Method for improving data utilization efficiency

Abstract

The invention discloses a method for improving data utilization efficiency, and relates to a data utilization method. According to the method, labeling and index operation are carried out aiming at a complex BI system constructed by an enterprise, that is, labels are divided into core labels and execution labels, after core label operation is carried out on the existing BI system of the enterprise, the labels are issued to a marketing system to be executed, the core labels are grouped again in the execution process of the marketing system, and then an operator adopts a targeted marketing means for the groups, so that the existing data assets can be well activated, and the data can be applied to a flexible and changeable marketing scene. Rapid combination and application of enterprise internal core data and marketing scenes are realized, the method also has the characteristics of relatively low implementation cost and relatively high implementation speed, and the data utilization efficiency of financial enterprises is improved.

Description

technical field [0001] The invention relates to a data processing technology, in particular to a method for improving data utilization efficiency. Background technique [0002] At present, many financial companies have a huge number of customers and a lot of historical data. For these data, companies have built complex BI systems for labeling and index calculations. However, it is difficult for these existing data assets to be quickly applied to marketing and customers. Come in operation. The traditional idea and approach is to import these data into the marketing automation system. However, such an idea and approach is just a good wish. When it is implemented, it will encounter the following three problems: [0003] 1) These historical data are transaction data, index data and third-party procurement data. Their data models are quite different from those of the marketing system. It is difficult to directly apply them to marketing scenarios and require a huge amount of work...

AI Technical Summary

Problems solved by technology

[0003] 1) These historical data are all transaction data, index data and third-party procurement data. Their data models are quite different from those of the marketing system. It is difficult to directly apply them to marketing scenarios and require a huge amount of work to convert

[0004] 2) These data are sensitive data in the financial industry and are not suitable for marketing systems

[0005] 3) The amount of these data is huge, and the marketing system needs to pay extra high costs to undertake them, and these costs are all repetitive construction

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