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This article shows you how to build a big data analysis platform for enterprises. The content is concise and easy to understand. It will definitely make your eyes shine. I hope you can gain something through the detailed introduction of this article.

In today's fast-growing information age, everything is being converted into data, everything is being measured by data, and some BI tools can be used to build flexible data analysis and display platforms. Companies represented by the Internet are paying more and more attention to data. Data acquisition is no longer difficult. The difficulty is how to analyze and gain insight agilely.

The information age is gradually changing to the data age, and data is becoming more and more important. It can reflect the business situation of the company and provide accurate reference basis for the company's operation and decision-making. The data platform is the middleware connecting data and enterprises, which can clearly display these data to enterprise leaders through certain specifications.

For enterprises to build data analysis platform, Qingdao aviation data architect Zhan Xuechao emphasized that the establishment of data platform should realize internal bottom-up and top-down management combination. Only by obtaining the recognition and support of business layer and leadership layer can the sustainable development of data operation be realized.

What is a data platform?

The data platform actually collects and organizes all the company's data, including sales data, financial data, ****, and some industry data, and displays them according to certain themes. For example, the topic of financial analysis will include some financial related statements, including the company's profit analysis, loss point analysis and so on. Through the analysis and display of these themes, to accurately display the company's business situation, as well as industry dynamics, so as to provide more accurate scientific guidance for the company's business, and thus achieve lasting profits.

The data platform will have some raw data, mainly sales, finance, etc. and some other data, as well as some industry data, such as business data for the home appliance industry. In terms of data processing and data display, in addition to the usual daily, monthly and annual reports, there are corresponding sales trend analysis, inventory warning and personalized product recommendation.

How to build a data platform?

The first premise is that the company must have its own raw data accumulation, and it must have a data production system, including the accumulation of historical data.

Method: Combination of bottom-up and top-down

"Down" refers to the company's technical team, business team, sales, etc., as well as business and IT departments. "Up" refers to the leaders of departments and senior management of the company. Let the IT team and business team promote this matter together, and closely integrate with the leadership of the company to participate in the construction of the data platform together. Only in this way can the sustainable development of the data platform be realized.

In the process of construction, communication is very important.

Perhaps the business staff and IT team have their own ideas about the construction of the data platform, and they also want to do something for the company through data, but the company's leadership may rely more on their own experience and may not necessarily believe the information fed back by the data platform. Without the support of the upper leadership, it is easy to lead to fatigue in the process of building a data platform.

In addition, the upper leadership may want to do a data platform processing, all the data to do a unified arrangement and display. However, when it is transmitted to IT teams and business departments, it is not paid much attention to in actual applications. For example, the reports made by IT personnel are not what business personnel want, resulting in poor communication between each other and slow data platform construction.

Principle: Fast iteration

When establishing the data platform, we can not determine some goals, set some themes, display achievements, share them with business personnel and leaders, let them participate in evaluation and suggestions, and continuously optimize and improve them. When relevant personnel have a sense of participation, the data platform will develop for a long time.

Determining the technical architecture of the data platform is parallel to determining the analytical theme.

Fast iteration, relieving the business of manual analysis.

First report, accumulate business experience, establish model for analysis and continuous improvement.

Attention to technical preparation and business knowledge preparation for lag big data analytics platform and business modeling

Data Platform Technical Architecture

The purpose of setting up full-volume DB is to reduce the pressure of directly reading data from production environment, unify data types, and facilitate cross-database, cross-database and file query processing.

The data warehouse is essential to the data platform, but not a priority. Building a data warehouse is time-consuming and can be done gradually.

According to the actual situation of the company, the data mart is divided, and according to the business rules and their own experience, the data analysis model is established.

When ETL is not enough to support or real-time requirements are difficult to achieve, consider distributed computing such as Hadoop.

It takes a quick iteration step by step. At the same time, we should also have a long-term perspective and try to avoid pushing forward restructuring. Also be sure to pay attention to time control and milestone building.

The above content is how enterprises should build big data analysis platforms. Have you learned knowledge or skills? If you want to learn more skills or enrich your knowledge reserves, please pay attention to the industry information channel.

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