Whenever reporting at the end of the year, the salespeople are all upright: "Boss, I earned 100 million for the company this year!"; the marketing planners are all high-minded: "Boss, behind that 100 million, there is 50 million that I invested. "Advertisement"; the products are

Text: Teacher Chen

Source: Grounded School

It’s the end of the year, and it’s time to think about performance appraisals again! A bloody reality is: How is the performance of data analysts reflected?

Whenever reporting at the end of the year, the salespeople are all upright: "Boss, I earned 100 million for the company this year!"; the marketing planners are all high-minded: "Boss, behind that 100 million, there are 50 million thanks to me. "Advertise"; the products are calm: "This year's 100 million, 80 million is driven by new products, that's what we do"; the operators are not calm: "Boss, don't listen to their nonsense, once the product is online There are a lot of bugs. If we don’t rely on our activities to attract new users, how can we have users?" These guys who may not look at data at ordinary times are very good at data thinking when it comes to showing off their merits, and everyone uses data to claim credit!

As for those who really do data, what should data analysts say?

boss! I wrote 100,000 lines of SQL!

This is a common reporting method. Report to the boss, I made 500 requests a year, wrote 200,000 lines of SQL, crawled 50 million pieces of data, wrote 200 excel daily reports, and 100 ppt reports... The subtext is: Boss, look at it, look at it , I worked, I worked so hard.

However, this return effect is approximately equal to 0. Why? If you put yourself in someone else's shoes, you will understand immediately. Suppose a salesperson reports performance to his boss like this: Boss, I made 800 bus trips, 1,000 subway trips, knocked on customer doors 5,000 times, and made 10,000 customer calls. How did the boss feel after hearing this? The boss will only ask one question: "So, what is your performance?"

Yes, so what is your performance? If we don't talk about performance, what's the point of doing the intermediate process? The core pain point of data analysis is here: data analysis cannot directly drive performance! Running numbers, organizing data, writing SQL, and beautifying ppt are all intermediate processes. To put it to the extreme, wouldn't a company be able to operate without the "Operation Data Daily"? Of course not. Many departments can do their own work as long as they see the numbers. What is the point of analysis? The capabilities of many grassroots data “analysts” are not as good as experienced operations and products. What's the use of coming?

What's worse is that these intermediate processes belong only to you, and are a work that has failed but failed. Even if you write 2 million rows of SQL, that is your own data analysis business. The company spends the money to let you do this. If you don't do it, who will? Just like sales never complain about traveling outside every day, the market never complains about more than 80 revisions of the plan. But if you don't do it well, you have to shoulder the blame. Doing too much and making many mistakes is the most common outcome. Unfortunately, many students happily enter the work of data analysis, but find it difficult to advance to the next level in 2 or 3 years, and they fall into this pit.

Yonghui Supermarket Data Center APP

supports business! New data product launched

This is also a common reporting method. The word "new" is the key. Because simply optimizing existing reports will fall into the trap of thankless efforts. But "new" reports or tools are different. New tools are part of new products and new processes, and everyone can work together to take credit. Do you still remember how the operation took credit at the beginning? "Without us, new products will not be popular!" Without data tools, new products will not be launched at all. This reason is equally useful. Therefore, actively coordinating with other departments, participating in major projects, and providing product/tool ​​(rather than human-powered reports) support can show results.

here particularly emphasizes the difference between tools and human flesh reports. Indicators such as when the tool will be launched, usage rate, and usage satisfaction can be quantified. For example, the access rate of a certain report made by the new tool is 80% in one day. The boss can see it at a glance. At the end of the year, there will naturally be data to claim credit. This is our achievement. ! For a new project, human flesh wrote 20,000 lines of SQL, and this matter became our regular work. If you do it, you should do it. If you do it well, no one will remember it. If you do it badly, you will take the blame. Therefore, an important ability for the leader of the data analysis department is the ability to create products from routine work. If you don't turn your work into a product, you will always be a handyman behind others.

solves the problem! Analysis suggestions were adopted

Some students may be surprised: "Don't they say that data analysts are the boss's strategists? Do you think strategists are very respected?" Yes, strategists are very respected, but the role of strategist is We need to fight for it ourselves. Often the advisor the big boss trusts is not a data analyst, but a manager in the marketing department. The reason is that the problem still lies in "Are we thinking about data or business issues?" Marketing managers think more about specific business issues, so they are more likely to be trusted by their bosses. Quite a few data analysts hover between keyboards and screens every day, and even their bosses cannot see them. The business department does not have a chair for its own meetings, which is far away from specific business issues. In the end, it was reduced to a human-powered counting machine, and naturally the salary increase became further and further away.

The key here is to solve the problem independently. Many times we write regular analysis reports, and we also write analysis suggestions for daily and weekly reports. Yet these recommendations are often thrown into the trash by business units. Because others have not explicitly entrusted us to do this. Maybe what people need is data, not analysis and conclusions. Therefore, how to combine specific needs into a project is very important. With an independent project,

can assess the effectiveness of data analysis. Use the simplest method: what is the benefit before data analysis and optimization, and what is the benefit after data analysis and optimization. The business department had problems, we solved them, tracked the results, and the results were made known to the world. Finally, we celebrated with the business department and had a happy ending. Ability to incubate projects from routine work. It is another important ability for data analysis to continue to improve.

The concept of data analysis has become popular in the past two years. A large number of students from various industries, backgrounds and with various purposes poured in. Many people hope for a better future than before. However, as a 9-year veteran, Teacher Chen has seen too many tragic examples of people who have worked for 1 or 2 years without promotion, fell into confusion, changed jobs in a hurry, and hit the career ceiling while jumping around. Therefore, I have summarized the truly effective performance points and share them with everyone.

Regarding the work efficiency and personal growth of data people, here is also a Zhi·Hu·Live - "How to become the data analysis talent that enterprises need?" to teach everyone to assess the situation and take advantage of the opportunities of enterprise data reform to find their own Location.

Postscript

Finally, I will not admit that the following dark truth is mine: As a leader of the data department, I deeply understand that the only KPI of data analysis work is the trust of the big boss, and the big boss does not trust us. No shit at all. The performance of data analysts and analysis specialists is essentially whether department leaders think they are performing well or not. For data analysts, it is most reliable if they can help leaders fight projects independently. For data analysis specialists, they can proactively think about how to obtain data and reduce requests for instructions. The numbers on the assessment form are all written out for people to see, and the psychological scale is the real thing.

Yes, please like it as much as you like, and it will be burned after reading.