Data quality issues undermine the reliability of analytics projects, posing significant challenges for analytics leaders and IT teams. In a recent Product Days session, Lauren Anderson and Jean-Guillaume Appert explored how Dataiku’s embedded data quality features can help you build trust in your data projects. Learn how you or your teams can easily identify, and fix issues, all within the same platform that you’re building analytics projects.
Why Data Quality Is Urgent to Fix
When you think about data quality, it's actually an issue that gets worse the longer you wait. If you haven't seen it, this is a pretty famous study that shows that prevention is basically the cheapest way to approach data quality issues.
If you're able to verify a record at the point of entry, it only costs a dollar. But if you wait to the point where you have to actually correct it and you're doing your cleansing, your deduplication, etc., that actually costs 10 times more than if you just got it at the point of entry — the correction cost.
And then finally, when you get to the end, the point of failure where you’ve missed the issue, it's gone into the business application, the machine learning model, etc., — this is your point of failure and your failure costs 100 times more than if you just fixed it initially.
The Persistent Challenges of Data Quality
The problems with data quality are not new. Over 20 years ago, when that study came out, organizations faced similar challenges, but the stakes have risen exponentially. Data's complexity, volume, and speed of ingestion have grown, introducing new hurdles:
- Data Complexity: Modern data comes from myriad sources in varied formats, both structured and unstructured. Unifying this data to make it usable is a massive challenge.
- Velocity of Data: Organizations ingest data at unprecedented speeds. Maintaining its quality in real time is a daunting task.
- Lack of Ownership: A recurring issue is unclear accountability for datasets. Without clear data stewardship, problems go unresolved, and quality suffers.
- Tool Fragmentation: The vast array of data tools — ranging from ingestion to quality monitoring — often creates confusion. Teams struggle to identify which tools to rely on.
- Organizational Silos: IT and business teams frequently operate in isolation, leading to inconsistent data results and lack of trust across the organization.
The Generative AI Factor
Adding to this complexity is the rise of generative AI and large language models (LLMs). These technologies depend on high-quality data to perform efficiently. Poor data quality leads to flawed outputs, eroding trust and undermining AI initiatives. As organizations explore generative AI, ensuring data quality becomes the cornerstone of success.
Building Trust Through Data Quality
To overcome these challenges, organizations must embed data quality practices across their operations. At its core, this means:
- Discovering and Understanding Data: Analysts need intuitive tools to explore datasets, identify issues like missing values or outliers, and prepare data for analysis. Platforms like Dataiku offer visual indicators and streamlined workflows to flag and address data quality problems efficiently.
- Fostering Collaboration: Data quality is a team effort. Platforms that support collaboration across technical and non-technical users help unify teams, enabling low-code, no-code, and coding experts to work together. Shared pipelines and common data grammars facilitate a shared understanding and smoother workflows.
- Enhancing Feature Engineering: Feature engineering is crucial for building effective ML models. Tools that help data scientists tag, discover, and reuse trusted features ensure consistency and reduce the risk of leakage.
- Monitoring and Governance: Data quality is not a one-time fix. Continuous monitoring ensures datasets remain reliable. Features like data lineage enable organizations to trace data origins and assess the impact of changes, strengthening governance and accountability.
- Scaling Operationalization: Data pipelines must be scalable and shareable. Platforms that enable orchestration, workspace sharing, and impact analysis help organizations scale their efforts while maintaining quality.
Dataiku: Your Partner in Data Quality
At Dataiku, we believe that addressing data quality challenges requires a holistic, end-to-end approach. Our platform integrates data quality tools across the entire analytics lifecycle — from discovery to operationalization — empowering organizations to build trust in their data.
Key Features to Drive Data Quality:
- Visual Profiling and Insights: Quickly identify missing values, errors, and outliers.
- Collaborative Pipelines: Foster teamwork across diverse skill sets.
- Feature Store: Ensure consistent and reusable features for ML models.
- Data Lineage: Trace data transformations for better governance and troubleshooting.
- Continuous Monitoring: Stay proactive with automated alerts for data drift and quality issues.
The Value of Collaboration
Ultimately, data quality is about teamwork. By enabling cross-functional collaboration and embedding quality checks into every step of the process, organizations can unlock the true potential of their data. With Dataiku, you not only address today's challenges but also prepare for the future — where trust in data is the foundation of every decision.