According to a survey from Dataiku, the number one barrier to getting more ROI from AI is the lack of data quality or the ability to access the right data. And, a more recent survey of 200 IT leaders validated this data, showing that nearly half (45%) say data quality and usability remain the biggest data infrastructure challenge that they face. So how can data teams make sure that the data used to build ML models and analytics projects is trusted, validated, and accurate?
I recently joined Ben Gardner-Moss, Principal Analytics Consultant from Aimpoint Digital, in a webinar in which we talked through the main challenges associated with data quality, along with tips on how users can master data quality in Dataiku.
The Main Issues When It Comes to Data Quality
Ensuring data quality presents several common challenges that can impede effective decision-making. From data inconsistencies to duplicate entries and missing values, these are some of the most common challenges that Ben mentioned he sees when talking to clients:
- Accuracy: Does the data correctly reflect the items that it is supposed to measure?
- Completeness: Is all of the data you need for your analysis available?
- Consistency: Do the values in your different source systems align as expected?
- Timeliness: Is your data up-to-date and relevant for the analysis you intend to undertake?
- Validity: Does the data conform to the bounds of formatting and business rules?
How Dataiku Can Help
Luckily, Dataiku has embedded, as-you-go data quality features to give teams the ability to more effectively operationalize data quality across the analytics and AI lifecycle.
Dataiku’s Approach to Data Quality
Ben and I had the chance to demo some of the great features within Dataiku that help our users master data quality when building data products. Some of the key features covered in the webinar include:
- Data Quality Rules: Set up customizable rules to validate data quality on one or several columns in your datasets, and get a comprehensive view of status with dashboards and visual indicators at the dataset, project, and instance level.
- Visual Indicators and New Ways to Explore Data: Quickly gain an understanding of issues in the columns of your datasets, like the percentage of missing values, with visual indicators and explore using the analyze function to see things like mean, median, and average of numerical values, outliers, and more.
- Charts and Dashboards: Explore data relationships with built-in interactive charts that can help you quickly visualize issues with your data, and publish results to stakeholders in dashboards to create a shared understanding
- Automated Alerts: Using Dataiku scenarios, get automated alerts in email, Slack, Teams, or other platforms for data quality rules failures, build failures, and more so that you can proactively respond to data quality issues as they arise.
Want to see a demo of these features in action? Check out this replay and learn about these and other features firsthand so that you can take control of data quality in your analytics and AI projects today!