been able to reduce the time to prepare insights and deploy analysis by 87%.