Data people's thinking habits
Every data practitioner has a heart to become a technical expert.
Since graduating, we have labeled ourselves "programmers", "ACM", "BAT", "Coder"... these highly industry-specific vocabularies are very attractive to every young person Li, "becoming an admired big cow" is a latent dream in every young man's heart. After embarking on a job, these original dreams are still driving everyone's continuous learning and progress, even if they live in the cruel reality of "no", "don't understand" and "cannot" every day, they are still Make persistent efforts to complete the first stage of personal technical ability accumulation.
Formally because of the pain of technology accumulation, everyone's identity as a "technical person" has been deepened.
With this habit of thinking, in work, no matter what kind of demand scenario he encounters, he will always regard "Coding" as the core of his work. Coding specifications, program performance, implementation skills, and R&D efficiency are the "technical people". Should be concerned. Things that have nothing to do with coding have all become "dregs" that affect efficiency, and the cost of communication and research at work is inertially pushed to the product or project manager to bear. In fact, this kind of cognitive deviation will not be so obvious in the first few years of growth. With the improvement of the rank sequence, technical people gradually feel the requirements of the "objective environment" and their own " The requirements considered by "subjective consciousness" have caused mismatches and anxiety.
Only when this contradiction actually affects one's own interests will it be taken seriously.
Reasons for thinking habits
Being immersed in the past growth environment and ignoring the environmental changes brought about by the improvement of personal abilities is the direct cause of thinking habits.
For data students, whether it is data analysis, data warehouse, or data architecture, what environment do we face? Is it "write beautiful SQL code"? Or "build a set of automated algorithms"? Actually neither. Technology is only part of our daily work, and our work requirements should be to go deep into the business process, use data to describe the process and current situation of the business, and predict what may happen in the future, that is, "help users realize the digitalization of the business ". From the realization of this situation, technology is no longer just the only content we care about, but to position our own identity in a specific business direction, and use our technical capabilities to solve unknown business problems. It can be said that at this stage, "don't talk about development if you don't understand the business."
You will gradually realize: business knowledge, requirements analysis, domain modeling, project management, R&D efficiency, the proportion of these things in the work will become higher and higher, far exceeding the coding workload.
In their daily work, technical people usually have some specific biases, such as "products only need to be prototyped", "operations only need to maintain the community", and so on. This kind of consciousness is the same thinking habit as "R&D only needs to write good code". If writing code is a must and cannot be interrupted, then this way of thinking will eventually be restricted by the environment and affect Follow-up career development. Because promotion is not just based on your code level. The strength of a person is always just one person, and the strength of a team is truly strong. Don't believe that "1 skilled programmer is equal to 100 junior programmers". In fact, you are often not the skilled programmer, but the junior.
The thinking system that data people should have
Simply put, data people should have their own thinking system.
For example, the data warehouse belongs to the knowledge system A, the analysis algorithm belongs to the knowledge system B, and the business knowledge belongs to the knowledge system C. When we encounter any problems, we quickly scan the A/B/C in the brain to extract relevant knowledge. Come out, multiply by the new problem, it is the knowledge system D. Over time, our scope of cognition has become larger and larger, and our understanding of problems has become more and more "fast and profound."
Taking the example of helping the business side achieve digitalization, we need to relate the following aspects:
- What is the business concept?
- What is the purpose and value of business existence?
- What aspects of the business will be involved, such as what should be done in product/operation/risk control/item management?
- What is the life cycle of the business?
- What are the technical paths needed to realize the business? In this process, what responsibilities should be assumed for interaction/front-end/back-end/data/analysis/products?
- As the number one technology position, what content should be considered and what meetings to participate in to obtain the most complete information to ensure the smooth implementation of the business?
Therefore, the business is knowledge system A, the technology of the job is knowledge system B, the technology of the partner is knowledge system C, and the process of project establishment is knowledge system D, and the business meeting process is knowledge system E... Multidimensional cross product, you His knowledge and understanding surpassed everyone. Over time, your position in the team will gradually be established.
How to improve yourself
There is essentially no difference between the knowledge needed to "do business" and the knowledge needed to "do technology".
The knowledge system of modern people is based on previous experience summaries (knowledge in books, explanations from business parties, and results of industry conferences), combined with personal practical experience, so as to achieve knowledge stacking. Data people often discuss how to weigh the depth and breadth of the personal development route, such as proficient in the implementation process of the entire big data architecture or proficient in the technical principles of a Hadoop platform. In the same way, the same situation exists in "business studies". Students such as products/operations are naturally more profound in business than ours. If you want to quickly accumulate your own business knowledge, you should let go of your prejudices and talk to them more. , Think about the problems they face, master all links of a business, and participate in them. To be fully responsible in the subdivisions in which you are responsible can become the top technical position of a business.
In the first episode of the first season of House of Cards, Frank explained his job, which is to clear the blockage in the party. Take on some chores, do more communication, try to clear the blockage in the team, you will learn more.
