ribe the concept of data warehousing and its relationship to data modeling, including the use of data marts and data lakes.

Q: What is a snowflake schema and how does it differ from a star schema in data warehousing

A: A snowflake schema and a star schema are both data modeling techniques, These schemas have wide use…

Q: Explain the following based on CRM – Customer Relationship Management The customer is always right…

A: Hello thank you for the question. As per guidelines, we would provide three sub-parts at a time.…

Q: Discuss how analytical CRM helps in customer segmentation and predictive analytics.

A: Customer relationship management (CRM) uses data analysis and statistical techniques to provide…

Q: Explore the role of predictive analytics in CRM for anticipating customer behaviors and trends.

A: Predictive analytics in CRM is a powerful tool that leverages data, statistical algorithms, and…

Q: Discuss the role of CRM in creating a 360-degree view of customer interactions and data.

A: A 360-degree view of customer interactions and data is a comprehensive and integrated perspective…

Q: Explain the concept of data warehousing appliances and their role in enterprise data management.

A: DATA WAREHOUSING :- The collecting, storage, and organising of enormous volumes of data from…

Q: Explain why Data Warehouse system is importance to the construction of an entire  marketing…

A: At its core, marketing intelligence is the ability to answer complex questions about your customer…

Q: Discuss the concept of data granularity and its significance in data warehousing.

A: Data Warehousing is derived from two terms which are data and warehousing. Data means raw data which…

Q: The researcher, before using secondary data, must see that they have certain characteristics.…

A: Researchers obtain two types of data: primary and secondary. The data that they obtain from their…

Q: What precisely is the dispersed processing model and how does SETI utilize it?

A: The dispersed processing model is also known as distributed computing.It is a technique that…

Q: Identify and explain 3 advantages and 3 disadvantages of business analytics.

A: Introduction Business analytics is the study of skills, innovations, and techniques for the…

Q: Define primary and secondary data and explain the purpose of their usage. Give examples for both.

A: Data collection plays a major role in every segment. It is hard to explore something which carries a…

Q: Define the term Analytics and explain its types providing some examples of each.

A: Analytics is the systematic computational analysis of data or statistics. It involves the use of…

Q: Discuss the significance of customer data in the context of CRM and its role in enhancing customer…

A: Customer Relationship Management (CRM) has become a pivotal strategy for businesses aiming to thrive…

Q: OLAP use in descriptive analytics must be explained.

A: OLAP, or Online Analytical Processing, is a technology that allows users to analyze large amounts of…

Q: What are some of the key distinctions between a descriptive model and a prescriptive model?

A: Marketing research give the advantage to business to know more about the market or industry in which…

Q: Explain the concept of CRM analytics and its role in extracting actionable insights from customer…

A: Customer Relationship Management (CRM) analytics is a powerful tool that businesses use to gain…

Q: Discuss the role of data analytics in CRM and how it helps in understanding customer behavior.

A: It is important for organization to identify the preferences of the customers and align their…

Q: Explain the concept of data warehousing and its role in consolidating data from multiple sources for…

A: The process of creating and using a data warehouse is known as data warehousing. A data warehouse is…

Q: interpret the output of a two way ANOVA

A: Stage 1: Determine whether the principle impacts and connection impact are measurably huge  To…

Normalization

Normalization is the process of eliminating redundancy in a database by organizing the data into several smaller tables. The large tables are normalized (converted) into smaller tables by using normalization rules, so that a database administrator can easily handle them. It also eliminates insertion, deletion, and update anomalies.