As soon as someone questions a statistic, the logarithm starts to crash everyone. Especially during the business analysis meeting Whatsapp Database and monthly meeting, the data team faced repeated and frequent torture, and different demanders were waiting for answers and explanations from different angles. Leader: I don't Whatsapp Database understand, why no one can give correct data? Business: The results given by technology are inaccurate... The data quality is poor, and it cannot be used. Data team: I didn't record the data, I didn't design the business database,
I didn't define the indicator logic, and I didn't know the impact of operational logic and policy adjustments on the data. The data is processed logically like this, not what you want, what should I explain? Very innocent, Whatsapp Database very speechless, still have to work! Model design, data governance, system and process combing, organizational rationality, data awareness and data team status, Whatsapp Database and business battles are not considered. In front of the work that must be faced at the moment, I just want to talk about "how should logarithms be correct?" I hope that through this article,
I can chat with friends in the data-related industry about "the handling of normalized data doubts, and the output of the data team's views in different scenarios". Second, logarithm, logarithm, what is correct 1. Whatsapp Database Scenarios for data comparison The new and old indicators are compared, and the new indicators are applied when they are online and replaced; New indicators are launched to confirm the accuracy of the data; If it does not meet expectations or the data fluctuates Whatsapp Database greatly, perform data inspection; The data on the application side is inconsistent, and the dimension summary is inconsistent, and an explanation is given; Data A and related data B do not match, cross-validated.