What is data operation and what does data operation do?

What is Data Operations? We can understand it from two perspectives: broad and chivalrous

Narrow: refers to the job position of "data operations". It is related to content operation and product operation.

Activity operations, like user operations, belong to a branch of operations and are engaged in data collection.

Cleanup, analysis, strategy and other work to support the development of the entire operation system in the direction of refinement;

Broadly defined: Data is a way to reflect the true state of products and users, and use data to guide operational decisions and drive business growth. with a data analyst.

Data operations are more focused on supporting front-line business decisions.

2. What is the main work of data operation?

1. What does data operation do: data planning.

Data planning is the foundation of the entire data operation system, and its purpose is to figure out what is wanted. Only by figuring out what your purpose is and what kind of data you need can the subsequent data collection and data analysis be more targeted.

There are two important concepts in data planning: metrics and dimensions.

1) What is an indicator?

Metrics are used to measure specific operational effectiveness, such as UV, DAU, sales amount, and conversion rate.

Wait a minute. The selection of indicators is based on specific business needs, and the events are summarized from the requirements, and the indicators are corresponded to from the events.

2) What is a dimension?

Dimensions are attributes that are used to break down metrics, such as ads**, browser type, visited region, and so on. Broadly speaking, the dimensions can be divided into four aspects: population attributes, device attributes, traffic attributes, and behavior attributes

Demographic attributes: including demographic data such as gender, age, and educational background;

Device attributes: including device type, model, etc.;

Behavioral attributes: activity, new and existing users, etc.

2. What does data operation do: data collection.

Data collection is the foundation of data analysis, and traditional data collection requires labor and time costs. At present, there are three common data acquisition schemes for data collection, which are burying.

Visualization of buried and non-buried points.

Visual burying: Visual burying is an extension of burying, which replaces manual burying through visual interaction. This method lowers the threshold for users to use and improves efficiency.

No buried points: No buried points subverts the traditional process of "define first and then collect", only one SDK needs to be loaded to collect all user behavior data, and then all behavior data can be flexibly customized and analyzed. Compared with the burying scheme, the cost of no burying is low, the speed is fast, and there will be no wrong burial and missing burial.

Data Operations. Data operation refers to the analysis and mining of data by the owner of the data, and the information hidden in the massive data is released as a commodity in a compliant form for the use of data consumers.

Data is everywhere in operations, so successful operations must be data-driven. Every aspect of your operations needs to be data-based. When we develop the habit of being data-oriented, we will have a basis for doing operations, and we will no longer operate blindly based on experience, but with a purpose.

When we have enough data, we can stop relying on subjective judgment and let the data become the referee in the company. Ideally, if we could track everything down, then all of our decisions could be based on data as a matter of course.

In the enterprise, we go from the overall strategy to goal setting, to the methods that drive business operations, and finally to the adoption of certain metrics to measure the effectiveness of data operations.

The role of data in a business is huge. People at different levels need to do different things with the data.

Data operations: The owner of the data through the analysis of the data.

Data operations: The owner of the data through the analysis of the data.

1. Analyze the current situationAnalyzing the current situation is the basic purpose of our data analysis, we need to clarify the current market environment, what is the market share of our products, what are the best registered users, what is the registration conversion rate, what is the purchase conversion rate, what are the competing products, what is the development status of competing products, what are the advantages and shortcomings of our competitors, etc., are all part of the analysis of the current situation. There are two aspects here, analyzing your own current situation and analyzing the current situation of your competitors.

2. Analyze the causesThe reason for the analysis is that data operators use it more, and those who do operations not only need to know what's going on, but also why it's so in a specific business. In business, we often encounter a certain day when users are suddenly very active, sometimes a large number of users suddenly churn, etc., every change has a reason, what we have to do is to find out the reason, and give a solution, these are the analysis reasons.

3. The futureThe third purpose of data analysis is to take precautions and use data analysis methods to change the trend of future products, which is very important for product operators. As an operator, you can determine the future trend according to the recent product data changes, according to the trend line and the strength of the operation strategy, and use the next period of time to verify whether this trend is feasible and achieve data-driven business growth.

Broadly speaking, data is the most authentic way to reflect the state of products and users, guiding operational decisions and driving business growth. Different from the position of data analyst, data operations are more sideways and slick to support the front-line business to make bad decisions.

An excellent operator should be familiar with the traffic profile of his own product, by looking at the traffic situation every day, the operator can clearly grasp the traffic indicators and their changing trends, and facilitate the evaluation of the past and future trends.

Data operations considerations.

Data operations need to understand what data is needed in the process of product operation. For example, e-commerce, first of all, we must look at the order volume, customer unit price, conversion rate, but also look at the process data of the user's noisy flow in different pages, stay in **, where to pull down, and so on.

Summary. Hello, for your question [what does data operation do] The answer to this question is as follows: Hello, the content of data operation is content operation, product operation and user operation.

The content of data operation is as follows: 1. Look at the data, and do the operation must understand the data and grasp the data. 2. Analyze the data, understand the changes in the data and the reasons for the changes in the data, and grasp the changes in them.

3. Operation plan, always sort out the plan, always pay attention to the effect, and always think about the summary. 4. Control the status quo, follow up activities, check publicity positions, pay attention to user events, etc.

What data operations do.

Hello, for your question [what does data operation do] The answer to this question is as follows: Hello, the content of data operation is content operation, product operation and user operation. The data operation work is as follows:

1. Look at the data, and you must understand the data and grasp the data when doing operations. 2. Analyze the data, understand the changes in the data and the reasons for the changes in the data, and grasp the changes in them. 3. Operation plan, always sort out the plan, always pay attention to the effect, and always think about the summary.

4. Control the status quo, follow up activities, check publicity positions, pay attention to user events, etc.

The following is a related extension, I hope it will be helpful to you: Data operation refers to the analysis and mining of data, the owner of the data, the information hidden in the massive data as a commodity, in a compliant form, for the use of data consumers.