How do experts do data analysis

Data, which now pervades every sector and corporate activity, has risen to prominence as a key production component. Data analysis has permeated many aspects of life as a result of today's fast-paced commercial environment. Many data professionals, on the other hand, jokingly refer to themselves as SQL Boy/Girl, cousin/cousin, counting machine, report creator, brainless tuner, and so on. They lack direction and expectations due to their lack of sense of worth and perplexity.

Don't fall into the trap of being tech-first. Do you have a firm grasp on it?

Don't fall into the trap of being tech-first. While there is nothing wrong with pursuing technological advancement and development, we cannot rely solely on modern means. An excessive fascination with so-called cutting-edge, sophisticated, and cutting-edge analytical procedures or instruments is unworthy. Furthermore, how to construct an enterprise's data warehouse is a broad concern, and there are numerous historical reasons for this. It's an excellent approach as long as it solves the problem; don't put too much emphasis on the sophisticated way.

A broad commercial perspective is required.

Everyone understands that data analysis requires knowledge of the business, but because they just know the name and not the essence, many people get lost at first. Despite the fact that the business is quite complex, there are still traces to follow in terms of data analysis. The first is the business model, or what kind of goods or services are provided to whom; the second is how the transaction forms a closed loop in various dimensions of the people-goods-field; and the third is the responsibilities of each enterprise department in the closed transaction loop, as well as the cycle. What is the primary goal of sex work? Only in this manner can the data analysis performed under the reserve be relevant to the business.

The data is not moved, but the concept is.

More than half of the data analysis is accomplished once the analysis ideas are worked out, and the full analysis logic will be more clearer and smoother. Analytical thoughts are divided into two categories. To begin, fully discuss the requirements with the business side, as well as the aim of the analysis and the issues to be resolved. Take nothing for granted. The procedure and findings of the analysis can only be valuable if the aim of the study is well understood. The building of the analysis framework is the second step. The company is adaptable and versatile. Using data to abstract the company necessitates making bold assumptions, double-checking them, and not jumping right into analysis when the demand comes in.

Analytical method that is scientific.

The aim is apparent, and the reasoning is clear, but it is difficult to draw accurate conclusions during the process of particular analysis if the analysis method is not adequate. Make a list of some frequent flaws: Verification of data sources is lacking: Industry negotiations, particularly in B-side business, necessitate a lot of offline communication, and the communication process is largely completed by sales, which can lead to data distortion, which affects analytical results. There is no awareness of the entire process: Only concentrate on a single link, neglecting the upstream and downstream parts of the business chain; if a single link fails, both upstream and downstream links must be modified; There is no correlation analysis: simply a single indication, only simplistic attribution, ignoring the reality that many problems are caused by several causes and that there is no causal relationship between multiple components and problems.

To provide solutions, combine business actions.

When anomalous indicators were discovered, appropriate steps were not performed in conjunction with data analysis; no conclusions were drawn after evaluating and listing data that would aid business development. All of them are "formalistic" data analyses that appear to be without flaws but have no guiding relevance. Finding problems in the business, analysing the problem with business understanding and logical thinking, determining the crux of the problem or the development trend, formulating a feasible plan, and then coordinating the resources of all parties to promote implementation should be the goal of data analysis.

From business to business, it's all about business. Data analysis isn't just for identifying problems or presenting reports; it can also be utilised to steer corporate growth!