What Your Business Intelligence Solution Can Lose Without a Data Warehouse?

Considering a Business Intelligence solution for better decision-making and business process improvement? You might think a Business Intelligence Data Warehouse (DWH) is necessary, but it comes with extra costs.

Can you implement a Business Intelligence solution without a DWH to save money? What benefits does a data warehouse bring, and is its business value evident? Let us discuss these points. 

Advantages of a Data Warehouse Compared to a Database

In your daily tasks, you work with a Database (DB) to input, store, and modify transactional (statistical) business data. That could be in the database of your accounting or CRM software, among others.

The database holds detailed info on your sales transactions, like Customer #1 from Segment #1 purchasing three units of SKU#1 on December 7, 2023. With tens of thousands of entries daily, you can’t use this data for decision-making without some initial preparation.

To get the data ready for analysis, you need to: 

• fetch it from the database (the source); 
• transfer it to specialized software (like Excel, Power BI, Tableau, etc.); 
• perform your calculations. 

The more calculations, the longer it takes, and the greater the chance of errors. Only then can the data be used for decision-making.

Data Warehouse

A Data Warehouse (DWH) is typically a collection of databases designed to store both detailed and summarized data. It’s primarily built for analyzing data to inform decision-making.

A Data Warehouse (DWH) database can serve as the origin for various aggregated and calculated data:

• Total Sales (by Location, Category, SKU, Period, etc.). For instance, all customers from Segment #1 bought 100,000 units of goods from Category #1, generating $1,000,000 in revenue in December 2023. 
• Total Sales Growth (by Location, Category, SKU, etc.), showing an increase of $100,000 or 10% in December 2023 compared to December 2022. 
• Budget Vs. Actual (by Location, Category, Period, Customer Segment, etc.), with the actual variance being $10,000 or -10%.
• and so forth.

You can use this data to build models, like predicting the demand for goods from Category #1 among customers in Segment #1. The DWH automatically loads and precalculates the data for analysis, saving you from spending on specialists and eliminating the risk of human error.

A data warehouse is not your typical database; it holds aggregated and calculated data meant for analysis. That’s why, if you’re aiming for analytics to drive business decisions, a DWH is indispensable.

Explore real-world applications showcasing the impact of mastering business analytics.

Advantages of Data Warehousing for Businesses

Let’s explore the benefits of a data warehouse by considering what you might miss out on without it:

• Ensure your BI tool runs smoothly. Dealing with large datasets without a DWH brings a high risk of performance issues, like hanging and crashing, nearly 100%. Instead of analyzing data, you’ll spend time waiting for your BI tool to cooperate.
• Ensure your BI tool functions reliably. Without a DWH, the risk of losing business data is almost certain. Do you want to risk making wrong decisions because the software doesn’t load all the necessary data without your awareness?

Potential Performance Challenges Without a Data Warehouse

Imagine your accounting software (such as Quickbooks) or CRM software (like Salesforce or Zoho). Now, picture connecting it directly to your BI tool (such as POWER BI or any other) WITHOUT a DWH to generate insightful reports and dashboards. 

This setup could lead to performance issues when downloading data from the source database.

1. Challenges with Performance in the Source Database

Picture this: on Monday at 9 AM, one part of your team is inputting new transactional data into the QuickBooks/Salesforce database, while the rest, using POWER BI, is trying to generate reports from the same database.

In this scenario, both QuickBooks/Salesforce and POWER BI might become unresponsive as resource-intensive requests from POWER BI slow down the performance of the source database. Essentially, your team would spend valuable time waiting for the software to function. Connecting your accounting/CRM software with the BI tool WITH a DWH prevents such challenges. 

With a DWH, you can eliminate performance issues with the source database because the data is loaded not directly from the source but from the DWH. Additionally, the DWH automatically extracts all the data from the source database outside of regular working hours, for example, at night, without disrupting others’ work.

2. Challenges with Performance in the BI Tool

BI tools often load raw data for analysis, like every transaction for every client at every moment. However, analytics typically needs aggregated data, such as total monthly sales by product group.

To process this data, you need software to aggregate and calculate. In this case, let’s say we’re using POWER BI on your computer. The speed of this process relies on your computer’s hardware. If your PCs aren’t equipped for such tasks, they might freeze, especially with a large volume of data. For instance, a retail shop with 100,000 transactions per day means POWER BI needs to analyze around 2 million transactions or lines of data over a 6-month sales period.

Additionally, if you want to view the long-awaited report from a different angle by applying a filter, the calculations will have to be redone because the aggregated data wasn’t saved anywhere. 

That means spending a lot of time waiting for the reports. 

In essence, without a DWH or using an in-memory approach, you’ll waste time waiting for your BI tool to function. 

With a DWH, you can eliminate performance issues with the BI tool because all aggregations and calculations are premade in the DWH. 

That provides you with data ready for analysis, and your BI tool doesn’t need to allocate resources to aggregations and calculations.

In Conclusion

While a Business Intelligence Data Warehouse (DWH) may seem like an additional expense, the question arises: Can a BI solution be implemented without a DWH to save costs? In daily tasks, working with a Database (DB) involves handling transactional business data. However, to make this data usable for decision-making, a process of fetching, transferring, and calculating is required. That is where a Data Warehouse (DWH) comes in—a collection of databases designed for analyzing detailed and summarized data, and supporting decision-making processes.