How can you address conflicting perspectives on data quality within your cross-functional team?

Defining clear goals for addressing conflicting perspectives on data quality within a cross-functional team is crucial. Start by outlining the overarching objective: to establish a unified understanding and agreement on data quality standards and practices across departments. Break down this objective into specific, measurable goals, such as defining key metrics for data accuracy, completeness, and relevance to business needs. Engage stakeholders from all relevant functions to gather input and ensure diverse perspectives are considered. Align these goals with organizational priorities, emphasizing the importance of reliable data for informed decision-making and operational efficiency. Establish timelines and milestones

I do agree that while individual goals may differ the overarching goals should enhance data driven decision making. Standard Operating Procedure must be developed for data quality checking and all team members must use this SOP to ensure consistency.

I will try to get the team to be on the same page as to what a quality entails. Subsequently, we would look at the indicator definition of the data elements being collected, especially those of interest causing conflict. If everyone understands these two dynamics, then the conflict would have been resolved significantly, because it will be a matter of reviewing the data, triangulation or better still collecting another data based on the new understanding and perspective.

Within a cross-functional team involves establishing clear criteria and standards for data quality upfront. It's crucial to encourage open dialogue where team members can discuss their perspectives and concerns about data accuracy, completeness, and reliability. Implementing regular data validation and verification processes helps identify discrepancies and resolve them collaboratively. Additionally, fostering a culture of transparency and mutual respect ensures that all team members understand the importance of high-quality data and work together towards achieving consensus on data quality standards.

Data quality disagreements in your cross-functional team? Here's a fix: Align on "Good Data": Discuss and define data quality (accuracy, completeness, etc.) for your project's needs. Walk a Mile in Each Other's Shoes: Let each department explain how data quality affects their work (e.g., marketing might need accuracy, sales might need timeliness). Standardize the Process: Develop shared data standards for formats, cleaning, and documentation. Measure Your Progress: Track key data quality metrics to see improvement and identify areas for ongoing work. Keep Everyone Informed: Encourage open communication about data issues to find solutions together.

Set Up Standards Once a series of common goals has been established and departmental needs have been assessed, the next step in creating unified standards is to set down these unified data quality standards. They should be achievable and measurable, but above all, agreed to by each member of the team. Well-defined criteria for ensuring data accuracy, completeness, consistency, and timeliness can help prevent conflicts by providing a point of comparison regarding data. The standards should allow flexibility to accommodate the idiosyncratic needs of an individual department but at the same time ensure that the overall integrity and usability of the data are not compromised.

On this aspect, it is imperative that the entry authority of certain types of data be established from point zero. This makes for easy ratification. Besides, the retention period for physical data should be extended beyond the approval period allotted to captured data.

From my experience, establishing data standards is the best approach, it ensures that everyone is on the same page. During data validation, the first step is to have a clear definition of what the standards are, that way data quality issues can be easily identified and the team can agree that indeed there is a data quality issue.

Using Tools The right tools can be very effective in managing data quality for your team. Tools for automatic monitoring and maintenance usually send real-time feedback on the occurrence of problems. They also have opportunities for performing data cleaning, validation, and enrichment to make sure that the best data is used by all members of the team. You make sure that everybody can produce and benefit from high-quality data by choosing tools that are friendly and accessible to everybody in the departments.

Having a "devil's advocate" role on cross-functional teams is helpful. No meeting should end on agreement regarding data quality, otherwise there is a key perspective missing. Most organizations will typically have stronger personalities lead meetings, and it can be hard for other participants to feel impelled to contribute. Forcing someone to take on a devil's advocate role, and having it rotate, will help build engagement from participants. The other key role is to provide critical feedback. Another key feature is to look at data from the lowest level possible - if data is high level or driven purely by analysts without input from front-line staff or supervisors, it is likely the data is missing a key perspective and risks quality.