Implementing and leveraging Data Quality Scores (DQS) with Secoda can transform your data management practices. By following these actionable steps, your data team can enhance data quality, streamline processes, and drive better decision-making with minimal custom work.
This is part three of our DQS series. Part 1 and Part 2
1. Secure stakeholder buy-in
Present the value proposition:
Start by explaining the significance of data quality and the specific benefits of Secoda’s DQS feature. Highlight how improving data quality can lead to better decision-making, increased operational efficiency, and significant cost savings. Additionally, address the potential risks of poor data quality, including compliance issues and increased business costs.
Showcase quick wins:
Demonstrating the immediate advantages of Secoda’s Data Quality Score (DQS) feature helps secure early stakeholder buy-in and proves its value right from the start. Here are some quick wins to highlight:
- Instant insight into data health: With Secoda’s DQS, stakeholders can instantly gain a high-level view of the data quality across critical datasets. This helps quickly identify areas of concern and prioritize where improvements are needed most, giving the team a fast and clear starting point.
- Automated data quality suggestions: Secoda’s DQS automatically generates actionable suggestions to improve data quality, such as adding missing documentation or fixing data consistency issues. These recommendations allow teams to make small but impactful changes without needing complex custom work.
- Enhanced trust and usability: The visibility and transparency provided by the DQS build immediate trust in the data across teams, improving data usage by business users and reducing hesitation or errors when using the data for decision-making.
- Fast reduction in manual audits: Secoda’s automation tools, like the AI-generated documentation and real-time monitoring, allow data teams to quickly reduce the time spent on manual auditing and increase operational efficiency.
2. Identify critical data elements
Prioritize impactful data:
Begin by identifying the data elements that most impact your business. Critical data elements can be identified in Secoda in many ways:
- Leverage Secoda’s DQS analytics: Utilize insights from the DQS Analytics page to see how different teams are performing in terms of data quality.
- Use Secoda’s lineage features: Identify key nodes in your data pipeline that interact with multiple parts of the system.
- Analyze popularity metadata: Use Secoda’s popularity metadata in the Catalog to sort by the most frequently used resources.
- Automate PII tagging: Review what Secoda has automatically identified as potential PII, and prioritize this sensitive data to ensure it meets the highest quality standards. This is especially important for organizations dealing with sensitive information, such as healthcare, fintech, and others.
3. Define clear policies
Set clear documentation guidelines:
- Create specific guidelines for data documentation, which might include using Templates in Secoda to ensure consistency across the team.
- Outline what information must be included in data definitions (e.g., descriptions, sources, data types).
- Provide clear instructions on how to use custom tags effectively to maintain organized and searchable data.
Establish ownership standards:
- Decide how data ownership will be defined within your organization.
- Clearly communicate what it means to be an "owner" of data, such as the responsibility to keep documentation up-to-date on a set cadence.
- Ensure owners are aware of their duty to notify other users of any changes to the data or its documentation.
4. Implement a Crawl-Walk-Run approach
- Crawl: Start Small Begin with a pilot project that focuses on a manageable subset of your data, maybe a particular business unit with the highest impact data.
- Walk: Expand Gradually Gradually increase the amount of data you’re working with, incorporating more datasets as you refine your processes.
- Run: Scale Across All Data Once the initial phases are successful, expand your efforts to include all data across the organization, ensuring comprehensive coverage and consistency.
5. Leverage Secoda’s built-in tools
After establishing your policies and identifying the critical data to start with in the crawl stage, it’s essential to delegate responsibilities effectively. Data owners should take charge of managing the Data Quality Scores (DQS) for their respective data sets.
Assess current data quality:
- Evaluate your data: Use Secoda’s built-in data quality scores to thoroughly evaluate your data across key dimensions like Stewardship, Usability, Reliability, and Accuracy.
- Understand the baseline: This assessment provides a clear baseline of your current data quality, helping to identify specific areas that need improvement.
- Detailed breakdown: Each table and column in your dataset will have its own DQS, complete with a detailed breakdown of the score, explanations for any deficiencies, and Secoda's suggestions for improvement.
- Automated auditing: Secoda automates this audit process, making it straightforward and time-saving for data owners to maintain and improve data quality.
Set data quality goals:
- Define realistic targets: Based on your initial assessment, set clear and achievable data quality goals that align with your organization’s overall data strategy.
- Create a roadmap: Develop a roadmap that outlines the steps needed to reach your desired data quality scores. This roadmap should include timelines, milestones, and responsible parties to ensure accountability.
Follow Secoda’s automated suggestions:
- Monitor key assets: Some of Secoda’s suggestions will involve setting up data quality monitors on critical data assets. Secoda’s no-code monitoring capabilities make it easy for a wide range of users to participate in data governance, regardless of technical expertise.
- Enhance documentation: Other suggestions may focus on improving documentation by adding detailed descriptions and relevant tags to your resources. Secoda offers a suite of automation tools to streamline this process, including an AI description editor that can generate bulk descriptions with a single click.
- Customize and automate: Customize the AI-generated descriptions in Secoda’s AI settings to align with your organization’s documentation standards. You can also set up Automations to update documentation rules across the board, such as automatically tagging new and existing confidential data.
6. Educate and track progress
Educate the team:
- Training and resources: Provide comprehensive training sessions and resources to ensure all team members understand the importance of data quality and how to effectively use Secoda’s features.
- Foster a quality culture: Build a culture that values and prioritizes data quality from the top down. This cultural shift is crucial for sustaining long-term data governance efforts.
Track and adjust:
- Continuous monitoring: Use Secoda’s DQS dashboards to continuously track progress against your data quality goals.
- Adapt as needed: Regularly review your strategies and make adjustments as necessary to ensure that data quality improvements are ongoing and aligned with your evolving business needs.
Conclusion
By following these steps, your data team can effectively implement Data Quality Scores using Secoda’s robust and user-friendly features. This structured approach not only enhances decision-making but also provides a strategic edge in today’s data-driven environment. With Secoda, you can consolidate data cataloging, quality management, and documentation into a single platform, reducing data sprawl and optimizing infrastructure costs. This integration supports continuous data quality monitoring and ensures your data is clean, reliable, and AI-ready from the start.
