Data has gravity

When you see this title, you can’t help but ask questions. Gravity is generated between two objects, how can data has gravity? Who is it attracting? Is data gravity better for more or less?

The universe is complex and changeable, but it still has a relatively stable system. For example, the Milky Way and the solar system we are in, the construction of this system is the universal gravitation proposed by Newton.

Similarly, we can also think that the data itself is also complex, such as the various business systems within the enterprise, the external government, the individual, and so on. In such a complicated environment, can it also produce some stable systems?

Here, we have to propose Newton’s universal gravitation formula, which is also the law of data gravity.

How to use data to prove the performance or value of the IT department?

The first is the quality of the data, the coarse granularity of the data, the problem of fraud, and the problem of dirty data can seriously affect the quality of the data.

Value will not exist for no reason, and it depends on our decision-making department, business department and IT department. If these three departments are separated, the distance between the data and the value becomes longer, data gravity will decrease, then value will decrease.

Regarding the distance between data and value, there is usually no guarantee for this link in the enterprise.

How to Create Heavy Quality Data?

First, reliability, data needs to be highly credible and cannot be falsified;

Second, depth, the data needs to fit the business needs, easy to analyze;

Third, speed, data is time-sensitive, and only through efficient analysis can it achieve its greatest value.

Let’s take a look at the current problems with enterprise data.

In order to solve these problems, what should we do?

• The underlying data processing – establish data credibility

• The establishment of the intermediate model – improve data depth

The model is divided into two categories, one is the data model and the second is the business model. The data model is actually based on the characteristics of the data itself, whether it is our main data, or our international top ten subject areas. The second is to develop for our business needs.

• Front end quickly rendered – increase the data speed

The data has a scene, so different diagrams, different tables, and its application in the actual process are different. In each case, for different reporting of leaders, different processing methods are selected for different applications of the business, and such data analysis is a quick analysis.

How to create value for quality

The value of heavy quality requires scenes to connect.

We can see from this picture that the value of the connection needs to be decided by the decision layer, the IT layer, and the service layer. So what are the data scenarios that the decision-making layer generally faces?

How to reduce the time for data preparation?

The solution is to automate the data, fix the required data, automatically change it according to the monthly changes, and then do not need so much manpower to repeat the work every month.

How to reduce the time to understand the data?

We can automatically push abnormal data or key data according to the personal habits of the decision-making layer, for example, through the mobile terminal prompt of the data dashboard.

How to scientifically measure the value created by various departments?

For decision makers, amoeba can be used to save manpower.

For the business layer, the focus is on people and things.

With regard to the growth of staff, we take an active + passive approach to empowering our business and promoting personal development.

For things, we want the data to meet more business scenarios like the picture below.

Let’s take a look at the data scenarios faced by the IT layer.

Many times, the decision-making layer believes that the IT department staff only needs to continuously optimize and upgrade the existing business, but in fact, more importantly, the IT layer can solve the urgent needs of the business layer.

To conclude, the ultimate goal is that our decision makers do not simply listen to the report, look at the PPT, but use the data to support his decision, so that his decision can enhance his management. For the business layer, we not only respond to the demands of the leaders, but we also need to think about how to use the data to promote business transformation. For IT layer, they need to be integrated with the business, from communication to guidance, rather than confrontation, in this way to improve data quality.

About how to shorten the distance between data and value, due to length limit, will be introduced in the next article.

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