Data Warehousing Process - A Beginner Guide
By:
Jewi Learn
On
14/02/2024Reading time:
12 min
Summary:
Data warehousing helps organization in several ways. One key aspect is that they could serve as the foundation for business intelligence and analytics initiatives. They provide analysts and business users with easy access to structured data, analytical tools, and reporting capabilities, helping them to extract actionable insights and drive tangible decision-making.
Imagine you work for a retail company with stores located across multiple regions. As a data analyst, your role is to analyze sales data to identify trends, optimize inventory levels, and improve overall business performance.
Data Collection
The first step is to collect sales data from various sources, including point-of-sale systems, online transactions, and customer loyalty programs. This data typically includes information such as product sales, customer demographics, transaction dates, and store locations. Once that is done, the next step is
Data Integration
to integrate sales data into the data warehouse from different sources using Extract, Transform, Load processes also know as ETL. This ensures data is standardized, cleansed, and stored in a centralized repository for analysis. Next, you might want to organize and structure data into tables to minimize redundancy. This brings us to
Dimensional Modeling
Using dimensional modeling techniques, the sales data is organized into tables such as Product, Time, Store, and Customer etc. These tables serve as the building blocks for analysis and provide context for the sales metrics stored in the fact table. The fact table contains quantitative measures or metrics related to sales, such as sales revenue, units sold, and average transaction value. It is linked to the dimensional tables through foreign key relationships, enabling analysts to perform multi-dimensional analysis.
Analysis and Reporting
With the sales data stored in the data warehouse, analysts can now perform a variety of analyses to gain insights into sales performance. For example, an analyst may want to conduct a
Trend Analysis to Identify seasonal trends, sales patterns, and fluctuations in demand over time. Geographic Analysis could also be conducted to compare sales performance across different regions, cities, or store locations.
Visualization
To communicate insights effectively, analysts use data visualization tools such as Tableau, Power BI, or Looker to create interactive dashboards and reports. Data visualization is the graphical representation of data. Visualizations such as bar charts, line graphs, and heat maps help stakeholders understand trends and make data-driven decisions.
Insights and Recommendations
Based on the analysis, analysts provide actionable recommendations to stakeholders and decision-makers. For example:
Adjust inventory levels to meet demand fluctuations and minimize stockouts.
Optimize product assortments and promotions based on customer preferences and buying patterns.
Identify opportunities to improve store layout, pricing strategies, and marketing campaigns to drive sales growth.
The thing is this, by using a data warehouse for sales analysis, the retail company can gain valuable insights into sales performance, customer behavior, and market trends. These insights help an organization in making a strategic decision, and ultimately enhance the company's competitive advantage in the retail industry.
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