To resolve team disagreements on data validity, 1- Establish clear data standards 2- Encourage open discussions 3- Implement a review process, 4- and assign a data lead for consistency 5- Keep refining these practices to ensure everyone stays aligned. #RunwayAdvices #RunwaySharesOpinion #Runwaypakistan

For a better data validity practice by our team, we can follow these. 1. First create a data validity plan (what to validate, with what, process to follow, inter linkage of questions, inter linkage of responses, data consistency etc.) 2. Train the team on these plan once, before starting the work. Most of their doubts can be clarified. 3. Perform random QC checks on the validations done by the others through experts in data validation 4. Any kind of data validation update should be communicated to all. 5. Monitor closely on the first 2 to 3 days time 6. Review finally on key pointers of data

Well, from my perspective defining the "ruler" before the data gathering is the key point. By ruler I mean the standards, standards have to be defined in as clear as possible. What do we.mean by data source? what,do we mean by respondents? What are we looking at? What aren't we looking at? For example, on a market study for a software we defined that on the desk research phase we WILL NOT talk to any potential or existing users, we need to see on this depth level the level of data available before conducting the interviews

To ensure everyone on our team aligns on data validity, start by establishing clear criteria and definitions for valid data. Organize a team meeting to discuss these standards, allowing each member to voice concerns and ask questions. Share examples of valid and invalid data, illustrating how it affects project outcomes. Implement a standardized checklist or framework for assessing data quality, and encourage regular check-ins to address any discrepancies. This approach not only promotes consistency but also builds trust within the team, ensuring unified support for the data’s reliability.