Data Management & Preparation Flashcards

What is the primary goal of data management in analytics?

Answer: To ensure data accuracy, accessibility, and usability

The primary goal of data management in analytics is to establish processes and policies that ensure data is reliable, readily available, and fit for its intended analytical purpose. This involves maintaining data quality, security, and integrity throughout its lifecycle. Effective data management is foundational for generating trustworthy insights and making informed decisions.

Which process involves removing duplicate and inconsistent data to improve quality?

Answer: Data cleaning

Data cleaning, also known as data scrubbing, is the process of detecting and correcting or removing corrupt, inaccurate, or irrelevant records from a dataset. This includes handling missing values, standardizing formats, and eliminating duplicates or inconsistencies. Data cleaning is crucial for improving data quality and ensuring the reliability of analytical results.

Which file format is commonly used for structured data storage in analytics?

Answer: CSV

CSV (Comma Separated Values) is a widely used file format for storing tabular data, where each line represents a data record and fields are separated by commas. It is a simple, plain-text format that is easily readable by humans and machines, making it highly compatible for structured data exchange and storage in analytics applications.

What is the primary purpose of data integration?

Answer: To combine data from multiple sources

Data integration is the process of combining data residing in different sources and providing users with a unified view of them. This is essential in analytics as data often originates from various systems, such as databases, spreadsheets, and APIs. Integrating data creates a comprehensive dataset for more holistic and accurate analysis.

Which database type is best suited for handling large-scale structured data?

Answer: Relational database

Relational databases are highly effective for managing large-scale structured data due to their ability to organize data into tables with predefined schemas and enforce relationships between them. They ensure data integrity, support complex queries using SQL, and are optimized for transactional processing and consistent data storage, making them ideal for structured data.

Why is data normalization important in data preparation?

Answer: To reduce redundancy and improve consistency

Data normalization is a process in database design that organizes tables to minimize data redundancy and improve data integrity. It involves breaking down large tables into smaller, related tables and defining relationships between them. This ensures data consistency, reduces storage space, and makes the database more efficient and easier to maintain.