dialogue with yu binping of qixin group: big models will accelerate the implementation of "data flywheel" in enterprises
2024-09-14
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data, as the fifth largest production factor after land, labor, capital, and technology, and one of the three elements of artificial intelligence, has become a consensus in the industry. in the wave of digital transformation, companies are constantly seeking innovative solutions to drive business through data in order to achieve business growth and efficiency improvement.
however, although many companies have started to build data platforms in the past few years, they often face the dilemma of "easy to build, difficult to apply". recently, yu binping, cto of qixin group, and 51cto discussed the difficult issue of how companies can use data to drive business growth.
data must be managed and applied
in the process of digital transformation, data management and application have become a topic that enterprises cannot ignore. yu binping believes that data collection, cleaning, governance and value mining are common challenges faced by many enterprises.
in actual operations, enterprise data is often scattered across different systems and departments, and some data is difficult to integrate due to permissions, formats, or technical limitations. moreover, much data is currently collected after the fact, and there are certain problems with data quality. in addition, different businesses have different data standards and different data contents for data cleaning, and ultimately "data collection, quality, and governance issues often cause the big data department to become a reporting department."
yu binping pointed out that a common problem faced by many companies today is that big data has become "big data", that is, although the amount of data is large, it is static and has limited practical application value. behind this phenomenon is the lack of data cognition and effective data application methods.
yu binping emphasized that data applications must be closely integrated with business scenarios and technical characteristics. enterprises need to cultivate compound talents who understand both business and technology. they can deeply understand business needs and use technical means to mine data value, thereby bringing innovation and growth to the enterprise. for example, in the retail industry, by analyzing customer behavior data, new business opportunities can be discovered and supply chains can be optimized; in the e-commerce field, using user authorization data for personalized recommendations can improve user experience and sales performance.
as a core component of the enterprise digital architecture, the data middle platform plays a key role in gathering and integrating data, whether in retail or to b business, ending the situation of data silos, realizing centralized management and analysis of data, and promoting data sharing and collaboration between different branches and business units. however, in yu binping's view, when faced with the deeper challenge of how to make data generate greater value and promote business growth, the traditional data middle platform seems to be unable to cope with it, "because the nature of the middle platform is difficult to play a role in business growth."
the flywheel turns, the concept comes first
so how should enterprises deal with the inability of the data center to enable business growth?
"to make data generate value or help business growth, we must first have a guiding concept." yu binping believes that without a guiding concept, it is difficult for the technical department or technology alone to make data generate greater value. in other words, under the guidance of the concept, we can promote the implementation of algorithms, data warehouses, large models and other "techniques". only when the two are carried out in parallel can data generate better value.
this consideration coincides with the concept of "data flywheel". the concept of "data flywheel" emphasizes data consumption as the core, promotes the deep integration of data flow and business process, and forms a self-enhancing cycle mechanism. the key to the data flywheel is to integrate data analysis and application into every aspect of the business, so as to realize the activation of data assets and innovation of business applications.
"in traditional enterprises, the 'data flywheel' can serve as a concept or guide for data application." yu binping emphasized that the data flywheel is not contradictory to the data middle platform, but is a leap forward based on it, pushing data application to a deeper level. as the infrastructure for data processing, the data middle platform focuses on building public data warehouses and big data platforms; while the data flywheel, with its dynamic circulation and continuous optimization characteristics, has become an accelerator for data application. the two complement each other and jointly promote enterprises to achieve a more efficient and intelligent leap in data governance and application.
taking his own business as an example, yu binping suggested that for data-driven enterprises, relying on data and technology for customer analysis and precision marketing has become the norm, and the continuous improvement of technical capabilities is crucial to ensure the accuracy of data analysis and results, including the construction of algorithms, models, data warehouses and ai applications. for those corporate teams that lack awareness of the value of data or technology, the key is to proactively find and solve business pain points, demonstrate the effectiveness of data technology through actual cases, thereby stimulating the solution of more needs and pain points, forming a virtuous circle, and unleashing the potential value of data.
large model support, flywheel landing accelerated
although the concept of "data flywheel" coincides with the current needs and pain points of enterprises, how to make the "flywheel" turn is still a problem to be explored.
in yu binping's view, the successful implementation of the data flywheel depends on clear customer and business goals, high-quality data, and appropriate technical means.
first, the data flywheel should focus on customers and businesses, rather than simply pursuing technology, to ensure that digital strategies can truly help customers and business growth. secondly, data quality must be ensured during the implementation process, covering data collection, processing, and construction, which is the basis for achieving the data flywheel effect. finally, technical capabilities must be considered, and appropriate algorithms, ai models, and other specific technical means must be selected to maximize value.
on the technical level, the rapid development of big models provides a powerful accelerator for the implementation of the data flywheel.
yu binping pointed out that the application of big model technology has greatly improved the convenience and efficiency of data application. "without the support of big models, the data flywheel may only remain at the theoretical level, or can only be applied in internet companies with data technology genes. for most traditional companies, to put this concept into practice, they need to rely on the power of big models."
for example, big model technology can quickly identify and classify data through its powerful semantic understanding ability, thereby simplifying the data governance process. in yu binping's practice, big models are used to optimize data cleaning and governance work, with an accuracy rate of more than 95%. such technical applications not only improve the efficiency of data processing, but also release more data value for enterprises and provide strong data support for business decisions.
in conclusion, for many chinese companies, the main goal is still to digitize their business and use technology to better serve their business and customers. companies must grasp this trend, continue to explore and practice, use data as a wing, accelerate the rotation of the flywheel, and achieve a leap in digital transformation. (xianning news network)
