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From last year's global hot ChatGPT, to this year's shiny Sora, big modeling technology and applications are in full swing.
Big model application landing also provides a new idea for the further development of big data technology. Volcano engine previously put forward the "data flywheel" concept as a new mode of upgrading enterprise digital intelligence, which is based on data consumption as the core, by reducing the threshold of enterprise data consumption, so that employees in all positions of the enterprise can look at the data, use the data, so as to achieve more scientific decision-making, more agile action.
In order to further improve the efficiency of the data flywheel, the volcano engine will also introduce the big model capabilities into the data flywheel, so that AI empowered data production, consumption, analysis and application of the whole chain of scenarios, reducing the data consumption threshold of enterprise employees, prying the enterprise data assets, data consumption to drive the release of data value and business efficiency.
In the recently held Data Flywheel Consumer Industry Seminar, experts from Volcano Engine introduced in detail how to realize data-driven business productivity upgrades through the Data Flywheel concept and the VeDI product of the Digital Intelligence Platform in the large consumer industry.
According to the introduction, at this stage, the large consumer industry is showing the development trend of channel differentiation and traffic competition. In the multi-channel and multi-touchpoint market environment consisting of online and offline, private and public domains, enterprises have deposited massive data that is differentiated across the entire domain. However, many enterprises can only use the data for basic operation analysis, and it is difficult to use it in cross-cutting areas such as behavior, membership and operation, as well as in richer marketing scenarios, making it difficult for the silent data assets to burst out with real data value.
Based on the above dilemma, agile and easy-to-use landing tools such as DataLeap, big data R&D governance with AI big model technology, and DataWind, data insight, provide supporting solutions for enterprises. In terms of data asset construction, DataLeap-Development Assistant is able to assist the R&D of digital warehouses with natural language, and support data asset construction through code generation, one-click optimization and repair, intelligent Q&A and other functions.
DataLeap-Data Finder Assistant, on the other hand, helps enterprise employees to easily retrieve multiple types of data sources, such as tables, datasets, dashboards, data metrics, etc., in the form of conversations, and to consume data autonomously and self-service. With the support of DataLeap-DataFinder Assistant, inclusive data consumption continues to inspire more creativity in data exploration by employees.
On the data analysis side, DataWind-Analytics Assistant frees employees from the once tedious task of reporting, enabling conversational commands for SQL query repair, visual querying, and dashboard exploration for more flexible, accurate, and efficient analysis.
In addition to data production and analysis, data application is also a major challenge in the digital transformation process of the consumer industry. Consumers' increasingly segmented, personalized, and convenient consumption needs, as well as the differentiation of online and offline channels, make retail enterprises face the challenges of upgrading their precision marketing capabilities, and digitalized operation of stores, which also puts forward a higher demand for enterprises' data application capabilities.
In this regard, the customer data platform VeCDP linked to the growth of marketing platform GMP, for the enterprise to provide efficient and intelligent marketing decision-making support, through the activities of intelligent circle selection of customer groups, intelligent portrait insights and reach strategy intelligent scheduling and other capabilities, simplify the analysis and strategy development process, to help enterprises to realize the self-service exploration of marketing activities and efficient tuning.
It can be seen that, after integrating the cutting-edge technology of AI big model, the data products under the Volcano Engine Digital Intelligence Platform have continued to make great efforts in identifying, collecting, processing, lowering the threshold, and improving the efficiency, which has provided stronger assistance for the enterprise's data production, consumption, analysis, and application. This series of data-driven links is precisely the vision that Volcano Engine Data Flywheel is committed to realizing.
In the face of the emerging and unlimited potential of big model technology, Volcano Engine's agile, secure and efficient all-links digital intelligence products may further help enterprises to explore the business growth under the new technology, and accelerate the transformation of digital intelligence empowered by the data flywheel.
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