This is a broad question, and the answer is certain: Yes. But a more detailed explanation is needed.
1. Will data make companies run better?
Since the object of data analysis is a specific business, let's start with the business. There must be such a job in this world, even without the introduction of numbers and calculations, it can still run well to a certain extent. Then, students who don't like to process data can find a position in this area. But you can think about it one more step. For this kind of work, if numbers and calculations are introduced, is it possible to perform better? Put this question aside for now and respond later.
2. Various industries are undergoing digital transformation
We must face up to the current background of the times. All walks of life are undergoing digital transformation, and this trend will continue forever without being blocked by the current epidemic. This shows that the business content of many industries will be transformed into the form of data, and it also means that data is the basic unit of business content; and the operation of the business is in the form of data flow. In other words, what people usually think of as work or work is essentially transformed into data processing. It is not difficult to find that the best methodology and tools for processing data are data science content such as so-called data analysis and data mining, and its main foundation is mathematics, statistics and computer science.
3. Data analysis has quietly become an element of business
It can be seen that data analysis has been internalized as a part of business practice. At the same time, it will also plan and guide business operations at a higher level, especially in the construction and operation of IT technology-based products to assume a necessary and sufficient role. In fact, the continuous development of any work inevitably requires information exchange and experience exchange, and such a process can be improved in efficiency and optimized through digital transformation. Therefore, once data science methods are introduced for industries that do not require the participation of numbers and calculations in the traditional sense, they will inevitably move towards a better development direction.
4. Data science has become an inevitable driving force for business
At this time, it needs to be acknowledged: the role of data science methodology with data analysis as an important content in business-driven is inevitable. However, some people may ask: Why can't I feel the role of data analysis? There are many reasons and the space is limited. Let me mention three important points first.
4.1 Business development is inseparable from data analysis
First, data analysis itself is a kind of thought or way of thinking, with great potential characteristics, and it is not as easy to see and touch as hardware. Example: In all kinds of Internet products, the functions that users can easily feel, and the various states of related programs that are not easy to feel, such as where it is easy to attract users, how to regulate and transmit the data volume of each port, etc. In fact, they are all set under the guidance of standard data analysis ideas and on the basis of investigation and analysis practice, and often require complex mathematical models as assistance.
It's just that operations like this, internalized in the process and hidden behind the results, seem too low-key and difficult to be noticed, so they are indispensable. Once this link is missing in a certain situation, the business failure is only a matter of time. In recent years, there are many examples in this regard. For example, a media company or an e-commerce company has not paid much attention to the content of evidence analysis for a long time, and did not accumulate and analyze historical data, resulting in a lack of reliable data for forecasting prospects, and ultimately the entire business line paralysis.
4.2 The status of data analysts is constantly improving
Second, data analysis is essentially an applied mathematical method. The relevant practitioners must at least have a solid foundation in mathematics, statistics, and computers, as well as a full understanding of business content, before they can use this tool for dynamic data analysis. Of course, it is not emphasized that there must be a relevant professional background, but it must be emphasized that strict relevant professional training is indispensable. This means that it is very difficult to become a qualified data analyst or data scientist after reading a few books on data analysis and even data science, and then full of passion and fighting spirit.
In fact, the industry of data analysts has been mixed, and the data analysts I have worked with and met, I have to say that many of them have insufficient professional quality or business understanding deviations, which ultimately leads to no real data analysis. The role it deserves, and it makes people question or even underestimate the role of data analysis itself. And many data masters like this have already occupied important positions in the company, and the impact can be imagined. It is not difficult to see such a phenomenon. The data analyst group, similar to product managers, does not account for many heads and qualified personnel, so secondary training and evolution are still very much needed.
4.3 Data analysis cannot be marginalized by perception
Third, the leadership of the relevant company has incorrect views on how to use the data. This is mainly manifested in the excessive use of performance figures as a means of saving face or seeking other benefits, and data modification or even falsification, and no longer attaching importance to the continuous optimization function of data analysis for business. The consequence of this is that the energy that should have been used to process real data is used to create inappropriate data, and in serious cases, they fabricate stories and deceive each other. Obviously, the role originally used for truth-seeking data analysis to drive the business has been replaced by fraud, and its importance has been marginalized.
There are many other reasons. In summary, there are three points:
- Fail to recognize the substantial importance of data
- Lack of qualified data analysis capabilities
- Doesn't have the quality of using data properly
Correspondingly, if data analysis is to be in a guiding position in driving the business and can play a decisive role, the following conditions need to be met:
- Give the job to a competent person,
- Let the incompetent people continue to participate in the training until they are qualified.
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