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Performing Smart Analytics and AI on GCP: What Does a Data Analyst Do to Ensure Quality of Data?

By: Author Alex Lim

Posted on

Categories Performing Smart Analytics and AI on GCP

Discover how data analysts ensure data quality through data cleaning, a critical process for accurate analysis and decision-making. Learn its importance and techniques.

Question

What does a data analyst do to ensure quality of data?

A. Data maintenance
B. Data cleaning
C. Data sensing
D. A data flip

Answer

B. Data cleaning

Explanation

Data cleaning, also referred to as data cleansing or scrubbing, is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure their quality and reliability. This step is essential for improving data accuracy, consistency, and usability, which are critical for effective analysis and decision-making.

Why Data Cleaning is Essential

  • Accuracy: Ensures that the data used for analysis is free from errors like duplicates, missing values, or structural inconsistencies.
  • Consistency: Standardizes formats (e.g., date formats) and resolves conflicts in data representation across datasets.
  • Improved Decision-Making: Clean data provides reliable insights, enabling better strategic decisions.
  • Enhanced Machine Learning Models: High-quality data improves the performance and generalization of AI/ML models.

Common Techniques in Data Cleaning

  • Removing Duplicates: Identifying and eliminating redundant entries.
  • Handling Missing Data: Filling in gaps using statistical methods or domain knowledge.
  • Correcting Structural Errors: Standardizing inconsistent formats like dates or units.
  • Addressing Outliers: Evaluating extreme values to determine their validity.

Data cleaning is a foundational task for analysts and data scientists alike, ensuring that downstream processes yield meaningful and actionable results.

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