As a veteran surfer, having sex in various neighborhoods is one of my few habits. Today, during the routine fishing time before work, I was swiping the screen of my mobile phone with passion when a movement stopped me.

“Data analysis is just a SQL boy.” Counting robots? Good guy, which come of face say oneself is an insider, we count person but have lofty technical pursuit and occupation ideal drop, oneself dish say oneself dish, don’t say kind of job low good. I really want to pick up the keyboard, a wave of anger.

But when I checked the comments section, there were so many people who felt the same way. Angina pectoris.

An inexplicable sense of justice rises, I ignited a sense of duty to defend the dignity of the profession, simply today I will write an article, for our number of people to justify, but also to perhaps have the same confused you solve problems.

What is the value of data analysis?

Data analysis, the analysis of the two words, data analysis to assist business decisions, accurate business decisions need to rely on powerful data analysis. For example, the launching strategy of shared bikes and the “Thousand people thousand faces” of Taobao’s homepage are supported by powerful data analysis capabilities and machine learning algorithms. There is no doubt that data analysis is extremely valuable in improving the business level of enterprises and improving social efficiency.

** There is plenty of room for a good data analyst to grow. ** Some of the data analysts I know, for example, have started to lead teams towards the CIO direction; Some become data consultants, using their experience and capabilities to provide services to other organizations. Some entered business departments and became business specialists/general managers.

When data analytics is done well, there is no ceiling to career development.

Many data analysts also understand the value of data analysis, but suffer from various problems in practical application, such as:

1, as a data analyst, but every day busy with the number of tables, empty a skill in the company to make no effort;

2. Due to the lack of understanding of the business, I was engaged in numerical analysis, lacking unique insights and ideas on the business, and did not make truly valuable analysis on the business;

3. The results of data analysis are difficult to be implemented and cannot really assist business decision-making.

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These problems happen so often that they may even make you wonder if your career is worthwhile, but don’t be afraid to find solutions.

In my opinion, these problems can be boiled down to one problem: ** lacks professional and efficient data analysis tools. To put it simply, it’s not that people fail, but that they don’t find the right tools.

Data analysts are busy taking numbers to make tables every day, because the data processing efficiency is not high. Take fetch, for example, and if we do it in SQL, we have to write code, and if we have to call multiple sheets, it’s even more complicated. If the new generation of self-service BI tools (this paper takes FineBI as an example) are used, the IT department can prepare the basic data, and data analysts can easily complete the number taking only by checking, and multiple tables can be easily held without code. Efficiency MAX!

When we get dense data, we often don’t know where to start. Traditional data processing tools just show you boring data, but good tools can get you started. For example, FineBI will ask you to think about what you want before you start analyzing, and then select the corresponding operation after you define the goal, and then select the relevant indicator data, so that you can get closer to your goal step by step.

Good tools, from the very first step, will keep you focused. With FineBI, you don’t have to spend a lot of time on counting and data processing, because that’s not the real value of data analysis. You can focus on business analysis, which is the real value.

With efficiency out of the way, the question arises: How do you make analysis that is truly valuable to the business?

In addition to understanding the business in depth, you need to build a rich library of data analysis models. Since the birth of data analysis, countless classic data analysis models have been deposited, which have been widely used in various fields and brought substantial business value.

Pyramid model, KANO analysis model, RFM model, shopping basket analysis model, four quadrant model…… They are often used and new classic models, but if they are built with traditional tools, they usually take a lot of time and are not flexible enough. A small adjustment will bring a lot of work, which is very unfriendly.

With FineBI, a variety of models can be quickly built by mouse operation (as shown below), and can be adjusted at any time as needed for exploratory analysis. Data analysis to do so, who will say that there is no practical value to the business!

But it’s not enough to make an analysis that is truly valuable to the business. It’s more important to make the analysis tangible and help the business make decisions. To help with business decisions, you need to have an analysis that is convincing from the executive level to the executive level so that it can move forward.

How do you make the analysis more convincing?

Visual! Accurate, intuitive and impact visual cockpit!

Traditional tools such as Excel, with few chart styles, few degrees of freedom and no linkage, have long been unable to adapt to the segmentation and complex presentation requirements of data analysts. Aforementioned FineBI will provide a powerful visual effect, built in more than fine chart style (pictured above), not only supports the commonly used bar charts, line charts and pie charts, entirely, bubble chart, heat maps, GIS map, also support the third-party chart plug-in, still can undertake linkage between charts, drill, in favor of the effect is amazing.

With such a blessing, are you worried that your analysis will not be convincing?

By putting the diagrams together, you can create an intuitive data analysis cockpit, and the process is very simple and efficient, basically just a simple click, drag and drop to complete!

Find no, the traditional tool just provides the data processing function, the operation is troublesome to say, in the face of complex data head are hemp, analysis is nowhere to talk about, such work of course is worthless! The new generation of self-service analysis tools like FineBI protect users from being burdened by dense data, and focus more on exploring the correlation and trend between businesses for business analysis, which can be said to really help users find the value of data analysis.

The difference between them is no less than that between calculators and computers.

The ancients knew that a good horse needs a saddle. A good data analyst with outdated tools can only add up, while a good data analyst with BI tools can promote each other and help you break the ceiling of your career development.

It’s time for a change, data analyst!