Data-driven software advantages (Chepinjue) – EJ Tech

This articleauthorChe Pinjueis a director of Hong Kong Science and Technology Parks Corporation, a visiting associate professor at the School of Chinese Business at the University of Hong Kong, a senior consultant at Alibaba Cloud, and writes a column for Hong Kong Economic Journal“Big Data for All”.

Earlier this month, I attendedGlobal Digital Economy Conferenceone of the speakers pointed out the difference between software and dataware. In fact, the dataware he mentioned is not a common word, and a more appropriate name should be data-driven software.

First of all, traditional software is based on static predefined logic written by developers. Basically, the algorithm will not change and output directly unless modified or updated by new code. The data within the software needs to be clearly defined, and operations are usually controlled by preset inputs rather than driven by dynamic data patterns. During the development process, the focus is on collecting requirements, system design, coding, testing, and maintenance. Data management may be a component, but not a core feature. Typical examples include word processors, financial management systems, and inventory control systems. The performance of traditional software usually takes into account user needs, balancing efficiency, speed, reliability, and also taking into account responsiveness and resource utilization.

On the other hand, data-driven software operates based on models trained with data and data analysis. It relies on dynamic (even real-time) data input to continuously adjust its behavior or output. The core functions of data-driven software depend on the quality, quantity and relevance of data, and usually include functions such as adaptive learning, predictive modeling and real-time data processing.

This software development process emphasizes data integrity, data source integration, scalability, and the ability to efficiently process large amounts of data, implement data processing processes, and generally apply machine learning algorithms. Examples include entertainment streaming platform Netflix, Alibaba (09988) recommendation systems, automated financial trading systems, and personalized marketing tools used by Taobao, a subsidiary of Alibaba. The effectiveness can evaluate the accuracy, reliability, and timeliness of data-driven decisions and predictions; whether the system can scale as the amount and complexity of data increases is also a key indicator.

In contrast, data-driven software is characterized by its ability to adjust to changes in data, improve upon past interactions and modify its output. However, it is highly dependent on the quality and quantity of data, and its performance is directly related to the data processed. Due to the need for continuous data verification, updates and a more complex data ecosystem, the development and maintenance of data-driven software is more complex than traditional software. In actual applications, companies should choose the appropriate type of software based on specific needs. For example, traditional software may be more suitable for applications that require high stability and predictable output. Data-driven software will provide greater advantages for scenarios that require extracting insights from ever-changing data and responding quickly.

At the same time, more and more companies have found that combining the two can give full play to their respective advantages and improve the flexibility and efficiency of the overall system. For example, an inventory management system can integrate data-driven demand forecasting models to more effectively manage inventory and optimize procurement plans. This combination not only improves the functions of traditional systems, but also enables companies to better adapt to market changes and cater to customer needs.

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