Building a Data Warehouse: Key Business Benefits and Development Approaches
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A data warehouse is the central hub where a business stores and organizes data, making it easier to analyze trends and generate insights to improve business strategies. Companies are already reaping the benefits of creating a data warehouse, and the investment figures are steadily growing: from USD 13 billion in 2018 to USD 33.7 billion in 2024.
But if building a data warehouse that grows with your business were that simple, we wouldn’t have needed to write this article explaining the workflow. As a data engineering company with more than six years of experience, CHI Software is guiding your through business benefits and key development approaches.
To find out the 10-steps flow of building a data warehouse, read our article in full.
Why Companies Invest in Building a Data Warehouse
Why do companies typically decide to implement a data warehouse? It’s the result that’s the incentive: businesses end up with a centralized, structured system that turns raw data into clear insights. And if you think that the benefits of setting up a data warehouse stop there, then just keep reading.
Higher Data Quality and Insights
A data warehouse is where your business’s information gets cleaned and standardized, eliminating duplicates and inconsistencies. What’s more, once you build a data warehouse, you have the ability to track trends and predict future opportunities. Historical analytics helps you stay ahead of the competition by analyzing seasonal sales patterns and identifying changes in customer behavior.
Smarter Decisions
A data warehouse centralizes your business data, providing a reliable source of truth for making informed decisions with information that is always up to date. Statistics show that companies using data-driven decision-making are almost three times more likely to see above-average growth than companies that assess their data only periodically.
Saving Time
Without a data warehouse, your team might spend hours manually collecting and correcting data from various sources to create reports. A data warehouse can automate these processes, reduce errors, and save as much as 40% of your employees’ time while yielding more accurate reports in a much shorter period, since your employees are focused on quality analytics.
Cost Reduction
Bringing data together from different sources reduces manual work, cutting labor costs and human errors. The higher the quality of your data, the more accurate your analytics and business analytics, and the lower your costs. On the other hand, data inconsistencies can be costly: a study by Gartner claims that poor data quality costs organizations at least USD 12.9 million a year on average.
Remember: partnering with a big data development company can significantly reduce the risk of data inconsistencies, which can be costly for your business. The right tools and expert guidance help your business maintain clean, reliable data — the foundation for smart decisions.
Smooth Growth
A well-built data warehouse design can easily handle increasing loads of data and more complex queries without slowing down.
It’s no surprise that more companies are recognizing the value of this flexibility. The global data warehouse market is likely to double from USD 33.7 billion in 2024 to a predicted USD 69.64 billion by 2029.
Strengthening Competitive Advantages
Companies that use data always outperform those that don’t. A data warehouse gives you the speed, accuracy, and insights you need to adapt quickly and improve operations. It’s no wonder then, that 91.6% of leaders believe access to data and its analytics are critical to business success.
Choosing the Best Approach to Building a Data Warehouse
Before you dive into building a data warehouse from scratch, you need to understand the different approaches that exist, and how you can apply them. How you structure your data warehouse affects everything from performance and scalability to cost and usability.
Inmon’s Approach (Top-Down Model)
Bill Inmon proposed the method of starting with a centralized data warehouse that serves as a single source of truth for the entire company. The central repository is built first, and later smaller data warehouses (individual data warehouses for different departments) are added to it.
Key features:
- All business data is stored in one structured system;
- The information is clean, standardized, and well-organized. The number of errors and duplications is minimal.
Best for large enterprises that require long-term scalable solutions and in-depth analysis for large data sets.
The biggest downside: Building a core data warehouse takes time, so companies need to be patient to see the full results.
Kimball’s Approach (Bottom-Up Model)
Ralph Kimball’s approach takes the opposite path. Companies first create small independent data warehouses focused on specific business areas like marketing, finance, or HR. Next, they integrate and form a full-fledged data warehouse.
Key features:
- The top-down model doesn’t take much time to set up, so companies can start using the data warehouse approach immediately;
- Since data marts (mini-databases for specific business areas) are created one by one, this model is more flexible and cost-effective;
- Data organization is approached using a star schema, where a main table (fact table) connects to several smaller tables (dimension tables), making it easy to analyze and retrieve information quickly..
Best for: Medium and small businesses that want fast results and flexibility.
The biggest downside: Maintaining integration requires more precise management, as it becomes less structured over time.
Hybrid Approach (Best of Both Worlds)
What if you need both structure and flexibility? The hybrid model combines the best parts of the Inmon and Kimball methods: it starts with a core data warehouse (like Inmon) and allows you to quickly create data marts for specific needs (like Kimball).
Key features:
- Ensures data quality and consistency while allowing fast and flexible access to data;
- The original data remains unchanged, helping to recover from errors.
Best for: Companies that need flexibility and long-term growth potential.
The biggest downside: Since the hybrid approach combines two different models, it requires careful planning, more setup time, and skilled staff to maintain both the structured core and the flexible data marts.
The most interesting part comes next! On our blog, we explain the process of data warehouse development step by step, from defining the problem to the final launch. Follow the link to read more.
