How to Build a Data Warehouse from Scratch

Data Warehouse

Thanks to data warehouses the most essential information about the organization and its activities can be stored and easily accessed in one place. It is of paramount importance for analysts and their work. Their main purpose is to make important decisions based on the data gathered from the source for further business promotion. 

Now, it is quite obvious that a data warehouse is entirely beneficial for any company, but then the question arises of how to build it. Since the market is evolving extremely fast it is important to keep abreast of the times and make use of all available innovations and technologies. Below you will find an inventory of stages a person must go through to build a warehouse.

Table of Contents

1. What are the main objectives of your business?

We should understand that companies can be at different stages of their development and have distinct objectives for the nearest future. Due to this, the first thing that should be done is communicate with the business owner, managers, and administrators. Based on information received from them a fact table is made.

 For example, if a company wants to increase sales, it is crucial to analyze the current sales modes and their efficiency for taking further well-informed decisions. All in all, the first thing you should do is to define your purposes and vulnerabilities that can be improved. If you are working or running a banking establishment and can’t do it by yourself, visit to get it done by the professionals.

2. Data analysis

Now you are expected to get as much information as possible. The best way to do it is to communicate with the company’s leaders and ask them questions. The more you ask, the more answers you get. It is crucial to detect all data sources and analyze them and their importance. It is a good idea to start with the information used by business owners for taking important operational decisions. You can investigate the available accounting documents and reports. It is essential to understand that there are no useless sources of data. If it exists, you should find out why.

During this stage, you also have to opt for the most appropriate physical environment, ETL, and modeling tools for enabling the development of data warehouses.

3. A data model construction

This step is about the development of software architecture (visit to get more information about it), which determines the success of the end product. Now you are expected to decide what business processes are to be correlated to work out a conceptual model of the information. You have to outline the themes that will be represented in the fact tables. It is essential to understand all critical performance determinants in all business operations and in which format they will be represented. If it is about money, for example, opt for a currency and its conversion rate. It always works like this.

After it, you have to establish the relations between all the elements and map information into the data warehouse. If you want to add a new fact, then all the dimensions must be correlated. In case you detect incomplete information, make corrections. During this phase, your accuracy can save you a great deal of time lately.

4. The building process itself

This step is a lengthy and responsible one. It will take you a minimum of 2 months to create a DW. Now you have to customize the platform and establish appropriate security measures. What’s more, it is crucial to pay great attention to the testing of the final product performance. It is done to analyze its speed and efficiency without quality losses.

5. Product launch

This step is the most pleasant one because it gives the opportunity to see the results of the previous stages. As a rule, it takes from 2 to 4 days to present a data warehouse. It is essential to analyze the quality of the end product and its performance. If any vulnerabilities are detected, they must be quickly illuminated. This step comes to the end only when you and the customers are fully satisfied with the developed data warehouse.