The most common data gathering challenges are timeliness (data arrives too late to be useful), completeness (missing records, partial submissions, incomplete fields), and accuracy (incorrect values, wrong time window, calculation errors, or faulty source data). Option A captures this classic trio. "Integrity" and
"consistency" are important concepts but are often encompassed within accuracy/completeness when practical issues arise. "Data visualization" is not a data gathering challenge; it belongs to reporting and communication after data is collected. Addressing these challenges requires activation discipline: clear definitions, documented sources, assigned data custodians, standardized templates or automated extracts, validation checks, and an escalation process for late or missing data. Another frequent root cause is unclear ownership- multiple teams assume someone else provides the number-so RACI and a collection calendar help. KPI reliability depends on trust; if leaders don't believe the numbers, the dashboard becomes ignored. High- quality data gathering is therefore foundational to performance management, not an administrative afterthought.