A data warehouse is a collection of data that has been transformed such that it’s easy for business users to access and analyze the data.

We’ll use this blog post to break down the definition and discuss how a data warehouse differs from a data lake.

Let’s start with the first part of the definition: “A data warehouse is a collection of data.” What does this mean? Data warehouses are meant to combine data from multiple data sources. If you think about it – a single customer is likely represented in multiple systems (databases) within your organization. A customer may have contact with your customer service department, sales department, place an online order on your website, or place an in-store order. To be able to fully analyze your customers, you need to be able to view the big picture. Data warehouses are modeled databases that provide this capability.

Let’s take it one step further: “A data warehouse is a collection of data that has been transformed.”Data warehouses display data that have been transformed, which may include some data cleaning but also includes calculating metrics and measures using organizational business rules. This is one way in which data warehouses differ from data lakes. Data lakes are comprised of raw data; data warehouses are comprised of transformed data. For example, the finance department within an organization may have a specific definition for how the business will define total revenues or total sales using the data available within an organization. This definition would be baked into the transformation layer of the data warehouse so that business users in an organization can easily evaluate total revenues or total sales without having to perform complex transformations or calculations themselves.

Finally: “A data warehouse is a collection of data that has been transformed such that it’s easy for business users to access and analyze the data.” Data warehouses, when architected correctly, are designed to be easy to use for business users. Because the data going into the data warehouse are cleaned and transformed, business users can connect to the data warehouse in a dashboarding tool such as Power BI or Tableau or even in an Excel pivot table to view and/or analyze the data in a more user-friendly way. The data in a data warehouse is modeled such that is very easy for business users to interact with the data. It should be as simple as selecting a chart type and dragging metrics and measures into the chart (or pivot table).

Now that we have an understanding of what a data warehouse is, why should I care? Why should my organization have a data warehouse?

Firstly, data warehouses are built for data analysis. Because data warehouses combine data from multiple sources, they provide organizations with the ability to see the big picture rather than analyzing data in silos. Organizations who analyze data from their ERP system separately from their CRM system, for example, are missing out on valuable insights that their competitors are likely harnessing. Additionally, data warehouses are designed to enable fast analytics. Many of the cloud-based flagship data warehouses are columnar data storage, which is a fancy way of saying that they’re optimized to be used for the purpose of analyzing data.

Lastly and most importantly, data warehouses enable data-driven decisions throughout an organization. Data warehouses, by their very nature, are designed to put data in front of everyone in an organization, regardless of their previous experience working with data, from business analysts all the way up to executives. Data-driven organizations consistently outperform those who aren’t fully utilizing their data.

There’s value in your organization’s data and Datalere is here to help you harness the power of that data. Feel free to reach out for a free consultation; we’re happy to discuss how Datalere can help you become a more data-driven enterprise.