In today's digital era, the convergence of AI and BI is undoubtedly a vivid practice in the process of digitalization. Traditional BI is gradually showing signs of weakness in the face of rapidly evolving market demands, while intelligent BI with the power of AI is attracting the attention of many enterprises with its impressive capabilities and incomparably broad development prospects.
So, what exactly is the relationship between AI and BI? IS AI FOR BI OR BI FOR AI? Is the integrated development of AI+BI a new trend leading the future? Now, let's take a detailed look at the future prospects of intelligent BI from the four aspects of "3W+1H", and jointly explore the infinite possibilities brought about by this convergence, so as to find the way forward for enterprises in the digital wave.
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01 What: How to understand the convergence of AI and BI
1. Conceptually and theoretically, the AI+BI model is valuable and promising
The difference between AI and BI is that BI is responsible for sorting out production relationships, and AI is an advanced new quality productivity. Then, the AI+BI model embeds AI into BI to build an AI-based BI platform, and uses AI intelligence to enable the BI system to solve more complex business scenarios and produce more accurate analysis results, so as to make decision-making more scientific and accurate.
2. In terms of specific scenarios, the AI+BI model can make some BI scenarios more in-depth and produce more valuable knowledge
For structured data, the BI system can apply some machine learning algorithms with higher accuracy to obtain more accurate analysis results. For example, in marketing, the use of AI+BI model can obtain more refined analysis results for each user on the basis of user segmentation, so as to give more accurate personalized marketing plans. There is also risk monitoring in the financial field, and the AI + BI model can analyze the internal relationship between financial risks and other indicators and behaviors, and make predictions more accurate.
For unstructured data, BI can apply AI technologies such as image processing, speech recognition, and text analysis to intelligently handle complex business scenarios in BI systems. For example, the AI+BI mode can input data through voice recognition technology and control the production of cockpit and data screen. There is also an intelligent customer service system, which does not need to manually collect customer questions and then assign personnel to answer, and analyzes customer problems through technologies such as semantic understanding and natural language processing to achieve real-time and automatic reply to customers.
02 Why：为何融合更多是 Al for Bl
1. AI is fundamentally different from BI
The development route of BI is data-based, mainly data management and analysis. Although the scope of AI technology is very wide, the main AI technology that can be used in the current BI system is to process unstructured data such as text and images. However, except for some specific industries, most enterprises rarely have the need for text processing and image processing, and most BI systems still need to process structured data.
2. The intersection of AI and BI is only in machine learning and data mining, and this intersection is minimal
AI machine learning emphasizes algorithms, and BI data mining also includes data management, and the selection of algorithms is relatively simple, without complex AI algorithms such as neural networks and deep learning.
3. The AI+BI model is difficult to become the mainstream of the BI market, and more AI For BI is more
It is not to replace BI with AI, but to use the relevant capabilities of AI as much as possible to improve the efficiency of BI tools in all aspects, reduce the threshold for getting started and using BI tools, so that more leaders and business personnel can use BI and help customers maximize the value of BI tools.
03 When：何时迈入 AI for BI 时代
At present, in China, it is expected that around 2025, BI will begin to enter the intelligent stage; By 2030, BI will also be further intelligent. With the continuous maturity of AI technology and BI systems, there will be more and more applications of AI in BI, and there will be more and more overlapping parts between the two, but because they are fundamentally different, they will not completely coincide, but exist in the way of AI for BI.
04 How：目前如何发展 AI for BI 产品
1. Questions are currently asked in a "conversational" manner
Conversational analysis: Directly with dialogue as the core entrance, it can realize real-time query and query, AI-assisted manual analysis of data, data asset retrieval, etc., systematically reducing the threshold for users.
Conversational construction: It is embedded in the original product process to improve the efficiency of construction and production, realize rapid generation of components/dashboards, and make analysis reports.
The core value of AI For BI is to lower the threshold for users, so that front-line business personnel who are closest to the business and furthest from technology can also make better and faster decisions driven by data. However, in recent years, a series of "Q&A BI" products launched by various BI vendors at home and abroad have found that there are very few scenarios that can really be used by users. There are two reasons why most Q&A BI products are reduced to "toys". On the one hand, it is because business people do not have data thinking and cannot ask valuable data analysis questions. On the other hand, it is true that the product is not mature enough. These two challenges make most AI For BI products immature:
Lack of explainability of results: One of the core landing scenarios of AI For BI is "conversational BI" People need to make business decisions based on credible data, and because the entire intent parsing and data generation process is a black box, people can't be sure that the data returned is the data they want to ask.
Recall and accuracy issues: i.e., users ask 10 questions, and how many of them are able to give the correct answers. In terms of technology, most of the previous "Q&A BI" products used the method of rule parsing or rule parsing + pre-trained (small) model to realize text-to-SQL conversion, and the technical limitations led to the recall and accuracy of Q&A were not ideal. Furthermore, due to the lack of cross-scenario generalization ability of the pre-trained (small) model, it is necessary to continuously add corpus for specific scenarios and retrain the model to improve accuracy and recall, resulting in unacceptable implementation costs.
In recent years, with the promotion and popularization of various "data analysis" courses on the market, more and more business personnel have gradually become data-minded and able to analyze business problems from the perspective of data. As the biggest technical dividend at present, the generalization ability of large models across tasks and scenarios has brought new opportunities for us to realize a mature "AI For BI" product.
2. The technology and product development path of AI for BI
For BI, one of the core landing scenarios of AI is "conversational BI"
Its core technology is Text2SQL, which is to convert natural language into specific data query statements. This technology has been studied in academic circles since around 2000, when it was mainly done by people working on databases, and many papers were presented at conferences in the field of databases such as VLDB.
At that time, the technology was not too strong, mainly based on traditional machine learning, which first abstracted the user's query into several categories, defined some templates, and then used supervised learning to make a classification model, and then filled in the templates. Due to the limitations of this technology itself, the accuracy of the product has been very low, far from meeting the requirements of productization. Until around 2016, when the Internet was maturing, it brought some new technologies, including: retrieval, recommendation, deep learning, etc. At this time, some engineers in United States tried to make product innovations, turning data query into a data retrieval problem in a limited space, and then using retrieval technology to solve Text2SQL. At the same time, he also made some amazing products at the time, which attracted a lot of attention in the BI field. However, the technical path at that time was still retrieval in nature, and the main problem of this technical path was that it could not really understand natural language, but divided a sentence into words for matching, and did not really understand the subject-verb-object and definite complement in a sentence.
But this form of product has attracted the attention of some companies with strong academic capabilities, such as MicroSoft, who have begun to use NLP technology based on neural language models to implement "conversational BI". At that time, although neural language models had begun to be used to understand semantics, there was a huge gap in the size of the models compared with the pre-trained models that appeared later and the current large language models. The capabilities of the model also had limitations, so the current situation of the product at that time was low accuracy, high configuration cost, and weak ability to understand intentions, and it was in a state of "artificial intellectual disability". Until the advent of large language models, the improvement of algorithms and the improvement of model size have brought a series of new capabilities such as context learning and thinking chain of large language models, giving us the opportunity to solve some problems that are difficult to solve with old technologies.
To sum up, the integrated development of AI+BI is undoubtedly a new trend at present and in the future. With the continuous advancement of technology, we have reason to believe that the integration of AI+BI will bring more innovation and breakthroughs to enterprises, let us look forward to the era of intelligent data analysis with AI for BI!
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