Advantages And Differences Between Using a Data Warehouse and Direct Data Analysis

How a company analyzes data can significantly impact its success in today's data-saturated world. Direct analysis of an organization's operational database and analysis using a data warehouse are the two main approaches to data analysis. Let's take a look at their advantages and differences.

Direct Analysis of An Organization's Database

1.    System slowdown: when performing analysis directly on the central database, all users may experience system slowdown.

2.    Current data only: Only current data can be queried when using this method. Accessing historical data requires significant resources, which may result in poor quality of service.

3.    Complex data integration: Combining data from different sources can be complex, time-consuming and error-prone.

4.    Effective for operational reporting: this approach is well suited for operational reports that are closely related to the day-to-day operations of an organization and require access to the most up-to-date data. It is ideal for monitoring activities in real time and making changes quickly.

Analyzing With a Data Warehouse

1.    Increased efficiency: by separating day-to-day operational activities from analytical activities, a data warehouse increases the efficiency of analytical work.

2.    Access to historical data: provides insights into past data, leading to a deeper and more complete understanding of circumstances.

3.    Simplified data integration: Improves data consistency and completeness by simplifying combining data from different sources.

4.    Consistency and data quality: An effective data management system ensures high quality and consistency.

5.    Ideal for strategic research: if you need historical data and consolidation of multiple data sources to explore strategy and trends, a data warehouse is a great solution. It facilitates in-depth analysis and allows you to create comprehensive reports that support long-term decision-making.

ETL Process

The ETL (Extract, Transform, Load) process transfers data from the operational database and other sources to the data warehouse. This process is necessary to ensure that data reaches its destination accurately and timely.

1.    Export: Collecting information from various sources such as files, ERP, CRM systems, enterprise databases, etc.

2.    Transformation: Ensures consistency and usability of data by cleaning, formatting, and modifying it to meet the organization's specific requirements.

3.    Loading: moving transformed data into a data warehouse for analysis and reporting.

Using A Data Warehouse as A Replacement

If money and time are limited, consider using a data mart instead of a full-fledged data warehouse. A data mart is a subset of a data warehouse that focuses on a specific business area.

1.    Time and cost savings: compared to an entire data warehouse, a data mart can be implemented faster and with less investment.

2.    Specific focus: data marts can be very effective for certain types of analysis as they focus on a particular business function.

3.    Phased deployment: you can start with one or more data warehouses and expand as needed.

Summary

Using a data warehouse to analyze data increases a company's productivity and ensures that decisions are based on accurate and reliable information. Any company that wants to grow and remain competitive in today's marketplace should do so. Using data warehouses can be a viable option in the short to medium term when resources are limited. Data warehouses are most valuable for strategic and long-term research, although direct analysis is well suited for operational reporting and quick decision-making.