An Enterprise-level Data Empowerment System That Features a Closed Loop, Accumulation, and Sustainability

Developing the Enterprise-level Data Empowerment System

Four Key Steps

Our goal is to quickly form a closed loop that covers data at different enterprise touchpoints and then bring together the scattered data to quickly use it to empower business. In this effort, we must consider four key steps. The first is the transformation of businesses into data. For this step, we must consider whether all the touchpoints of an enterprise are authentic and connected. The second is the transformation of data into assets. For this step, we must consider whether data can be managed as assets. The third is the transformation of assets into applications. For this step, we must consider whether an enterprise’s assets are effectively applied. The final step is the transformation of applications into value. For this step, we must consider how to leverage data assets to empower businesses. The ultimate purpose of all applications is to boost business growth, customer acquisition, and value production. To achieve these goals, it is essential for us to form a closed loop that accumulates data. Ultimately, the data mid-end and data energy must be sustainable.

Purpose of Developing This System

The following figure shows how we built a closed-loop, accumulative, and sustainable enterprise-level data empowerment system. The figure shows the enterprise-oriented data bank to be released by Umeng+. Now, let’s discuss how data banks and businesses work with each other. Based on cloud infrastructure, such as MaxCompute, Umeng DataBank will continuously help enterprises collect data from various scenarios and touchpoints, perform data governance, purification, model processing, and form various application services. Based on the connection capabilities of UMID, multiple accounts and terminals are normalized, and data can be connected across different terminals, specifically different mobile clients, servers, and client platforms. All of this helps developers gather data assets from all scenarios and touchpoints and manage applications.

The Umeng DataBank

Every company needs to build its own data bank. For example, in the Alibaba ecosystem, we have millions of merchants during the Double 11 Shopping Festival, and many brands and merchants have built data banks on Alibaba. Similarly, Umeng+ has been deeply engaged in data intelligence services for nine years. Drawing on its experience of serving millions of Internet enterprises, Umeng+ launched Umeng DataBank for developers and, together with Alibaba Cloud MaxCompute, formed a set of core solutions for users. Data banks are required for solving several problems. Data banks can solve the problem of data asset management and the application of data, which can be expressed in four words: collection, construction, management, and use. Businesses can be transformed into data, and data can be transformed into assets. This process involves data collection and the conversion of terminal data into data assets. Next, these assets are applied. In this process, we push various messages and use marketing to obtain new customers, which includes app push and various operational recommendations. These services all can be provided by data banks.


AI and smart engine products are essentially data production and collection products. Collection is the fundamental basis of data quality, whereas the efficiency, quality, and efficiency of data collection are crucial. Data collection requires you to answer several questions. Do you have full control over your company’s data tracking? Do you understand how data should be tracked in a given scenario? What kind of data will be generated after tracking? Are the tracking points correct and valid? Tracking is a long-term operation. You must constantly verify that tracking is healthy and ultimately related to the fundamental questions you’re concerned with. If data tracking is inappropriate, then all the capabilities based on it, such as AI, will not function properly.


After data is collected, the most important thing is to solve the user asset problem. First, user asset management must solve the problems of trust and normalization. Data is created at many touchpoints. Among all the requests sent to an app, many represent fraudulent traffic. So, how can we ensure that devices are trustworthy? Based on the connection capabilities of UMID, multiple accounts and multiple terminals are normalized. By connecting data across different terminals (different mobile client, server, and client platforms), it supports stream conversion and relationship insights. After normalization, we can create an automatic tag production library to ensure efficient tag production in private domains. This empowers business teams to quickly create tags, gain insights, identify target users, and ultimately form operation actions for customers.


After collecting data, Umeng+ integrates it with the customer’s data. By seamlessly connecting with MaxCompute in the cloud, Umeng supports greater openness and return capabilities.


No matter how powerful an enterprise’s containers, databases, and algorithms are, or how intelligent their applications are, it is necessary to go back to the four key steps. First, we must transform businesses into data to manage data collection and quality. Second, we must transform data into assets. This gives the management a clear understanding of user data assets, the number of terminals, the number of touchpoints, the data generated each day, and the number of accumulated users. Third, we must transform assets into applications. Accumulated data should be quickly converted to applications that serve the business team. This way, the business team can better innovate with the help of technology and data and will not have to wait for resources. The most fundamental thing is to build scenarios and closed data loops that cover all touchpoints and business behaviors. Such scenarios and closed loops can accumulate data assets. This is the only way to ensure that the enterprise mid-end and data empowerment are sustainable and that the power of data grows richer and better throughout the data utilization process.

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