Generative AI revolutionizes data analytics
Imagine a scenario where you can talk to a computer as if you were talking to a human. In this scenario, you don't need to learn complex technologies, and you can organize data, analyze complex data sets, and generate reports through natural language. A few years ago, this might have been the stuff of science fiction, but today, with groundbreaking advances in generative artificial intelligence (AI), it's not far off.
AI-related technologies have been developing continuously, and on November 30, 2022, OpenAI publicly released the chat robot ChatGPT, bringing AI to the public's attention again. The "smartness" and "intelligence" embodied by ChatGPT quickly made it the fastest growing product ever. The interactive experience with ChatGPT is reminiscent of the situation when users first interacted with Google in the late 1990s or when the iPhone first came out, and it also gave everyone a glimpse of the possibilities of future technology.
At the end of June this year, Snowflake and Databricks, the world's two major data platform manufacturers, will hold their respective annual conferences. The themes of the conferences are closely related to AI and large models. Generative AI has once again become the topic center of the big data circle. As Nvidia CEO Huang Renxun mentioned in his speech, ChatGPT will become the "iPhone moment" of artificial intelligence.
The popularity of artificial intelligence is not groundless. In fact, generative AI has crossed a major chasm, moving from a theoretical concept to a practical tool with wide-ranging applications. The maturity of large models like ChatGPT has brought generative AI mainstream and brought new possibilities to various fields including data analysis.
In this article, we'll take a deep dive into the transformative impact of generative AI on data analytics and human-computer interaction, as well as the challenges it presents, and look ahead to the technology's exciting future.
The Disruptive Impact of Generative AI on Human-Computer Interaction
The emergence of generative AI has brought about a major revolution in human-computer interaction. From the days of Command Line Interface (CLI) and Graphical User Interface (GUI), we are moving towards a future dominated by Language User Interface (LUI). This transformation allows everyone to use the power of AI to conduct data analysis with a low threshold.
In the CLI era, users interacted with computers by typing precise commands into the terminal. A GUI, on the other hand, introduces a more intuitive approach where users can use a mouse or touch to interact with graphical elements such as buttons, menus, and icons. However, both methods require some technical knowledge and familiarity with the system.
In contrast, LUI allows users to interact with computers in a more intuitive and natural way -- through language. With simple language instructions, a user can ask a computer to perform a task, and a generative AI model understands the user's request and performs the task.
The impact of generative AI is that it changes the way we interact with computers, further lowering the threshold for people to use computers/AI to improve work efficiency. This change is driving a new model of collaboration, allowing us to work alongside AI models as "twin programmers" or "twin artists." This trend augments human capabilities, not replaces human jobs.
Overall, generative AI has disruptive impacts on data analysis and human-computer interaction. As technology continues to advance, we can expect smarter, more efficient ways of working, and more natural and intuitive interactions with computers.
From query to conversational AI changes data analysis
The advent of generative AI is changing not only the way we analyze data, but also the way we interact with it. The shift towards Language User Interface (LUI) has made data analysis more intuitive and accessible. Here are some use cases for data analysis driven by AI:
- query and retrieval
Let's take the example of a business analyst who wants to understand sales trends. In the past, they needed to write complex SQL queries or use specialized data analysis tools. With the advent of LUI, they can simply ask the AI system in natural language, for example, "What were the sales trends for the last quarter?" or "Show me the top performing products for the last month." The AI system then translates this request into code, performs analysis, and presents the results in a user-friendly format.
- Data cleaning and preparation
Another example is in data cleaning and preparation. Data cleaning is generally considered a tedious and time-consuming process. With generative AI, users can tell the artificial intelligence system to clean the data through simple instructions. For example, a user can say "delete any rows that contain missing values" or "replace all instances of 'N/A' with zeros" and the AI system will perform those tasks.
- Analysis and Visualization
This new form of interaction extends to other areas such as data visualization and report generation. For example, a user can ask the AI to "create a bar chart showing sales by region" or "generate a report on customer demographics," and the AI system will fulfill those requests.
- forecasting and optimization
Plus, generative AI makes it easier to search voice data, share insights, and leverage them to drive business value. For example, it can be seen how many complaint calls the call center receives and whether empathetic communication from customer service representatives to customers leads to an increase in sales.
- Speech Recognition and Natural Language Processing
Using natural language processing (NLP), generative AI can also understand unstructured data such as notes, for example by selecting qualitative assessments to analyze the likelihood that an insured driver is at fault; Missing information in structured data.
This transformation will bring about what everyone calls "data democratization" : allowing more people to access data sets, analyze data, and truly realize the beautiful vision of everyone using data .
However, this new way of interacting also brings certain risks. As AI systems become more integrated into our everyday tasks, the potential for misuse or error increases. Therefore, it is critical to address and mitigate these risks through strong security measures, careful system design, and user education.
Conclusion: The Dawn of a New Era in Data Analytics
In the future, generative AI isn't just a new tool in our arsenal; it's a game-changer that will revolutionize data analysis and human-computer interaction. By automating complex tasks and making data analysis more accessible, generative AI will dramatically increase efficiency and productivity. Again, like any powerful technology, it presents challenges that need to be addressed, including data security, bias, and accuracy issues.
As we stand on the brink of this new era, there are some questions we cannot avoid: How can we responsibly harness the power of generative AI? How will we ensure that it benefits all of humanity, not just a few? The above questions will profoundly affect our future as we explore uncharted territory in this exciting new field.
We can see that the potential of generative AI will help companies lower the threshold for using data, promote the democratization of data, and unleash digital intelligence productivity. On July 14th, we will hold the Kyligence 2023 User Conference with the theme of "Unleashing Digital Intelligence Productivity" in Shanghai. Friends who are interested in this are welcome to click the link to sign up . We sincerely invite you to explore the possibility of Data + AI together, meet the challenges together, and reshape the future of data analysis.
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.