Data warehousing has often been described as a business analyst’s dream. Imagine having all the information about an organization’s activities stored in one place, accessible through a single set of analytical tools. That’s exactly what a data warehouse does.
But the question is—how do you turn this dream into reality?
The first step is proper planning. Since the main goal of a data warehouse is to provide accurate, timely, and relevant information to decision-makers, you need to start by understanding the type of questions users will want to ask. For example:
- How many registrations did the company receive in each quarter?
- What industries are purchasing custom software development in the Northeast?
To get there, a warehouse system must be developed step by step. Let’s look at the major stages involved in data warehouse development.
Step 1: Determine Business Objectives
Every warehouse project begins with the big “why.” Why is the company building a data warehouse in the first place?
In most cases, the company is going through rapid growth and needs the right balance of administrative, sales, production, and support staff. Executives need to know if expanding overhead staff will actually pay off. Similarly, as new sales strategies are rolled out, leaders want to measure whether they’re really effective.
On top of that, external market changes may shift the company’s focus from national to regional strategies. Business leaders must understand how these shifts affect overall performance.
To make the right calls, managers rely on a set of metrics—Key Performance Indicators (KPIs). These are numerical values that track business activities such as:
- Units sold
- Gross profit
- Net profit
- Hours worked
- Students taught
- Repeat student registrations
All these metrics are pulled together into what’s called a fact table. This allows decision-makers to see beyond surface-level profit and analyze deeper causes. For instance, a small contract might require almost the same administrative effort as a large one, but yield far less margin. Tracking contract size then becomes essential to spotting what drives big wins.
Step 2: Collect and Analyze Data
Once objectives are defined, the next step is gathering the actual data.
Most companies already have various reporting systems in place. These might come from:
- Accounting software
- CRM applications
- Time reporting systems
- Custom applications
Apart from these, there’s also hidden or “overlooked” data. For example, handwritten notes, email reports from supervisors, or small desktop databases that track shipping dates. People may dismiss these as unimportant, but in reality, every piece of information exists for a reason. Ignoring them could mean missing out on valuable insights.
During this stage, warehouse builders need to interact closely with staff, supervisors, and analysts to understand how they currently collect and process information. Many tasks may be automated later, but automation is only possible once you fully grasp the process.
This phase requires deep engagement—listening carefully, repeating what you understood, and clarifying until you have a complete picture of the business processes.
Step 3: Identify the Critical Business Processes
By this point, you should have a clear picture of the main business processes and how they connect. You already know the KPIs to track, but now you must identify the entities behind those metrics.
Take an example: A training sale.
- A customer might not have an existing relationship with the company.
- They may require classes or an on-site trainer.
- New product releases (like Windows 2000 back in the day) could drive demand.
- The company might run promotions or hire new sales staff.
Each of these events influences the outcome.
A data warehouse captures these relationships by structuring data into fact tables (for KPIs) and dimension tables (for entities like customers, products, promotions, and time). Together, these form a dimensional model—essentially a 3D map linking facts with the dimensions that produced them.
For instance, if a salesperson sold 60 units, the fact table connects this sale with the salesperson, customer, product, and date.
Because of this structure, data warehouses typically contain multiple fact tables, each focusing on a specific subject, but connected through shared dimensions. Later, these facts and dimensions are processed into OLAP cubes, making it possible to analyze information across multiple perspectives.
Step 4: Build a Conceptual Data Model
Once the business processes are identified, the next step is designing a conceptual data model.
Here, you define:
- Which topics will become fact tables
- Which dimensions will be linked
- The KPIs for each process
- The storage format and unit of measurement
Consistency is critical. If you’re a global organization, you’ll need to standardize units like currency. For example:
- Decide on a base currency
- Define when exchange rates will be applied
- Track rate changes if necessary
Each fact table row represents the result of interactions between entities. So if your data is incomplete or inconsistent (a common issue in legacy systems), you’ll need to clean and fix it before integration.
The fact table usually uses a composite key, built from the foreign keys of all linked dimension tables. This ensures that each fact is tied to the right entities.
Building this model requires careful preparation since errors at this stage can cause massive rework later.
Step 5: Identify Data Sources and Plan Transformations
Now comes the practical question: where will the data come from, and how will it be transformed?
For example, in a company that tracks training sales, the main sources might include:
- An in-house training application
- A CRM system for sales
- A time-tracking system for staff
The challenge is that these systems don’t always share identifiers (like employee IDs or customer numbers). Reconciling this is part of the design.
Another major task is data cleansing. Many OLTP (transaction) systems have missing or incomplete data. For instance:
- ZIP codes might be left blank
- Discounts may not be recorded properly
- Time logs could be inconsistent
You may fill gaps programmatically (e.g., deriving ZIP codes from city/state) or manually (retrieving old files). Both cost money, so you must plan carefully.
Once clean, the data must be transformed into a consistent structure. Transformations may involve:
- Simple field mappings
- Format conversions
- Unit conversions (pounds → kilograms, inches → centimeters)
- Data summarization
- Complex algorithms for derived values
You’ll also need to decide on the right tools for this—whether built-in services like DTS (Data Transformation Services) or third-party ETL software.
Finally, schedule extraction wisely. Pulling data impacts performance, so it’s best to run these jobs during off-peak hours (like weekends).
Step 6: Define the Tracking Duration
A data warehouse consumes enormous storage, yet it’s designed to keep historical data for analysis. So how do you balance storage with accessibility?
The solution is granularity management.
Data can be stored at different levels of detail over time. For example:
- First 2 years: daily data
- Next 3–5 years: weekly summaries
- Beyond that: monthly summaries
This way, analysts can still run reports on historical performance, but without overwhelming storage.
If older data is imported, it can also be transformed into the right format directly. Planning this archiving strategy early saves both cost and effort later.
Step 7: Put the Plan into Action
Finally, with the plan in place, it’s time to build.
Since warehouse projects are usually large, staged delivery is key. One proven approach is to first build a data mart—a smaller segment of the warehouse—to demonstrate value quickly.
Over time, each segment (like puzzle pieces) integrates with others, expanding the system’s capabilities.
When properly executed, the warehouse provides executives with consistent, aggregated historical data to analyze business performance. It becomes a powerful tool to understand how different factors interact, helping leaders make better strategic decisions.
Final Thoughts
Building a data warehouse isn’t just about technology—it’s about understanding business goals, processes, and people. Each stage, from defining objectives to implementing the system, plays a critical role in ensuring the final product delivers real value.
With careful planning, strong engagement, and structured execution, a data warehouse can transform raw data into actionable insights—empowering businesses to make smarter, faster, and more informed decisions.
