Rule Make data quality audits and correction plans a routine task. One of the biggest mistakes businesses make is treating data quality issues as a one-time problem rather than an ongoing process. too late in the process, it can lead to significant setbacks for all stakeholders. Data teams often bear the brunt of the blame, as conflicts arise hong kong rcs data between them and data consumers when pipelines fail to provide accurate, timely information.
Establishing a regular audit routine and subsequent correction plans prevents the onset of sudden or unexpected challenges. Your team should not panic when a report is due, nor should they wait for other department members (like IT) to get accurate data.
Rule Prioritize data quality as a key talking point in meetings. While it’s easy to focus on impressive charts and positive trends in team meetings, how often do we discuss the accuracy of the data behind those visuals? How many business users know concepts like data deduplication or record linkage? Sure, these are IT concepts, but then again, business users are the true custodians of customer data. They need to understand the issues affecting data accuracy and reliability – primarily since they rely on this data to drive campaigns, reports, and business-critical decisions.