This content originally appeared on DEV Community and was authored by chinemerem okpara
Data Analyst vs Data Scientist
(Reference: https://youtu.be/K3pXnbniUcM?t=1175)
When it comes to working with data, the roles of Data Analyst and Data Scientist are often confused—but they serve different purposes.
Data Analyst
Focus: Looks at what happened in the past and present
Methods: Uses tools like Excel, SQL, Power BI, Tableau, Python (sometimes) to clean, query, and visualize data
Output: Reports, dashboards, and visualizations that explain trends, anomalies, and performance
Goal: Provide descriptive and diagnostic insights (e.g., "Sales dropped by 10% last quarter because of fewer repeat customers")
Data Scientist
Focus: Looks at why it happened and what will happen next
Methods: Uses statistics, machine learning, and programming (Python, R, etc.) to build predictive and prescriptive models
Output: Algorithms, forecasts, recommendations, or intelligent systems
Goal: Generate predictive and prescriptive insights (e.g., "Here's a model predicting which customers are most likely to churn and the best strategy to retain them")
Excel vs BI Tools (Tableau)
While Excel has long been the go-to tool for data analysis, modern Business Intelligence (BI) tools like Tableau, Power BI, and Qlik offer significant advantages:
Key advantages of BI tools over Excel:
Big data handling capabilities: BI tools can process millions of rows efficiently, while Excel struggles with datasets beyond 1 million rows, often leading to performance issues and crashes.
Automation of processes: BI tools can automatically refresh data from multiple sources, update dashboards in real-time, and schedule report distribution, eliminating manual copy-paste operations common in Excel.
Greater capacity for large datasets: Modern BI tools can handle terabytes of data through optimized data engines and in-memory processing, far exceeding Excel's limitations.
Enhanced security features: Enterprise-grade authentication, encryption, and audit trails that track who accessed what data and when, providing comprehensive data governance.
Row-level security controls: Ability to restrict data access based on user roles - for example, a sales manager can only see data for their region, while executives see all regions. This granular permission system is impossible to implement effectively in Excel.
Version control management: BI tools maintain a single source of truth with automatic versioning, preventing the common Excel problem of multiple versions floating around with names like "Sales_Report_Final_v2_FINAL.xlsx".
Advanced visuals and interactive dashboards: Dynamic, clickable visualizations with drill-down capabilities, real-time filtering, and professional-grade charts that update automatically as underlying data changes.
Top 3 Visualization Tools Comparison
When comparing the leading BI tools—Power BI, Qlik, and Tableau—Tableau consistently comes out on top thanks to:
- High performance with large datasets
- Quick and interactive visualization capabilities
- User-friendly interface and learning curve
- Huge community support and resources
This content originally appeared on DEV Community and was authored by chinemerem okpara
chinemerem okpara | Sciencx (2025-09-17T21:06:05+00:00) Business Intelligence Fundamentals Part 1: Roles and Tools. Retrieved from https://www.scien.cx/2025/09/17/business-intelligence-fundamentals-part-1-roles-and-tools/
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