1. No Ideas: Data clutter, don't know where to start

Causes: Analysis of the business objectives are not clear, resulting in excess data collection; analysis methods and analysis of the scenarios don't know how to combine, resulting in no way to start.

Countermeasures: First, learn to understand the business context and the team's business objectives; familiarize yourself with the various analysis methods and application scenarios, which are described later.

2. No focus: the analysis of the logic is not rigorous, the raw copy of the hard to put together a mess of conjecture

Causes: did not take into account the overall fluctuations in the data caused by the possible reasons for the correlation of the indicators as a causative indicator, it became a? analyzing for the sake of analyzing?

Countermeasures: data analysis should form a closed loop, to determine the goal of analysis? Collect data? List possible causes (pyramid/formulaic thinking, described later)? Verify conjecture? Draw analytical conclusions? Follow-up to optimize countermeasures.

3. No planning: when analyzing, but found that the data is missing, the collection is difficult

Causes: the value of the product on the line of the benefit is not clear, did not plan in advance to observe the indicators and the development of the relevant data collection needs, the ingenious woman is difficult to cook a meal without rice ah!

Countermeasures: clear product success indicators, can be conceived in advance to analyze the idea, and then push back the details of the data needs required.

4. Unrecorded: data anomalies without knowing what was done

Causes: untimely synchronization of information within the team. It may be a surge in product data due to activity, or a product update that causes data to fall due to system failure.

Countermeasures: Establish a collaborative mechanism within the team to synchronize information to the ****Help platform in a timely manner. For example: X days before the operational activities on line, timely synchronization to the product-related activity planning, and make backup records and notify the relevant departments.

5. Unskilled: unfamiliar with analysis tools, time-consuming analysis

Causes: analysis tools such as excel, if not in the school has a special course, basically self-study or report the relevant courses, work is busy and did not find time to study alone is the root cause.

Response: It is recommended to list their weak links, targeted to find the relevant courses to learn, if it is a white, it is recommended to learn systematically, later will be involved.

On the 5 common problems and countermeasures in data analysis, Qingteng Xiaobian will share with you here. If you have a strong interest in big data engineering, I hope this article can help you. If you still want to know more about data analysts, big data engineers tips and materials, you can click on other articles on this site to learn.

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