Does your organization have a strategy for data quality control? Improving data quality and reducing operational costs requires a solid plan. Establishing a strategy to manage data quality and set organizational standards is essential.
To develop and execute a data quality strategy, you need both a dedicated team and a well-defined process. Here’s how to structure your team and develop effective processes.
Building Your Team
Start by recruiting a data governance team. This team will set clear data definitions, create comprehensive policies, and oversee the documentation process. They ensure that data is collected, managed, and integrated properly across the organization.
Your team should include experts from various functions within the organization. A cross-functional team fosters a data-driven culture and creates data champions throughout the company.
At the helm of this team is typically the Chief Data Officer, who manages the team’s focus, communicates procedures, and monitors success. This leader forms an executive committee with leaders from different departments like finance and marketing. The committee is responsible for developing and overseeing data governance policies and processes.
Mid-level managers from different departments join the team to champion the data governance strategy and ensure collaboration across functions. They define processes, establish data quality metrics, and promote best practices.
Finally, the team assigns data owners, stewards, and users. Data owners manage compliance, administration, and access control. Data stewards act as intermediaries, interpreting data and creating reports. Users are responsible for entering and utilizing data daily and reporting any irregularities.
Define Scope
When implementing a data quality strategy, start with business processes that can benefit immediately from improved data quality. Choose projects with clear, identifiable issues. Begin with smaller projects for quick results, which will help garner executive support for larger initiatives. Each project should have a clear cost estimate and timeline.
Map Data to Key Business Processes
Once the initial project’s scope is defined, map the data flow within the process. Understanding how data moves through your organization and which business processes it impacts is crucial. This mapping helps you see the bigger picture and identify areas for improvement.
Analyze Financial Implications
After mapping the data flow, analyze the financial implications. Poor data quality might affect more areas than initially thought, revealing greater cost-saving opportunities. Collaborate with business management, accounting, and finance to ensure accuracy and gain support for future projects.
Select the Right Technology
Determine the technology needed for data quality evaluation. A diagnostics tool for data discovery and profiling is essential. This tool helps evaluate data set differences over time, quantify outcomes from cleansing, and estimate the project’s ROI.
Determine Data Quality Metrics
Choose metrics to capture the business impact of data quality initiatives. Metrics can range from simple to complex, depending on the data elements involved. Key indicators might include relevance, completeness, timeliness, accuracy, and consistency. Link these metrics to business initiatives to communicate the value of data quality projects.
Establish Best Practices
As you learn from each data quality project, establish best practices. Consistently using these practices will highlight the importance of data quality and influence a cultural shift within the organization. By demonstrating measurable results, you can advocate for the ongoing importance of data quality.
By following these steps, you can develop a robust strategy for managing data quality that supports your organization’s long-term success.
