How to quickly improve data analysis capabilities

Data analysis is an indispensable part of modern enterprise operations, but how to quickly improve data analysis capabilities, especially in combination with company business to achieve rapid results, is a challenge faced by many data teams. This article will systematically introduce the three levels of data analysis capabilities and provide practical methods and tools for your reference.

A classmate asked: "Currently the data team's analytical ability is relatively weak. We want to improve our analytical ability so that we can combine it with the company's business and see quick results." ... Today I will give a systematic introduction. Data analysis capabilities are divided into three levels, and they must be improved layer by layer.

1. Three levels of capability requirements

• Primary ability: Use SQL, Python and other tools to extract data as required to meet data viewing needs
• Intermediate ability: Based on business processes, proactively sort out indicator systems, develop data dashboards, and replace sporadic and temporary data collection needs with fixed data monitoring
• Advanced capabilities: Based on data monitoring, proactively discover business problems, provide feasible suggestions, and design experiments to test business solutions. Only by doing this can data truly be effective.

There are many introductions to primary skills on the Internet, including:

• Do a lot of exercises to improve your proficiency in number extraction
• When recruiting, use complex queries in the work as test questions to select those who can answer them correctly.
• Provide sample tables + query logic, use AI tools to assist SQL writing
• Create a "data collection error book" and practice repeatedly on the indicators where errors were made

In short, practice makes perfect, and you can create a qualified data acquisition tool by practicing more.

However, starting from the intermediate level, you need to combine data with business and have your own insights. There is little sharing on this topic online, most of which introduce popular practices such as AARRR. Today, I will introduce it to you in depth.

2. Key Point 1: Use standard templates to replace scattered data

There are four common data analysis requirements:

1. Monitor business conditions

2. Analyze the cause of the problem

3. Predict business trends

4. Test business ideas

Among them, monitoring is the most demanding and time-consuming. If you want to improve your analytical ability, you should sort out the indicator system by business line (such as sales, operations, products, marketing, logistics, etc.) and generate a fixed monitoring template (as shown in the figure below). This will deepen the understanding of the business for novices and greatly reduce the workload of temporary data acquisition, freeing up manpower to delve into more advanced things.

3. Point 2: Summarize the general business trends

"Those who work with data can't understand data" is a pain point for many basic students. After establishing monitoring indicators, you should first understand the regular trends of key indicators for business assessment (such as sales revenue, profit, number of new users, number of active users, etc.)

There are three types of rules that need special attention (as shown below):

1. Natural cycle: whether the indicator is related to seasonal changes and holidays
2. Lifecycle: Trends in key business indicators from launch to de-launch
3. Cohort changes: Trends over N time periods after user registration

Only after understanding the normal trend can newcomers understand what "normal trend" means, and then find the real indicator abnormality (rather than randomly analyzing a 1% fluctuation for a long time). This is the starting point for in-depth analysis.

IV. Point 3: In-depth business process

To gain insights based on data, we must first go deep into the business

For example, in sales, you can understand:

• How many steps are there in the sales process and what data are recorded?
• How many types of sales channels are there and how does each perform?
• How many products are sold and what is the proportion of each product?

For example, supply, you can understand

• There are several steps from raw materials to finished products
• What resources are consumed at each step
• What results does each step produce?

In the process of understanding the business, pay attention to:

• How much data is collected in each link
• Which aspects does the business pay most attention to?
• What is common business practice?

After understanding, you can build an indicator system according to business processes and common practices (as shown below). This will enable data monitoring without blind spots and think about problems from a business perspective (rather than "what indicators are there in the database, I'll just dump them all out")

5. Point 4: Quantify the effects of business actions

For key business actions that are often performed, it is necessary to establish a quantitative monitoring mechanism.

For example, if the business wants to improve sales performance

1. If the marketing department is doing a promotion, they can use data to record which orders are promotional orders, observe the growth of promotional orders, and calculate the revenue of the activity.

2. If the sales department holds a sales training session, there may not be data to record how much each person has improved. At this time, the only option is to record which people/companies participated in the training and then see if the indicators have changed.

Doing so can, on the one hand, deepen the understanding of the business for new employees, and on the other hand, help them understand the effects of various business actions from the results. Only then can we have materials for in-depth analysis. Some companies don’t even know the data of what the business has done, and they have to ask about it afterwards… This is not considered “in-depth analysis”.

6. Key Point 5: Split business problems and form analytical hypotheses

Learning to make assumptions first and then find evidence is a key step from intermediate to advanced (as shown below)

The analytical assumptions come from three sources:

1. Make assumptions based on past rules, experience, and trends
2. Make assumptions based on business concerns
3. Based on the major problems found in the structural/hierarchical analysis, hypotheses are proposed

All three methods require the accumulation of the previous steps. When there are many possible assumptions, such as a simple "the impact of rain on performance", senior analysts can break down several situations (as shown below). This kind of thinking from coarse to fine requires long-term training. Only by breaking through this step can you advance to the ranks of masters.

In summary, the improvement of analytical ability requires

1) In-depth understanding of business processes and business details

2) Understand business rules and measure business practices

3) Make reasonable assumptions and find key elements

Moreover, a lot of training is essential.