What data analysis does traditional enterprises lack?

What data analysis does traditional enterprises lack?

In search engines, integrated portals, social networks, instant messaging, e-commerce and other enterprises operating under the long-tail economic model of the Internet, data analysis has always attracted much attention. These enterprises are at the forefront of data analysis technology and application, constantly innovating the basic data architecture, accumulating massive data, having huge data teams, and constantly deepening the application practice of data analysis in their respective relatively accurate business fields.

However, in traditional enterprises, although more and more attention has been paid to the application of data analysis methods in management and business decision-making, compared with Internet companies, the depth and effect of traditional enterprise data analysis applications are far from enough. So what is the lack of data analysis in traditional enterprises? According to the long-term practical experience in traditional enterprises, here are some personal feelings.

First of all, the most important thing for enterprises is data analysis tools. In recent years, we are surprised to find that in many cases, enterprises often have one or even more data analysis tools, such as purchasing mainstream business intelligence suites or data analysis and data visualization tools. This shows that enterprises have realized the importance of data analysis, but mistakenly believe that buying a set of advanced business intelligence or data analysis tools will enter the era of data analysis as soon as they run the data analysis platform.

Secondly, traditional industries lack general attention to data analysis. Except for a few well-managed enterprises, many traditional enterprises are people-oriented, thinking that their daily business is well understood and there is no data analysis. There are also some enterprises that think that data analysis is only aimed at top management, while business intelligence/business decision-making system that consumes a lot of budget is only positioned to provide a small amount of highly summarized data for top management (reflected in KPI kanban, etc.). ), these can't help management decision-making, let alone track the implementation of management decision-making and promote the development of management decision-making. And senior managers often don't use a system specially built for him.

Thirdly, compared with Internet companies, traditional enterprises lack professional data analysts and data acquisition and analysis skills. In most traditional enterprises, there is no special data department, post or role, and the data requirements for management and operation decisions are often borne by IT departments, and the IT departments of many enterprises are also under-constructed, and their skills are mainly IT system planning and operation. Therefore, enterprise data lacks sufficient capacity to plan and implement data analysis.

Then, traditional enterprises often lack the focus of data analysis. Compared with Internet companies, traditional enterprises have many characteristics, such as wide business scope, huge organization, multiple management levels, complex business logic and so on, in addition to the number of users and data. The enterprises operated by the Group have many business segments and complex holding relationships, which are far from being comparable to short and sophisticated Internet companies. The challenge of traditional enterprises trying to conduct all-round data analysis is enormous. Even in the same enterprise, there is no single effective analysis object, analysis mode and analysis means. Therefore, traditional enterprises must effectively identify the needs of core data analysis according to the management and operation problems they face for a period of time. It is neither realistic nor effective to lack key data analysis.

Then, we find that in traditional enterprises, there is often a lack of effective means to obtain data. In traditional enterprises, there are generally more than twenty core business systems, different types of database systems, a large number of database instances, and a large number of manually maintained data files. In a medium-sized business system, there are often more than 1000 tables, not to mention that some core business systems are closed systems. It is very difficult to extract business data directly from the business database, which is almost equivalent to restoring complete business logic. Therefore, it is difficult to effectively integrate data at low cost in a short time. Even if many enterprises have established data warehouses, they cannot fully meet the demand for data acquisition.

Finally, traditional enterprises lack a comprehensive grasp of data assets. Due to equity, history, business and other reasons, many enterprises that operate in groups or have huge marketing networks do not adopt centralized systems. Business systems and databases are also deployed in independent subsidiaries or remote terminal stores, while functions such as management and business decision-making, products and marketing strategies are located in the group headquarters, marketing headquarters, business sub-groups and regional management institutions. The contradiction between the fact that core data assets are not mastered and the functional requirements of management and business decision-making is the primary obstacle to data analysis. Many of these enterprises can only obtain the data needed for management and business analysis by manually collecting the data submitted by their subordinates, and the scope, depth and efficiency of data analysis are extremely insufficient.

Therefore, there is still a long way to go to fully popularize the technology and application of data analysis in the management and business decision-making of traditional enterprises, and the application of big data mentioned by Internet companies is even out of reach in traditional enterprises at this stage.

The above is what Bian Xiao shared for you about the lack of traditional enterprise data analysis. For more information, you can pay attention to Global Ivy and share more dry goods.