The Ultimate Guide to Data Warehouse Design  

If you’re looking to improve nearly any aspect of your business operations, you must first understand where you’re starting from. Ultimately, this level of business analysis boils down to not just having as much data as possible — but having data that is well-managed, easily retrievable, and easily analyzed. This can be easier said than done.

The good news? Setting up a data warehouse can help you build forecasting models, identify trends, and keep things in your organization running more efficiently overall. Before you can begin reaping the benefits of a data warehouse, of course, you have to design and implement one.

What is a Data Warehouse, Anyway?

Specifically, a data warehouse refers to a single, centralized location where a business stores large amounts of data. Oftentimes, this data comes from many different sources — and multiple locations across the country or even across the globe. Housing data in a centralized location makes it easier to carry out business intelligence, analysis, and reporting.

Data warehouses are ideal because they deliver a consistent location for data to be stored. This also makes it easier for authorized employees to locate the information they need when they need it, improving efficiency and saving the company time/money. Likewise, data warehouses are stable and provide the highest level of security, which is especially important for businesses that may be dealing with sensitive information.

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Steps to Designing a Data Warehouse

The exact steps you’ll need to take to design and implement a functional data warehouse within your organization may vary depending on your exact needs, budget, and goals. However, designing a data warehouse will generally follow the same series of steps.

1. Consider Goals and Objectives

The first thing you’ll need to do is to determine exactly what you want to get out of your data warehousing project. It can be helpful to think of these goals in conjunction or in relation to the organization’s broader objectives. Where does your data warehousing project fit in with the bigger picture?

During this time, you’ll also need to set some basic requirements for your data warehouse, such as:

  • Its location
  • Its disaster recovery plan
  • Compliance needs (depending on your industry)

2. Ask Questions and Analyze Current Data

Once you have a better idea of what you want to get out of your data warehouse project, it’s time to take a closer look at how data is currently being handled within your organization. More than likely, your employees already have organized means of collecting and storing data, perhaps on local servers or in some other format. However, it’s also important to look at the less formal data management practices your employees may be engaging in. This includes notes taken down on paper, data stored on thumb drives, data saved on company computers, and the like.

The better you understand how data is being handled (and what kind of data is being worked with), the more comprehensively you’ll be able to design a data warehouse.

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3. Build a Data Model

Next, it’s time to create your first data model. A data model is a sort of blueprint for how your data will be visualized and distributed once it is collected. This is arguably the most challenging and tedious step in the entire process, but it’s important to get this part right. There are many different options for data modeling, with the most popular including:

  • Galaxy schema
  • Star schema
  • Snowflake schema

4. Decide on an ETL Solution

After you’ve created a data model that you’re happy with, it’s time to select an Extract, Transfer, Load (ETL) solution that will handle the actual transfer of data from any given location to your warehouse. Choosing the right solution here is key, as a great ETL tool will be reliable, fast, and easy to use.

5. Plan Your Implementation

Before a data warehouse solution can be implemented within your organization, you’ll need to plan a careful rollout that involves training your employees on how to use the system. Generally, it’s best to handle the rollout on a smaller scale before fully implementing a data warehouse across the entire organization. This will help to identify obstacles that need to be addressed, ensuring a more seamless transition down the road.

The Final Word on Data Warehouse Design

There’s a lot to keep in mind when it comes to designing a data warehouse, which is why many business owners and even business analysts decide to hire this work out to an experienced professional. Established companies are all about making it easier for organizations to get the most out of their data without the need for extensive coding knowledge or expertise. Consider learning more about how these platforms work and what they can do for your growing business.