What is data analysis? Data analysis is the use of large amounts of data, statistical and quantitative analysis, explanation and prediction, and fact-based management to drive the decision-making process and achieve value growth. Today's enterprises are paying more and more attention to data analysis, eager to tap potential value from the massive data they have accumulated, promote business growth, and improve corporate efficiency.
How to perform data analysis? How to play with data analysis? This is the biggest difficulty most companies encounter when implementing data analysis. At this time, many companies will consider using data analysis tools. However, there are various data analysis tools on the market. When choosing, you should pay attention to choosing practical ones. As someone who has experienced it, I can recommend some that I commonly use in my work. I recently experienced the TableAgent data analysis agent independently developed by Jiuzhang Yunji DataCanvas Company. TableAgent allows large models to have a positive impact on personal productivity. Empowerment, from writing minutes and making summaries to a new level, as long as you can ask questions, you can become a senior data analyst and gain insight into the mysteries of data. Let’s take a look!
Data analysis background
- In the digital age, the importance of data analysis is as ubiquitous as the air. Business data analysis is the foundation of digital management and intelligent decision-making. At the same time, data analysis is a highly professional job. Descriptive analysis, diagnostic analysis, and predictive analysis will be daunting to most people who can only use Excel.
- Data analysis plays an important role in various industries and fields
TableAgentIs this the case?
- On July 9, 2023, OpenAI released the Code Interpreter plug-in that shocked the world. For a while, everyone became a data analyst from dream to reality. In fact, as early as June 28, Jiuzhang Yunji DataCanvas company had taken the lead in releasing TableGPT. Now Code Interpreter has been renamed Advanced Data Analysis, and TableGPT has also been reinstalled with a series of major upgrades under the name TableAgent to face the society. Open for public beta.
- TableAgent is an intelligent agent developed on the basis of the DataCanvas Alaya Jiuzhang Yuanshi large model that can realize privatized deployment of enterprise-level data analysis. It has very powerful intent understanding capabilities, analysis modeling capabilities and insights. After fully understanding the user's intentions, TableAgent autonomously uses advanced modeling technologies such as statistical science, machine learning, and causal inference to mine value from the data, thereby providing insights to analyze opinions and guide actions.
- Conversational data analysis, what you need is what you get
- Private deployment, data security
- Support enterprise-level data analysis, large-scale, high performance
- Support domain fine-tuning and specialization
- Transparency process, audit supervision
TableAgentUsage scene
- Financial sector
- Transportation Industry
- Communications industry
- Internet industry
TableAgentMain functions
- TableAgent can provide private deployment for enterprises. Code Interpreter's biggest obstacle for many domestic enterprise users is that enterprise data cannot be transferred to online shared service platforms due to various reasons such as security and compliance. TableAgent provides privatized deployment for enterprises. The system is deployed within the enterprise and data is not leaked out, which fundamentally solves the problem of security and compliance. At the same time, TableAgent can also meet the requirements of large-scale and high-performance analysis of enterprise-level data. This is also Code Interpreter's current shortcomings.
- Data analysis is different from language tasks such as dialogue, summarization, and writing. It requires understanding the data and the user's analysis needs. It requires being able to automatically write code, debug the code, and run the code. It also needs to understand the data results generated by the code running and then use them. Gain deep insights into your data
- Code generation tasks are different from general writing tasks. Typos can be tolerated in writing and will not cause content generation to fail. However, in code generation tasks, even if there is only one character error in the variable name, the entire task will not run and the task will fail. Therefore, implementing open data analysis based on code generation is a great challenge to model capabilities.
- Most large model applications for data analysis are based on fixed indicator systems or calls to existing analysis system interfaces. This technical route does not require the generation of code, but it is not open enough and users’ analysis needs are limited by the existing indicator system. Ability to design and analyze systems. TableAgent chose the more difficult code generation route and creatively proposed an expert model group approach to solve these problems.
TableAgentExperience
- Body location:TableAgent
- Main page after registration
- First, the right side is the data set, which provides some sample data sets. You can choose or upload the data set yourself.
- Next, we select a sample data set. Tableagent will help us design some analysis scenarios based on the content of the data set. Scenario analysis is very interesting and more comprehensive than the human brain.
- Intimately list table abbreviations
- I couldn't help it anymore, so I tried it first. This kind of data analysis application scenario for product marketing is the most common. Let’s see how it performs.
- The data analysis results are clear at a glance and achieve the results we want.
- What’s surprising is that it’s also good news for code programmers.
- Based on the data analysis model, the output implementation ideas and code process make the entire analysis echo from beginning to end. It is simply too powerful.
- At the same time, the analysis conclusions and the statistical and analytical dimensions of diffusion will be given thoughtfully.
- Next, we upload the data content of our real scene in the following format:
- The accuracy of the analyzed data and the shared histogram are relatively obvious.
- During the analysis process, the application of functions can provide developers with better and clear ideas to verify the results of data analysis.
- Through the process of multiple attempts, the thinking process can be seen more clearly, making it clear at a glance.
- The entire data analysis process makes all analysis results, calculation dimensions and analysis processes clear at a glance, freeing our hands and productivity. A great experience!
TableAgentAdvanced Point
- TableAgent is an enterprise-level data analysis agent developed on the basis of the DataCanvas Alaya Jiuzhang Yuanshi large model that can achieve privatized deployment. It has very powerful intent understanding capabilities, analytical modeling capabilities and insights, and privatized deployment has surpassed There are many data analysis models on the market.
- After fully understanding the user's intentions, TableAgent independently uses advanced modeling technologies such as statistical science, machine learning, and causal inference to mine value from the data, and then provides insights to analyze opinions and guide actions.
瀻结:
- This is a new result of integrating innovative applications.
- The Alaya Jiuzhang Yuanshi large model independently developed by Jiuzhang Yunji DataCanvas Company is the key technical support behind TableAgent. The Alaya-ZeroX model group, which is fine-tuned on the Alaya basic large model, completes complex analysis tasks through a series of model combinations that are good at different capabilities. Models with different parameter sizes simultaneously meet the requirements for generation quality and inference performance.
- Another important capability brought by this release of TableAgent is professional fine-tuning. Different companies in different industries have professional language backgrounds and unique needs for analysis models in data analysis. It is difficult for general analysis tools to meet professional requirements. TableAgent provides professional fine-tuning for enterprises.
- TableAgent has designed a T+ (Table Family) system for this purpose, which can efficiently implement customized fine-tuning work. At the same time, the system has the ability to self-iterate. The systematic system supports more efficient upgrades in all aspects of data analysis, allowing users to You can get an ever-upgrading data analysis experience without any awareness.
- DataCanvas Table Family （T+）
- TableAgent: data analysis agent
- TableBench: Data analysis capability evaluation benchmark
- TableTuning: Data analysis LM fine-tuning
- TableInstruct: data analysis instruction set
- TableLive: self-iteration engine
- Alaya-ZeroX: Data Analysis GPT Model Group
- DeepTables & YLearn: structured data deep learning, causal learning toolkit
- DataCanvas Table Family （T+） Conceptual Framework
- In the future, TableAgent will further integrate the analysis capabilities of unstructured data, and jointly innovate with the self-developed DingoDB multi-modal vector database and DataCanvas Alaya nine-part knowledge model. In the future, there will be further upgrades in complex analysis tasks, automation, human-computer interaction, and intelligent agent collaboration.