In today’s world, filled with data and information, a company’s analysis of its data can significantly impact its success. There are two main methods for performing data analysis: direct analysis from the company’s (operational) database and the other using a data warehouse. Let’s look at the differences and benefits of each.
Direct Analysis from the Company’s Database
- System Slowdown: Performing analysis directly on the central database can cause the system to slow down, affecting all users.
- Current Data Only: This method is only effective for viewing recent data. Accessing historical information requires significant resources, which could degrade service.
- Difficulty in Data Integration: Combining data from different sources can be complicated, slow, and prone to errors.
- Effective for Operational Reports: This method is effective for operational reports that require access to the most current data and are directly related to the business’s daily operations. It is ideal for monitoring activities in real time and making immediate adjustments.
Analysis from a Data Warehouse
- Better Performance: A data warehouse improves analysis performance by separating daily operational tasks from analytical tasks.
- Access to Historical Data: Allows viewing of older data, providing a more complete and detailed view of the situation.
- Simplified Data Integration: It makes combining data from different sources easier, improving consistency and integrity.
- Data Quality and Consistency: With a good data management system, data is ensured to be of high quality and consistent.
- Ideal for Strategic Analysis: A data warehouse is suitable for strategic and trend analysis that requires historical data and integration of multiple data sources. It allows for in-depth analysis and generates detailed reports that help in long-term decision-making.
The ETL Process
To transfer data from the operational database and other sources to the data warehouse, the ETL (Extraction, Transformation, and Load) process is used. This process is essential to ensure that data reaches the final destination accurately and timely:
- Extraction: Capturing data from various sources, such as operational databases, files, ERP systems, CRM, etc.
- Transformation: Cleaning, formatting, and adjusting the data according to the specific needs of the business, ensuring that the data is consistent and usable.
- Load: Transferring the transformed data to the data warehouse, where it will be available for analysis and report generation.
Implementing a Data mart as an Alternative
If cost and time are limited, consider implementing a data mart instead of an entire data warehouse. A datamart is a subset of a data warehouse and focuses on a specific business area.
- Reduced Costs and Time: Datamarts require less investment and can be implemented more quickly than a complete data warehouse.
- Specific Focus: By focusing on a particular business function, datamarts can be very efficient for certain types of analysis.
- Gradual Implementation: You can start with one or several datamarts and eventually integrate them into a complete data warehouse if necessary.
Conclusion
Choosing to analyze data using a data warehouse improves the company’s efficiency and ensures that decisions are based on accurate and reliable information. This is essential for any company that wants to grow and stay competitive in the current market. If resources are limited, considering the implementation of data marts can be an effective short- and medium-term solution. In summary, while direct analysis suits operational reports and quick decisions, using a data warehouse is more beneficial for strategic and long-term study.