Richard Lim

Customer analytics focuses on identifying common purchase patterns and optimizing the customer journey using historical data. For instance, by tracking each step of an online customer's e-commerce purchase, we can pinpoint where churn rates are highest or conversion rates are most successful. These insights make it easier to develop strategies to improve key performance indicators (KPIs). Today, it is common to personalize customer personas, predict preferences & behaviors, and recommend high-demand products. This approach is becoming increasingly sophisticated and accurate with the integration of big data analytics and artificial intelligence.

With the radical advancements in AI, even non-experts can now extract, explore, clean, preprocess, and visualize data by simply feeding it into AI systems. However, data analysis goes beyond the technical skills. After processing the data, it's essential to craft a clear, compelling storyline and present the core message to stakeholders, helping them understand the insights the data reveals. Specialized knowledge and expertise are what make data analysts add value to their work. A deep understanding of industry-specific concerns and the ability to forecast future trends through domain experience are critical for creating impactful and actionable analytical outputs.

Personalized visualizations, (individual profile dashboards) are a powerful way to provide insights tailored to each user. Since users have unique needs and purposes, personalized visualizations can reflect their preferences, leading to engagement and improving user retention. However, personalized visualizations also come with risks, such as potential privacy breaches or data security issues. To mitigate these risks, consider implementing the following measures: - Personalized Data Permissions (PDP) - Data Policy Notifications - Guidance and Tutorials - Administrative Ethical Responsibility These steps can help balance the benefits of personalized visualizations between privacy and customization.

Most ETL pipelines typically consist of three main stages or layers: Raw, Dev, and Prod. However, the structure can vary depending on the specific requirements and available resources. To build a holistic and stable ETL pipeline, it’s essential to understand the characteristics of each stage and design an infrastructure accordingly. For instance: - Batch pipelines and streaming pipelines should be designed differently to meet their unique requirements and objectives. - Table formats and JSON/XML formats need to be handled differently based on data volume and refresh frequency requirements. Using a clear naming convention can help distinguish between various data types and sources in data lakes. For example: Raw | CRM | User_Activity.

I recommend using RAG (Retrieval-Augmented Generation), which allows AI to search and reference domain-specific knowledge from pre-built databases. Since Large Language Models (LLMs) are mainly for general-purpose tasks, it is crucial to improving their accuracy and relevance for specialized tasks by integrating domain-specific knowledge as part of their training data. RAG is a recent concept to enhance LLM performance and reducing hallucination, one of the most critical challenges in domain-specific AI models. RAG operates in four main stages: indexing, retrieval, augmentation, and generation. Among these, the indexing stage is the most important for ensuring quality, and it should be carefully assessed and validated by domain experts.

To turn analytical output into engaging and relatable stories, keep the following points in mind: Structure the Storyline: People are more likely to engage when the story starts with a compelling hook that relates to their lives. Deliver a Single, Clear Message with Visuals: Audiences prefer insights delivered one at a time. Pairing a clear message with a single, focused visualization makes it more memorable and impactful. Ask Thought-Provoking Questions: Questions that brings curiosity can encourage people into deeper engagement. These strategies will help your data insights inspire action and drive results toward achieving the desired goals.