Data Acquisition and Preparation
Posted: Thu May 29, 2025 4:31 am
Receive Request: A business user or department has a question, a problem, or needs specific information.
Clarify Needs: The BI Analyst engages with stakeholders to fully understand the business context, the desired outcome, what decisions will be made based on the analysis, and the specific metrics of interest. This phase is critical to avoid an "answer looking for a question."
Identify Data Sources (The "What" and "Where"):
Based on the objective, the analyst identifies the relevant data sources (databases, CRM, ERP, web analytics, spreadsheets, etc.).
Assess data availability, accessibility, and quality.
Extraction: Use SQL, ETL tools, or scripts to pull data from the identified sources.
Transformation & Cleaning:
Handle missing values (impute, remove).
Correct errors and inconsistencies.
Standardize formats (dates, categories).
Remove duplicates.
Join data from multiple tables or sources.
Loading: Load the prepared data into a staging whatsapp number list area, a data mart, or directly into a BI tool.
Exploratory Data Analysis (EDA):
Perform initial investigations to understand the data's structure, distributions, and basic patterns.
Use descriptive statistics and simple visualizations to get a feel for the data.
Formulate hypotheses.
In-Depth Analysis (Answering the "Why"):
Apply appropriate analytical techniques (e.g., trend analysis, cohort analysis, segmentation, correlation analysis, basic statistical modeling).
Drill down into data to find root causes.
Clarify Needs: The BI Analyst engages with stakeholders to fully understand the business context, the desired outcome, what decisions will be made based on the analysis, and the specific metrics of interest. This phase is critical to avoid an "answer looking for a question."
Identify Data Sources (The "What" and "Where"):
Based on the objective, the analyst identifies the relevant data sources (databases, CRM, ERP, web analytics, spreadsheets, etc.).
Assess data availability, accessibility, and quality.
Extraction: Use SQL, ETL tools, or scripts to pull data from the identified sources.
Transformation & Cleaning:
Handle missing values (impute, remove).
Correct errors and inconsistencies.
Standardize formats (dates, categories).
Remove duplicates.
Join data from multiple tables or sources.
Loading: Load the prepared data into a staging whatsapp number list area, a data mart, or directly into a BI tool.
Exploratory Data Analysis (EDA):
Perform initial investigations to understand the data's structure, distributions, and basic patterns.
Use descriptive statistics and simple visualizations to get a feel for the data.
Formulate hypotheses.
In-Depth Analysis (Answering the "Why"):
Apply appropriate analytical techniques (e.g., trend analysis, cohort analysis, segmentation, correlation analysis, basic statistical modeling).
Drill down into data to find root causes.