BITM Business Intelligence

List any two scope of business intelligence.

Why data in data warehouse must be non volatile?

How does data transformation improve the quality and usability of data in a warehouse?

What is data analysis?

Why is data considered as important factor for decision making?

What is Data Modelling?

Define the concept of association rule in datamining.

What is Data Visualization?

List any two BI tools.

What is the limitation of OLAP?

Discuss any three importance of business intelligence.

Explain the ETL process.

What is the importance of clustering algorithm? Explain with example.

Discuss any three factor that should be consider to design an effective dashboard for executives.

How does OLAP differ from traditional database queries in terms of analysis capabilities?

How poor visualization choices can mislead users? Explain with an example.

Explain challenges in BI implementation with example.

Illustrate and explain data warehouse architecture.

“A Retail Company wants to analyze customer data to improve sales”, explain with example how classification algorithm can be applied to achieve this objective?

Compare Business Intelligence tools with traditional reporting tools. What makes BI more powerful? Explain.

Analyze the following case carefully and answer the questions that follow:

You’re on Amazon, and it knows precisely what you want before you do. It’s like having a mind reading shopping assistant. Amazon uses data to forecast what products will be super popular. This means they always have the stuff you want ready to ship. Fast delivery, great prices, and a wide selection, that’s the Amazon promise.

When Amazon suggests products you might like, it’s not a coincidence. They’ve studied your past purchases and browsing history. Their recommendations make shopping more accessible and more enjoyable. It’s as if Amazon is saying, “Hey, we found these cool things that you’ll love!”

Questions:

a. How does Amazon use Business Intelligence to predict product demand?

b. What types of data are involved, and how do these recommendations influence buying behavior on Amazon?

c. How do features like personalized suggestions and fast delivery contribute to customer loyalty?

d. What potential challenges or ethical issues (such as data privacy or over personalization) might arise from collecting and analyzing customer data?