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The transformation path of the world's largest semiconductor distribution company: continuous trial and error, building a corporate data culture In the past two to three years, the digital transformation achievements of the General Assembly have been the objects that many companies want to learn from, but for the General Assembly, this kind of development has found its own path through years of exploration, trial and error. How exactly did they construct the data culture of the enterprise? Having ERP does not mean that the company has successfully digitized Zhuang Shixiong, Chief Information Officer of WTU, pointed out that during the epidemic period, employees can smoothly carry out remote work, but only by establishing an online work environment so that communication is not restricted by space, but it does not mean that the company has successfully digitized. He gave an example that many companies currently have ERP systems, but the existence of an ERP system does not mean that they have successfully digitized. The real digitization must start with the construction of a complete data culture. How to build a corporate data culture? Zhuang Shixiong divided the construction process of the General Assembly into four steps. The first is to establish a data-based advance team. At the beginning, it took the form of a special task force. The General Assembly began to establish its own data science team five years ago, mainly through This team explores technology and connotation, and quickly understands the meaning and gap of data-based and data-driven business. The second step is to deconstruct traditional non-digital behaviors into data through deconstruction. For example, although the company's ERP system collects all customer transaction data, most offline behaviors during the transaction process will not be recorded, such as contact frequency, other information provided by customers to the business, these traditional non-data behaviors, New methods are needed to reconstruct data into data. If you want to use these accumulated data to further predict customer needs, what external data should be extended and included, and how should the data be linked? These are all things that must be considered in the next step. Even if you have data, it is useless if you don’t have a technical team to use it effectively. Therefore, the next stage is to build a team that can handle data and AI. Finally, on the basis of the accumulated data and the technical team, more projects will be implemented and cultivated. More importantly, the process of data deconstruction, construction and optimization is a cycle, which will be adjusted continuously with the development of business and changes in the general environment. This is the basic spirit of digital transformation: there is only a starting point and no end point. Is the purpose in the corporate vision or to win the race at hand? Although establishing a data culture is an important thing, few companies are currently able to adjust their structure from top to bottom and invest a lot of resources in transformation like the General Assembly. "Do you want to do things based on the vision, or win the competition in front of you?" Zhuang Shixiong believes that just entering the second step is a very hard work, requiring a high investment of manpower and money, but it is impossible to see results in the short term. " It’s very difficult to succeed without top-level support.” Since 2019, the information department of the General Assembly has held an internal hackathon at the end of each year, hoping to stimulate colleagues to think, stimulate creativity, and empower internal actual business through technology. In 2022, the General Assembly will further cooperate with the Artificial Intelligence Technology Foundation (AIF) to launch a three-stage AI talent empowerment project for nearly eight months, starting from the Python programming language necessary for AI, followed by AI practice courses covering core technologies such as machine learning, deep learning, and convolutional neural networks, as well as practical combat to solve the pain points of various departments. What is different from the past is that this year's practical combat has expanded to various departments, and a team of domain knowledge experts and IT colleagues will use AI to solve problems raised by each department.
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