Hello, and welcome to the third post of the BigQuery Bootcamp series. In this post, we will deeply explore the Relationship Between AI and Data Analysis and understand the impact AI has on data analysis. Data analysis and Artificial Intelligence (AI) form a complementary relationship in today's business and technological environments, and the convergence of these two fields has become a key element in promoting data-driven decision-making and innovation. We will examine how to effectively integrate AI with data analysis using BigQuery.

The Complementarity of AI and Data Analysis

Data analysis aims to derive meaningful insights through a series of processes that involve collecting, cleaning, exploring, analyzing, and visualizing data. AI automates these data analysis processes, adds depth to the analysis, and enables more sophisticated predictions and decision-making. The complementarity between AI and data analysis is evident in the following aspects:

1. Automation and Enhanced Efficiency

AI significantly enhances the efficiency of data analysis by automating various stages. For instance, AI-based tools can automatically handle repetitive tasks in data collection and cleaning, saving time and resources. Additionally, AI algorithms can quickly process and analyze large datasets, reducing the burden on human analysts.

2. Predictive and Decision-Making Support

AI serves as a powerful tool for predicting future outcomes and supporting decision-making based on historical data. Machine learning models learn patterns from data to solve various business problems such as sales forecasting, customer churn prediction, and inventory management. These predictive capabilities help executives and decision-makers make more strategic and evidence-based decisions.

3. In-Depth Analysis and Insight Generation

AI excels at identifying complex data patterns and correlations that may be difficult for humans to detect. Advanced AI technologies like deep learning uncover subtle patterns, providing profound insights that drive innovation in areas such as product development, marketing strategies, and customer service enhancements.

Integrating BigQuery with AI

BigQuery, Google's powerful data warehouse, efficiently supports large-scale data analysis and AI modeling. Integrating BigQuery with AI enhances every stage of data analysis, enabling more precise and meaningful results. Here are the primary methods to leverage AI within BigQuery:

1. BigQuery ML (BQML)

BigQuery ML allows you to create and train machine learning models directly within BigQuery using SQL. This feature enables data scientists to develop models without the need for a separate machine learning platform, bridging the gap between data analysis and machine learning.

Key Features of BQML

• Model Creation: Use the CREATE MODEL statement to build various types of models, including regression, classification, and clustering.
• Model Training: Efficiently train models using large datasets.
• Prediction: Perform real-time predictions using the ML.PREDICT function.
• Model Evaluation: Analyze model performance using various evaluation metrics.

Practical Example: Creating a Sales Prediction Model with BQML

Let’s create a sales prediction model using the sales_data.csv file provided in the previous post. This example demonstrates how to correctly use the ML.PREDICT function by specifying input_label_cols in the OPTIONS.