Framework for Business Analytics | Dominic Ligot

Summary

This lesson introduces the framework for executing business analytics, focusing on how raw data becomes business value. It covers the process of data extraction, warehousing, and analysis, emphasizing the ETL (Extract, Transform, Load) process. The three key types of business analytics—descriptive, predictive, and prescriptive—are explained using the example of the Waze app, demonstrating how data can be used to understand current situations, predict outcomes, and provide actionable recommendations. The overall goal is to transform raw data into insights that drive business decisions and actions.

Takeaways

• 😀 Raw data is similar to coffee beans – it needs to go through a transformation process to create value.
• 😀 Data extraction is the first step in turning raw data into actionable business insights.
• 😀 The data warehouse stores and organizes data, cleaning and curating it for analysis.
• 😀 The ETL (Extract, Transform, Load) process is essential for moving data from source systems into a data warehouse and into analytical tools.
• 😀 Data is stored in tables in the warehouse, such as sales, pricing, and cost data, to understand business relationships.
• 😀 The data warehouse joins tables automatically, preparing them for analysis without manual intervention.
• 😀 Descriptive analytics helps businesses understand what is currently happening by profiling and benchmarking data.
• 😀 Predictive analytics uses historical and current data to forecast future trends, like predicting travel time in the Waze app.
• 😀 Prescriptive analytics provides recommendations and evaluates the costs and benefits of different decisions, as seen in the Waze app’s route suggestions.
• 😀 Waze is a practical example of all three types of analytics: descriptive, predictive, and prescriptive.
• 😀 The process of turning raw data into business value involves extraction, warehousing, analysis, and the application of one of the three analytics types: descriptive, predictive, or prescriptive.

Q & A

What is the first step in turning raw data into business value?

The first step is data extraction, where data is gathered from various sources, such as source systems, transactions, and documents.

How is the process of extracting and moving data into a data warehouse referred to?

This process is known as ETL, which stands for Extract, Transform, and Load.

Why is it important to store data in a data warehouse?

A data warehouse centralizes and organizes data, ensuring it is clean and ready for analysis. It is crucial for preparing data for business analytics.

What does data warehousing eliminate for business analysts?

Data warehousing eliminates the need for manual joining of data, which can be time-consuming and error-prone. It automates the process of joining tables to prepare the data for analysis.

What are the three main types of business analytics?

The three main types of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics.

Can you explain descriptive analytics with an example?

Descriptive analytics involves analyzing historical data to provide insights or benchmarks. An example is using the Waze app to see current traffic conditions without making predictions.

What is the role of predictive analytics?

Predictive analytics involves using historical data to predict future outcomes. For example, Waze predicts your travel time based on historical and current traffic data.

How does prescriptive analytics work?

Prescriptive analytics provides recommendations based on data and simulation. Waze uses prescriptive analytics to suggest the best routes and their trade-offs, such as the number of accidents or police presence on the road.

What happens after data is stored in a data warehouse?

Once the data is stored in the data warehouse, it is cleaned, curated, and joined in a normalized form, making it ready for analysis by business analysts.

What is the analogy used in the script to explain how data becomes valuable?

The script uses the analogy of turning coffee beans into a beverage. Just like raw coffee beans need to be processed, data needs to be extracted, stored, and analyzed to generate business value.