8 steps to build a data warehouse

1. Define your business requirements

As a data warehouse spans all areas of the business, it is essential that all departments are involved in the design process. The process of gathering requirements involves all stakeholders and helps each department understand the purpose of the data warehouse, how it will benefit them and what outcomes they can expect.

Requirements gathering can take the form of individual or joint meetings.  This phase often proves to be one of the most difficult parts of the data warehouse implementation. As a data warehouse contains data from many sources and across many departments, there are often discussions about sharing information and prioritisation. A skilled business analyst can act as an external mediator to ensure that all stakeholders are satisfied with the project objectives set.

The requirements gathering phase is very important in ensuring that departmental objectives are aligned with the overall project and that agreement is obtained from all stakeholders. In-depth insight into the data used for analysis can also help to highlight current and future needs, which is likely to reveal where data is and is not being used effectively.

2. Setting physical environments

Your data warehouse will typically have three environments, mimicking best practice in software development. The three environments are the development environment, the testing environment and the production environment, and these are used together to ensure that changes are tested for integrity and security before being deployed live in the production environment. They allow development and quality assurance to take place without affecting the production environment.

Three separate environments are also needed to run test data, identify breakpoints to fix and reduce the stress on server workloads. It is not enough to simply set up different physical environments. The different processes (e.g. ETL, OLAP cube and reporting) must also be set up appropriately for each environment.

3. Implementing data modelling

Data modelling is the blueprint from which the data warehouse is built. It can help to visualise data relationships, standardise naming conventions and establish security process compliance.

This is known as the most complex phase of data warehouse design. A good data model will allow your data warehouse system to grow easily and perform well!

Data modelling is usually done at the data warehouse level and branches into the data warehouse. The three most popular data warehouse data models are Snowflake, Star and Galaxy schema. The model you choose will affect the structure of the data warehouse and data models, and help guide the overall architecture within the warehouse.

4. Choosing the ETL solution

ETL stands for Extract, Transform and Load and refers to the collection and processing of data from different sources into a central data warehouse where it can be analysed later. Your business has access to many sources of data, but often presented in a way that makes it difficult or impossible to consume.

A good ETL process can mean the difference between a slow and difficult to use data warehouse and an elegant data warehouse that adds value to every part of your organisation. Therefore, it is vital to choose the right ETL solution.

5. Online analytical processing (OLAP cubes)

The OLAP framework helps you to analyse the data in a data warehouse or data repository. As you sort data from multiple sources in your warehouse, the OLAP cube helps you organize all the data into a multidimensional format, which makes analysis easier.

6. Create a front-end

This section refers to the front-end visualization where users can understand and apply the results of data queries. If users cannot visualise the reports, the data warehouse is likely to provide little value to them, so front-end development is an important part of the data warehouse initiative. Another important area is the complexity of the reporting tool.

• How often should reports be published?
• Do they require special formatting?
• And does the user need an interface that allows customization of the reports?

7. Optimisation of queries

The more data a query returns, the more resources the database needs to process and store the data. It is therefore important to minimise queries, especially if you are paying extra for query performance.

This stage is very much tailored to the needs of each organisation.

8. Implementing the final product

The hard work is done and you are close to getting value from your shiny new data warehouse. At this point, team members should be trained to use it. Quality assurance and testing throughout the process will ensure that there are no bugs or usability issues.

While these are the standard steps for creating a data warehouse, it is important to remember that every scenario is different. There may be additional steps that your business needs to take based on the complexity of your requirements or the needs of your organisation.