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Data Warehouse vs Database

You need a Data Warehouse if you plan to implement a Business Intelligence (BI) solution to support your decision-making and improve your business processes. But DWH development is linked with additional expenses. Is it possible to save your money by implementing BI solution without DWH?


Data Warehouse vs Database

Of course, when all you have is a hammer everything looks like a nail. The more detailed picture demonstrates that it's more cost-effective to use the right tool for the job.

A Database is used for storing the data. A Data Warehouse is used for the analysis of data.


You are using a Database (DB) during your daily activities for entering, storing and modification transactional (that is, statistical) business data. 

This can be detailed information about what you sold to whom and when: the Сustomer #1 from Segment #1 bought three units of the SKU#1 on the 10th of March 2020). 

There can be tens of thousands of such entries per day. So you can’t use these data as a basis for decision making without initial preparation. 

To prepare the data for analysis, you have to : 

  • download the data from the DB;
  • upload it to the special software (e.g. Excel, Power BI, Tableau, etc.);
  • make your calculations. The more calculations you need to do, the more time they take, and the higher the chances of making a mistake are. 

Only after this, the data can be used for decision making.

Data Warehouse

A Data Warehouse (DWH), as usual, is a set of databases. A data warehouse stores both statistical and aggregated data. A DWH is created primarily to analyze data for decision making. 

A DWH could be the source of the following aggregated and calculated data:

  • Total Sales (by Location, Category, SKU, Period, and more). For example, all Сustomers from Segment #1 bought 100 000 units of goods from Category #1 brought $1,000,000 in March 2020;
  • Total Sales Growth (by Location, Category, SKU, and more). For example, it increased by 100,000$ or 10% in March 2020 compared with March 2019. 
  • Budget Vs. Actual (by Location, Category, Period, Сustomer Segment, and more). For example, the actual variance is $10,000 or -10%. 
  • and so on.

These data can be used to create models, e.g. to predict demand for goods from Category #1 from Сustomers from the Segment #1.

The data for the analysis are automatically loaded and precalculated in the DWH so you don’t have to spend financial resources on specialists’ salaries to get analysis-ready information. This also negates the possibility of human error.

A data warehouse is different from a database in that it contains aggregated and calculated data for analytical purposes. This is why you can’t do without a DWH if you need analytics for making business decisions.

Using BI without DWH you could face such risks as:

Business data loss. Risk of incorrect analytics due to business data loss (loss of data due to temporary connection glitch, denial of access to the data during report generation, loss of access to the historical data due to its deletion at the source).

Performance issues. Using analytics could be impossible due to the BI-tool freezing, crashing, or becoming unresponsive.

Check out other benefits of a data warehouse.

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I've been leading a department specializing in eLearning applications and Business Intelligence for 14 years.
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