The development of AI (Artificial Intelligence, artificial intelligence) is only a hundred years old, but it has profoundly affected people's thinking and insights, and is gradually related to every aspect of everyone's life and work. From the initial rule engine and the introduction of statistical methods, to the expert system based on knowledge representation and reasoning mechanism, to the neural network to promote the accelerated development of deep learning and complex AI algorithms in the context of big data, to the recent hot LLM ( Large Language Model, large language model) lighted the singularity, and ignited the topic of the AI industry. Many fields are rapidly seeing, trying, and implementing the possibility of AI to further promote the rapid progress of productivity, and the data analysis industry is not alone. exception.
Released AI-enhanced engine 4 years ago to unleash big data productivity
In different industries, AI has been regarded as an important engine for future development, among which automation and intelligent assistance and replacement of labor is one of the key applications, and this is especially evident in the field of data analysis.
In the past few years, how to fully combine data and AI has become a hot spot of widespread concern in the industry. With the rapid development of data technology and the continuous optimization of AI algorithms, the combination of Data + AI has become a powerful tool: the big data platform provides large-scale data collection, processing and analysis capabilities, while AI can learn from massive data. Abstract and summarize the patterns and relationships behind it, realize AI-driven automatic analysis, combing and refining instead of manual repetitive labor.
As a leading provider of big data analysis and indicator platforms, Kyligence has been deeply involved in integrating AI into data platforms and indicator applications to support business operations and decision-making more efficiently and self-service. Among them, the AI-enhanced engine serving customers in various industries is one outstanding example. As early as 2019, Kyligence launched an AI enhancement engine to achieve data model adaptive matching business query requirements : traditional data modeling relies on experts to develop and design models, which has a very high threshold for use, and once the business changes The data model changes brought about will lead to a huge workload; the AI enhanced engine will actively analyze business usage patterns, such as actual data characteristics and query habits, and use machine learning algorithms to predict the most commonly used business scenarios to serve At:
- Automatically design models and intelligently recommend optimization suggestions, greatly reducing the difficulty of modeling. On the one hand, it can efficiently empower business personnel to use data independently, and on the other hand, it can also effectively control data development costs;
- Agilely respond to changing business needs, automatically change the data model, and flexibly adjust according to factors such as query popularity and resource usage, and always give priority to the most valuable data to achieve intelligent acceleration.
AI frees human value from tedious, repetitive tasks, unlocking big data productivity
With the intelligent support of the AI enhanced engine to fully release the productivity of big data, Kyligence has also been selected as Gartner's recommended manufacturer for enhanced data analysis for three consecutive years (2020-2022). And the recognition and affirmation of product advantages.
In the 2022 China ICT Technology Maturity Curve Report released by Gartner, Kyligence is listed as a recommended vendor
In the era of large models, bringing business closer to big data
From the 1990s to the beginning of this century, AI has entered a new stage of development. The emergence of NLP (Natural Language Processing, Natural Language Processing) has greatly promoted the civilianization of AI applications. Using AI to identify natural language, analyze and generate text information simplifies the difficulty of people interacting with software and platforms to a certain extent, but NLP has always been troubled by the understanding of natural language, the breadth and richness of the semantic library. Today, with the support of LLM-based foundations such as ChatGPT, the accuracy of natural language understanding, thinking and reasoning ability, and output in natural language are all at a new level. The human-computer interaction revolution brought by LLM is bound to It will also profoundly affect the next round of changes in smart usage.
In the past, the flow of data analysis requirements was usually completed through the dialogue between people and the interaction process between people and the data platform GUI (Graphic User Interface, Graphical User Interface). The main obstacles here are:
On the one hand, business personnel, data analysts, and data engineers need to continuously go through a cycle from explaining requirements to feedback solutions, and then performing complicated data processing and processing. Such a model will not only cause information loss, but also inefficient value conversion A typical manifestation of , the collaboration between multiple different roles often takes several days or even longer to complete the processing of a data requirement;
On the other hand, GUI-based user experience, because it presupposes the user's intention and operation process, even if it is continuously optimized, it still cannot compare to using natural language to interact.
Thanks to the human-computer dialogue mode supported by LLM, the transition from GUI to LUI (Lanuage User Interface, natural language interactive interface) is quietly happening. Such a process of reshaping user experience truly focuses on user ideas , to directly understand the meaning expressed by itself, instead of being transmitted through the common drop-down boxes and buttons on the interface; at the same time, for LUI, it is not only a change in the input method, but also reflected in the insights and opinions at the output end.
What will happen at the next stop of Smart Data?
Starting in 2022, Kyligence will release a one-stop indicator platform Kyligence Zen based on its technical accumulation in AI enhancement engines and the practical experience of many enterprise customers. With the help of Kyligence Zen, enterprises can quickly build an indicator management system, transform the data assets of the data platform into indicator definitions with business meaning, and provide business users with self-service use. Through its low-code indicator services, everyone can quickly use indicators to develop Work.
In the next step, based on the indicator platform, AI enhancement engine and large model technology, what changes will be brought about by the way enterprises use data?
Kyligence will release the blockbuster new product of Data + AI at the user conference held on July 14th. We invite you to attend the digital intelligence transformation event on July 14th and witness another change in smart data usage! Welcome to click the button to register now!
About Kyligence
Founded in 2016 by the founding team of Apache Kylin, Kyligence is a leading provider of big data analysis and indicator platforms, providing enterprise-level OLAP (multidimensional analysis) product Kyligence Enterprise and one-stop indicator platform Kyligence Zen for users Provide enterprise-level business analysis capabilities, decision support systems and various data-driven industry solutions.
Kyligence has served many customers in banking, securities, insurance, manufacturing, retail, medical and other industries in China, the United States, Europe and Asia Pacific, including China Construction Bank, Ping An Bank, Shanghai Pudong Development Bank, Bank of Beijing, Bank of Ningbo, Pacific Insurance, China UnionPay, SAIC, Changan Automobile, Starbucks, Anta, Li Ning, AstraZeneca, UBS, MetLife and other world-renowned companies, and reached global partnerships with Microsoft, Amazon Cloud Technology, Huawei, Ernst & Young, Deloitte, etc. Kyligence has received multiple investments from institutions such as Redpoint, Broadband Capital, Shunwei Capital, Eight Roads Capital, Coatue, SPDB International, CICC Capital, Gopher Assets, and Guofang Capital.