SaaS businesses are increasingly relying on data warehouses to manage and organize vast amounts of data. As a SaaS company, you need to collect data from multiple sources (databases), making it difficult for you to manage and analyze it.

A data warehouse allows you to store this vast data amount in a centralized repository. This places you in a better position to manage and analyze data and make informed decisions. Not only does it allow you to gain valuable insights into your customers but you can also get a 360-view of your business operations and market trends.  

Let's take a quick look at what a data warehouse is, its different types, how to build a data warehouse, including its costs, and why you need to hire data architects to build one.

What is a Data Warehouse? 

A data warehouse is a unique, cost-effective single-storage location of consolidated data. It's an integrated, non-volatile storage space that makes it easier for your SaaS business to identify trends and gain valuable insights into your business. 

It's the data warehouse that helps you to turn raw data into actionable insights. Unlike a database that deals with your daily business operations, a data warehouse is mostly about analysis and reporting. Let's understand how this non-negotiable business asset can help a SaaS company or what the purpose of a data warehouse is:

• Business intelligence: Data warehouses simplify the process of navigating data from across different systems. It's a single source of accurate and consistent data, meaning you can rely on it to generate reports and conduct data analysis more efficiently. In fact, it reduces the risk of errors and enables business intelligence to deliver actionable insights.
• High data quality: Thanks to data warehouses' non-volatile nature, your SaaS company can preserve historical data in data warehouses. It also helps you to clean and organize data to maintain its quality and consistency.
• Cost efficiency: Data warehouses make it easy to collect data without spending a lot of time and money. The time you save in this automation process can be used for extracting valuable data without the need for manual handling of data.  

Types of Data Warehouses

Before delving into the steps to create a data warehouse, it makes sense to be aware of the various types of data warehouses, including:

• Enterprise Data Warehouse (EDW): An EDW is a single repository that contains all current and historical data of an organization to ensure broader access and analysis. Unlike a data warehouse that collects and stores data from a specific department of an organization, an EDW collects data from multiple sources. This type of data warehouse is a valuable tool for gaining actionable insights and data analytics.  
  
   
• Operational Data Store (ODS): An operational data store provides a real-time view of your business processes or operations. It's a specialized database that collects data from various systems. Your SaaS company can use ODS for light-duty analytical processing or operational reporting. It does not need you to transform the data for analysis, making it easier for you to make important business decisions.   
   
• Data Mart: It's a smaller version of a data warehouse. Data marts are a cost-effective solution for your SaaS business that are in need to analyze specific data sets without investing in a large-scale data warehouse infrastructure.
  
   
• Cloud Data Warehouse: It's a centralized repository where you can store, manage, and process vast amounts of data in the cloud. A CDW offers a robust and scalable solution that leverages the power of cloud computing for data analysis and reporting.  

Essential Building Blocks of a SaaS Data Warehouse   

A data warehouse is the foundation of a successful SaaS business. Understanding the integral components of a data warehouse is essential for you. To help you with that, we have jotted down the building blocks of data warehouses below:

• Data Sources and Integration: This is about pulling data from various external source systems into the warehouse. First, determine the right systems that generate relevant data for your SaaS business. It can be mobile applications, CRM systems, payment gateway, website, or other operational systems.
  
   
• ETL Process: This is where you extract raw data from external sources with the help of ETL (extract, transform, load) tools. Then you need to clean and transform the extracted data in accordance with the data warehouse's requirements. Lastly, you need to load the transformed data into the warehouse.
  
   
• Data Storage and Modeling: You are going to need a suitable data warehousing technology for data storage and modeling. You can leverage a relational database (PostgreSQL, MySQL), a data lake like Amazon S3, or Snowflake (a cloud data warehouse). Next comes the data model designing part. Make sure to design a data model that supports your SaaS business data analysis needs.
  
   
• Data Access and Reporting: The last step is to ensure stakeholders can access data through dashboards and tools for analysis.

How to Design a Data Warehouse for SaaS? 

Now that you have a clear idea of a data warehouse and its various types and components, it's time to create one. Here's a step-by-step guide on how to develop a data warehouse for your SaaS business:

Step 1: Understanding Your Business Requirements and Data Needs

This is a non-negotiable step, which sets the foundation for your business. Before diving into technology, you need to clearly define what you want to achieve with your data warehouse. The first step is to identify key stakeholders. Who will be using the data warehouse? Sales, marketing, or product development? Understanding their needs is crucial.

Next, you need to define your data requirements. What data will help you answer the most pressing business questions? User activity, platform usage, financial data? List them down.

Once you are done defining your data requirements, set goals and objectives. What do you hope to gain from your data warehouse? Improving customer experience, optimizing operations, driving revenue? Make sure your goals are clear, transparent, and realistic from the beginning.

Step 2: Choosing Your Data Warehouse Architecture

The architecture serves as the blueprint of your data warehouse. There are three main options for you to consider, including:

• One tier architecture: This is the simplest data warehouse architecture you can rely on. If yours is a small SaaS business with limited data volume, this one is ideal for you.
  
   
• Two tier architecture: This separates data storage and processing into two layers. Thanks to its flexibility and scalability feature, it's ideal for moderate data volumes and complex analysis needs.
  
   
• Three tier architecture: This provides the most flexible and scalable architecture solution for large-scale data warehousing.

Step 3: Cloud vs. On-Premise Technology

Cloud-based data warehouses are gaining popularity for SaaS businesses due to many reasons. First, cloud resources can easily adapt to your growing data needs. They are cost-effective, too. You only pay for what you use, eliminating expensive hardware investment. Plus, cloud solutions offer easier setup, maintenance, and access. On-premise technology is well suited for specific security or regulatory needs.

Step 4: Designing Your ETL Process and Data Pipelines

This stage involves transforming and moving data from external source systems (websites, apps) into your data warehouse. For this, you need to develop workflows to extract data from various sources, clean and standardize it. Then you need to load it into the warehouse.  

Set up automated processes to keep extracting, transforming, and loading data. This ensures a constant flow of fresh information.  

Step 5: Implementing Data Security and Compliance

Data security is paramount. Here's how to protect your SaaS data warehouse:

• Data encryption: Encrypt sensitive data to prevent unauthorized access.  
• Access controls: Restrict access based on user roles and permissions.  
• Compliance considerations: Ensure your data warehouse complies with relevant data privacy and security regulations (e.g., GDPR, CCPA).

Step 6: Testing and Optimization for Performance and Scalability

Simulate real-world data volumes to assess performance. Identify and improve slow-running queries for faster results. Continuously monitor your data warehouse design for optimal performance and scalability.

Cost and Time Considerations 

The upfront cost of building a data warehouse from scratch can be high but its long-term benefits are worth considering. A few factors determine the cost. The cost of cloud storage depends on the amount of data you want to store. The cost also depends on the ETL tools you use.

You may need to invest in hardware solutions like servers, storage devices, and networking equipment if you opt for an on-premise data warehouse. The cost of software licensing is another factor to consider.

Now, you might be wondering, “How long does it take to build a warehouse?” The time it may take to build a data warehouse depends on various factors. They may involve the size and complexity of your data, the team's experience, and the chosen technology. Usually, it takes from a few months to over a year to build a warehouse. You can benefit greatly from hiring remote engineers to manage setup and optimization.

Why Hire Data Engineers for Building a Data Warehouse? 

It takes expertise and skills to build your own data warehouse that data engineers have. Skilled data engineers know how to ensure a successful warehouse implementation. Here are more reasons why you should hire remote data architects:

• Specialized skills: Data engineers are experts in data modeling, integration, and managing ETL pipelines. They know how to transform raw data into actionable insights.
• Efficiency and scalability: They can set up your data warehouse efficiently and ensure it can handle your growing data needs.
• Long-term maintenance: Data warehouses require ongoing management. Data engineers can help keep your data clean, your ETL processes up and running, and your system up-to-date.

Conclusion  

In a data-driven economy, if SaaS businesses are to thrive, they need a well-structured data warehouse. Without this, it's impossible to make informed business decisions and improve customer experience.

Developing a data warehouse is not an easy undertaking. It demands the skills and expertise of an expert data engineer. They bring expertise and specialized skills to the table, letting your SaaS business leverage the full potential of real-time data through a well-curated data warehouse.