Data planning refers to collecting and sorting out the data requirements of business departments and constructing a complete data index system.
There are two important concepts here: indicators and dimensions! Index, also known as measurement. Indicators are used to measure specific operational effects, such as UV, DAU, sales amount, conversion rate and so on. The selection of indicators comes from specific business requirements, events are summarized from requirements, and indicators correspond to events. Dimensions are attributes used to subdivide indicators, such as advertising sources, browser types, access areas, and so on. The principle of selecting dimensions is to record those dimensions that may have an impact on indicators.
2. Data collection
Data collection refers to collecting business data and providing data reports or data kanban to business departments.
A clever woman can't cook without rice, and the importance of data collection is self-evident. At present, there are three common data acquisition schemes, namely buried point, visible buried point and no buried point. Compared with the buried point scheme, the non-buried point scheme has low cost and high speed, and will not be buried by mistake or leakage. No buried point is becoming the new favorite of the market, and more and more enterprises have adopted GrowingIO's no buried point scheme. In the scene without burial point, data operation can get rid of the bondage of burial point demand and spend more time on business analysis.
3. Data analysis
Data analysis refers to in-depth analysis of business data through data mining and data model. Provide data analysis report, locate problems and propose solutions.
Data analysis is the key work of data operation, and both data planning and data acquisition serve for data analysis. Our ultimate goal is to locate problems through data analysis, propose solutions and promote business growth.
About what data operation does, Ivy Bian Xiao is here to share with you. If you are interested in big data engineering, I hope this article can help you. If you want to know more about the skills and information of data analysts and big data engineers, you can click on other articles on this site to learn.