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 knows how laborious the process is. This guide will walk you step by step through the data warehouse development process. Let’s get started!
Let our team build the perfect data warehouse for your business!
91.6% of business leaders believe that access to data and analytics is one of the most essentialelements of an organization’s success;
CHI Software’s clients’ achieved 99% data accuracy thanks to optimized data ingestion, transformation and ETL (Extract, Transform, Load) processes.
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.
Creating a data warehouse can help you derive meaningful insights and, at the same time, optimize internal tasks.
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.
Immon’s approach is best for large enterprises.
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.
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.
Kimball’s approach is best for medium and small businesses.
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.
Creating a data warehouse can be a complex process – but our data engineering expertise can help simplify it. We’ve broken it down into clear, easy-to-follow steps for better understanding. Here is a detailed guide to help you build a data warehouse from scratch.
These steps of data warehouse development cover the working process from goal setting to launching the system.
1. Define Your Business Goals
You should know not only how to build a data warehouse, but also why it’s essential for your business. Your first step should be answering the question: “What problems do I want to solve with a data warehouse?” – so having clear goals will help you focus on what data to collect and how to structure it to meet your business requirements.
How to Build Data Infrastructure: 7 Key Steps & Tips
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2. Assess Your Data Sources
You also need to understand where you get your data from – whether that’s your internal systems (CRM, ERP, and marketing platforms) or external sources (social media, partners, or public databases)?
Knowing your sources allows you to determine what data is available, how reliable it is, and how often it is updated – all of which are key to building a consistent high-quality data warehouse.
3. Choose the Right Architecture
Choosing the right architecture affects the efficiency and scalability of your storage. You have several options when building a data warehouse:
Consider the various architecture options before setting up a data warehouse.
A centralized data warehouse that acts as a single location for all of your company’s structured data;
A data lake, designed to store large amounts of unstructured or semi-structured data, such as images, videos and IoT data;
Data marts that work with databases of specific teams (for example, marketing or sales), pulling only relevant data from a centralized repository or business applications;
A hybrid model that combines all of the above elements. A structured core harmonizes all the data, while data marts and data lakes provide flexibility for different teams;
Cloud architecture (AWS, Google Cloud, or Microsoft Azure) allows companies to store massive amounts of data without the upfront costs of on-premises solutions.
4. Create a Project Roadmap
A well-planned roadmap will help you to avoid cost overruns and delays. In addition to TCO and ROI, you will also need to determine:
Scope: What business problems the data warehouse will solve;
Budget: Consider software, hardware, and maintenance costs;
Timeline: Estimate the development and deployment phases.
5. Select a Technology Stack
At this stage, you need to choose the tools you will use for your data warehouse. CHI Software recommends including:
There are three main types of tools crucial for building a data warehouse from scratch.
A database management system (DBMS) for storing, organizing, and searching data. Traditional options include Oracle, SQL Server, and popular cloud solutions such as Google BigQuery, AWS Redshift, and Snowflake;
ETL tools for transferring data from various sources to the warehouse. Informatica, Talend, and Apache NiFi are our recommended choices for structured data. Databricks and Apache Spark are the best choices for large amounts of data;
Analytics/BI tools (Tableau, Power BI, and Looker) for analyzing and visualizing your data in the form of dashboards, reports, and data exploration features. If you need advanced analytics, you can choose Qlik Sense and Sisense.
6. Set Up Data Integration and ETL
After you have a clear plan and tech stack, it’s time to collect and prepare your data for analysis. CHI Software helps our clients gather all their data in one place, clean and organize it with ETL tools. Here’s an overview of our approach, that we’ve refined and tested through experience:
First, we map data flows – a visual plan that shows how data moves between systems.
Then, we move on to designing smart transformation logic to cleanse, standardize, and structure the data and prepare it for full use.
The final step is building automated data pipelines that help manage continuous data flow with minimal manual effort.
7. Structure and Optimize Your Data
Perhaps you’re wondering how to make a data warehouse that will operate using accurate and logical data? The answer: focus on principles and strategies that define how data is stored and used:
Normalization. Breaking data into smaller, logical tables help to avoid repetition and save storage. For example, instead of storing customer info with every order, keep it in one place and just link to it.
Denormalization. Combine data from multiple tables into one, so that the system doesn’t have to jump around to find answers.
Data granularity. Data granularity refers to the level of detail in your data. For example, daily sales are more granular than monthly, as more detailed data is required. More detail means better insights but more storage and slower queries if overdone.
Business logic. Decide how key metrics like revenue, churn, or profit are calculated. Make sure everyone uses the same formulas, document them in the same way so there’s no confusion later.
Use this guide to kick off your project – or reach out for expert support.
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8. Maintain Data Quality
Even the most advanced system with reliable data can sometimes fail – so we believe that strict data quality standards are one of the most important elements of a successful project.
At CHI Software, we normally:
Schedule regular testing to detect problems at an early stage and certify that each data set remains accurate, consistent, and reliable;
Enforce role-based access controls to protect sensitive information and ensure that only the right people can see the correct data;
Combine the previous techniques with a data management system that assures you have access to the right information when needed.
9. Deploy and Test your Storage
Before your solution goes live, it needs to prove its effectiveness. We believe that the best way to do this is through a multi-stage testing process:
Pilot testing: Simulating real-world use with a small group to observe user behavior and identify pain points early on.
Data validation: Matching the data stored in the system to its sources to preserve lost or incorrectly converted data.
Performance testing: Evaluating speed and stability under heavy data loads and complex queries, even during peak hours.
Security testing: Checking access control, encryption, and overall data protection for vulnerabilities.
User acceptance testing (UAT): The final stage of testing before the launch, which usually involves independent testers. UAT allows you to see if you have achieved the end result of a system that meets all your requirements.
10. Launching and Monitoring
Launching your system may be the finish line in project development, but it’s only the beginning of real-world use. At CHI Software, we help our clients smoothly transition from testing to full deployment and make sure everything goes according to plan. How, exactly? With regular system reviews and automated monitoring tools. Our ongoing support includes:
Monitoring system speed and query performance;
Keeping track of storage usage and data growth;
Monitoring for security issues or access violations;
Sending notifications when an issue requires attention.
With effective monitoring, you can avoid costly downtime, catch problems early, and keep your data infrastructure running.
CHI Software: Your Partner in Data Warehouse Development
How do data warehouses work in real life? We are ready to explain everything in the language that should be most readily understood – and that is the results that companies have achieved with CHI Software’s data engineering services.
Achieving 99% Data Accuracy in Marketing Analytics
In business, every second counts. The faster you can analyze your data, the quicker you can optimize campaigns, track product performance, and adjust to customer behaviors. Still, companies can face slow, fragmented analytics without a well-structured data warehouse.
Taking an example from our experience, a leading international marketing company faced these exact problems. Its business intelligence system relied on multiple disconnected data sources and expensive Azure Analysis Services cubes, resulting in slow reporting and high costs.
For this client, CHI Software created a data warehouse in Azure Synapse.
CHI Software stepped in and took action:
Combined all of the company’s marketing data sources into a single structure and created a centralized data warehouse in Azure Synapse;
Migrated from Azure Analysis Services cubes to materialized views, which reduced infrastructure costs;
Optimized ETL processes and increased the efficiency of data ingestion and transformation;
Enabled real-time analytics with interactive Power BI dashboards.
The results speak for themselves:
Reduced analytics infrastructure costs by 30% and optimized budget allocation;
2 times fasterdata processing, accelerated reporting, and insights;
99% data accuracy, providing accurate marketing analytics;
5x increase in data scalability;
Increased the return on investment in marketing by 15%.
Real results, real clients – CHI Software is ready to be your data partner.
Let’s build together
50% Faster Ad Evaluation with Centralized Data Warehousing
Our next client, an adtech company, was losing time and money due to manual processing and data scattered across multiple platforms. These issues were leading to slow decision-making and high infrastructure costs, and required immediate action.
A more efficient data warehouse architecture for our client reduced costs by 30%.
To address these challenges, our team:
Developed ETL pipelines to receive and process data from various sources;
Switched over to AWS Lambda architecture;
Created smart advertising rules in AWS Lambda using real-time data;
Built a centralized reporting system that provides insights through interactive Google Sheets dashboards;
Developed a real-time monitoring system.
The results are impressive:
50% reduction in time for advertising evaluation;
Instant response to fluctuations in advertising effectiveness through real-time monitoring;
30% cost reduction due to a more efficient data warehouse architecture;
Reduced manual workload through automated campaign management.
Conclusion
One thing is now clear: a data warehouse is essential to your business intelligence, strategy, and competitive strength. This statement is backed by the actions of business leaders, 91% of whom are increasing their investment in data and analytics. At CHI Software, we’ve seen this firsthand. Our clients weren’t just dealing with slow reports or messy spreadsheets – they were losing time, money, and opportunities due to unstructured data.
By creating a data warehouse, your organization can avoid these issues, make decisions faster, scale more smoothly, and automate insights.
Wherever you are in your data journey, we’ll help you move forward with clarity.
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FAQs
How much time does it take to build a data warehouse with CHI Software?
The complexity of your data, its volume, and your business needs are the main factors that determine the time and budget for building a data warehouse. As a rule of thumb, a basic data warehouse can generally take three to six months, while a large-scale enterprise data warehouse (EDW) can take up to 12 months.
At CHI Software, we follow a phased approach:
1. First, we do our best to understand your business needs and create a project plan, which takes a week to three weeks to complete;
2. Next, we determine the best architecture and design for your storage. This can take up to six weeks;
3. The next step is development and integration, which can take up to 16 weeks;
4. The last stage is testing and deploying the new data warehouse, which takes four weeks or more.
How much does it cost to build a warehouse solution with CHI Software?
The cost of a data warehouse depends on data volume, infrastructure, integration, analytics tools, and maintenance. In general, the final cost for small and medium-sized businesses starts at USD 30,000. In contrast, enterprise-level solutions range from USD 100,000 to USD 500,000.
How does cloud-based data warehousing compare to on-premise in terms of cost savings?
Cloud data warehouses such as AWS Redshift, Google BigQuery, or Snowflake significantly reduce initial costs and offer greater flexibility through pay-as-you-go pricing, meaning you subscribe and pay only for what you use. This pricing model makes cloud solutions ideal for companies that are looking for quick deployment and easy management.
On-premises solutions require a larger initial investment, but can be cost-effective in the long run for large companies: To figure out which is right for you, we suggest you calculate the total project cost at the start, along with what you will need to pay for storage and maintenance.
How difficult is it to migrate from my existing data systems to a new data warehouse?
The difficulty depends on the volume and structure of your existing data, the number of integrations required, and the type of solution you have (hybrid or cloud). If your data is well organized and you're not dealing with too many systems, the process can be relatively straightforward. However, if you're working with a large volume of data or have complicated systems in place, it might be more challenging.
Can you tailor a data warehouse solution to my industry-specific needs?
Absolutely! CHI Software specializes in customized data warehouse solutions for a wide variety of industries:
- E-commerce and retail;
- Healthcare;
- Financial services;
- AdTech and marketing;
- Logistics;
- Real Estate;
- Travel & Hospitality.
Sirojiddin is a seasoned Data Engineer and Cloud Specialist who’s worked across different industries and all major cloud platforms. Always keeping up with the latest IT trends, he’s passionate about building efficient and scalable data solutions. With a solid background in pre-sales and project leadership, he knows how to make data work for business.
Ivan keeps a close eye on all engineering projects at CHI Software, making sure everything runs smoothly. The team performs at their best and always meets their deadlines under his watchful leadership. He creates a workplace where excellence and innovation thrive.
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