What is the real data analytics guru? Some say they can easily play with various analysis tools, some say they can find correlations in large amounts of data, some say they can identify data anomalies in a report at a glance, and some say they can write a classic data analysis report.

In fact, for a data god, these are necessary skills, to practice such 18 skills, the most important is to improve their data sensitivity.

The so-called data sensitivity is good insight into the relationship between data and business. An excellent data analyst can always quickly insight into the problems behind data and the guiding significance to business.

What is quick insight? For example, when entering a restaurant, ordinary people may only pay attention to “hot business” and “large customer flow”, but an excellent data analysis expert will often evaluate the data and the meaning behind the data, such as customer flow, customer unit price, table turnover rate, number of waiters, etc., so as to roughly evaluate the profitability of the restaurant.

If the restaurant also does take-out, the competitiveness of dishes can be quickly judged by giving him a data of repurchase rate, and the possible problems in store operation can be quickly judged by giving him a chart of the trend change of order quantity.

It takes a lot of experience and training to get to this level, so how do you deliberately train your data sensitivity as a data analyst? This article suggests 5 ways to help you.

Familiar with industry and business

In data analysis, insight into data must be combined with business, and the basis for improving data sensitivity is to have a deep understanding of business.

From the vertical perspective, it is necessary to be familiar with the historical data and development trend of its own business; from the horizontal perspective, it is necessary to memorize the average level of various indicators in the industry and important data of important competitors.

Note that the familiarity mentioned here is not only limited to statements, but also to go deeper into the frontline and communicate with business personnel to deepen the understanding of the meaning behind each data.

To what extent? For example, if you get a data of the company’s business, you need to quickly determine whether there is an anomaly and the level in the industry, and then measure and improve the input-output ratio of the data.

In addition to understanding the business, we should also accumulate more important data and rules in daily life, such as talent turnover rate, land rent price of each region, average salary of each industry, profit margin of each industry, key indicators and basic rules of each industry, which are valuable for us to analyze the business in a more comprehensive way.

Improve memory

Memorizing a wide variety of industry and business data is a must for those of us who work with data a lot, but everyone has good memories and bad ones. Here are a few tips to help you improve your memory.

1) Memorize by formula.

In the e-commerce industry, for example, memorizing a core formula [revenue = traffic * conversion rate * unit price * repeat purchase Rate] indirectly memorizes the four most important metrics. After memorizing the key metrics, remember the corresponding data.

For a normal index we just have to remember what’s before the decimal point, or we can even get rid of the oddment and remember integers that are close to it.

2) Read statements often.

It is suggested that when you go to work every morning, set aside ten minutes to review the important data and simply calculate the year-on-year changes. Special attention should be paid to anomalies.

Whether it is the business data of our own company or competitors, as well as the analysis report of the industry, we often see new, and every time we read it, we will have new thoughts.

3) A bad pen is better than a good memory.

It is recommended to list and sort out the core data that you pay attention to once a week. For the high-frequency and core data, you can copy it by hand to deepen your impression.

Improve mental arithmetic

Good data analysts, who don’t let calculations get in the way of quick insight, are usually good at mental arithmetic. I suggest you use less calculators in your daily life and improve your mental arithmetic ability.

Improve logical reasoning ability

Logical reasoning is simply infering the unknown from what is known. A good data analyst, even if entering an unfamiliar industry, can estimate business models and profit margins based on common sense.

In corporate business, data analysts can always draw convincing conclusions from the underlying logic, one step at a time, through the clues of data correlation.

Of course, strong logical reasoning skills also depend on years of deliberate practice. How to practice deliberately?

In our future derivation process, we should pay attention to two points: one is to start from the underlying logic, and the other is to constantly ask ourselves questions from various angles in the derivation until we come up with an unquestionable conclusion.

In addition, it is not good to do things behind closed doors. The best and stupidest method is to copy the data analysis report done by the big gods, imitate their analysis thinking and derivation process, and do it again yourself.

Choosing the right tool is critical

To improve data sensitivity, focus on looking beyond the data to see the essence. However, many traditional data analysis tools (such as Excel and SQL) make users face to face with dense data from the very beginning, which not only interrupts our thinking by boring data, affects our efficiency, but also is not conducive to the development of our analytical thinking.

By contrast, a professional data analysis, such as the one I’m using **FineBI (**www.finebi.com/?utm\_sourc…

FineBI can guide us step by step to identify key indicators.

With the help of FineBI, we can get insight into the key indicators in the data.

Data sensitivity comes from understanding the details of the business and the implications behind it, which is a long-distance exercise that can’t be learned overnight. Adopting these little habits in your life can help you become more data savvy and move from data analyst to business analyst.