With the rapid development of artificialintelligence (AI) technology, data analytics is undergoing a revolutionary transformation. While you’re pondering, " Will generative AI tools like ChatGPT and Copilot replace me? ", those who already use AI tools effectively are thriving and shining. In February 2025, LITEON+ hosted the Unicorn University and invited big data analytics expert and corporate trainer Jack Peng to reveal the core processes of data analysis. He combined strategies for applying AI tools to enhance efficiency and decision-making,from inquiry to action.
The speaker explained the six key steps outlined in the Google Data Analytics Certificate course: ASK →PREPARE → PROCESS → ANALYZE → SHARE → ACTAt each stage, leveraging AI tools can yield greater results with less effort.
Before collecting data, designing the right questions is critical. Asking precise questions can significantly increase theefficiency of data analysis. When faced with complex data sets and unsure where to start, AI can assist in identifying potential analytical directions. However, the speaker also advised caution about uploading any data to AI platforms. Be data security-conscious to avoid leaking personal or corporate information.
**AI Tips: Help interpret statistical charts quickly and suggest analysis directions.
Preparing and cleaning data has long been a pain point in data analytics—tedious and dull. AI tools now make it easier than ever to identify and clean up "dirty data," simplifying these tasks significantly. For instance, when dealing with unstructured data like faxed documents or handwritten information, AI can convert them into digital formats, unifying the dataset preparation process. This saves manpower and allows human resources to focus on more critical tasks.
**AI Tips: Assist in identifying and cleaning dirty data; digitize image-based data.
Once the data is structured, AI can be further utilized to analyze and interpret the results. Traditionally, interpreting statistical charts required extensive professional knowledge. Now, AI provides intuitive explanations, making it easier for everyone. Moreover, AI can quickly generate multidimensional insights, leading to more effective identification of the "why" within the data.
The speaker shared a pro tip for issuing commands: “Explain it in a way a five-year-old would understand.” This helps make complex jargon more accessible. Additionally, AI can generate Excel files and write VBA code, simplifying data processing and reducing labor-intensive tasks. This greatly enhances the efficiency and accuracy of data analysis.
**AI Tips: Assist in statistical analysis, support diagnostic analytics, and automate Excel generation.
The final step in data analytics is to materialize the data's value to guide decision-makers’ actions. While AI's capabilities in data visualization are still limited, it can suggest design styles, color palettes, and main visual themes, providing "insight candidates." Of course, the final analysis report still requires expert curation and refinement.
Since cold, hard data can hinder effective communication, AI can help present it in a story-like format, breaking down professional barriers for better cross-departmental understanding andconsensus.
**AI Tips: Use AI as your visual design advisor, assist in report generation, and transform data into stories.
In the digital age, leveraging AI tools can break down the barriers between professions, enabling swift acquisition of knowledge across various fields. Instead of worrying about whether AI will replace you, learn to harness its power and redefine your value. It’s vital to remember that AI's recommendations still require human judgment and evaluation. Real value lies in combining AI's efficiency with human insight to uncovertreasures in the vast sea of data.
