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5 Stages of the Data Warehousing Process

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big data growth

Large organizations produce a significant amount of data daily. The data must be stored in a shared capacity to allow access by various organization departments for analysis, reporting, and decision-making purposes.

In short, data warehousing is a centralized repository of data from various sources. Hence, a data warehouse is a storage point for all the data a business generates collected from multiple sources.

Before we move into the stages of data warehousing, let’s look more at how data is measured.

Big Data

Statista says the estimated amount of data created in 2021 was 79 zettabytes. If you don’t know what a zettabyte is, then the statistic has no relevance to you.


A zettabyte is equivalent to a trillion gigabytes. You’ll be aware of gigabytes as your mobile phone has a storage capacity measured in it. For example, an iPhone can have 64GB of storage. A gigabyte is a thousand million bytes.

Globally, we’re generating a lot of data, so the ‘big data’ analytics sector is growing as fast as data is generated and stored hence the need for data warehousing.

Stages Of The Data Warehousing Process

There are five notable stages of data warehousing, starting with defining the business goals and how they are measured.

Establishing Business Objectives

Every business has objectives, and the first step in the data warehousing process is clearly determining those business objectives. The general aim of every business is to make a profit, but business objectives that lead to that need to be more vivid.

The business should have an overarching goal besides that, for example, delivering the best telecommunication services in the industry. Every department should have objectives that contribute to the larger company goal.

These objectives need to be quantitatively measurable for the data warehousing process to succeed. These objectives will also guide managers’ decision-making process to lead the organization.

Collecting And Analyzing Information

big data

The next stage will collect and analyze the information to put into the data warehouse. Each business department will create a summary and analytical reports that include information about various business processes.

Collecting the data can be challenging, but the process becomes more manageable if the information is automated. However, you may have to retrieve the data from the appropriate parties manually.

A data warehouse is a collection of interrelated data structures, so data migration from the various departments and business processes will be necessary. Identifying the core business processes will help with the data analysis, which is integral to the data’s storage.

Cleaning of data may also be necessary at this stage to ensure only vital information is stored in the data warehouse.

Developing A Data Framework

Storing data in the warehouse needs to be done in an organized and efficient manner. The best way to do that is to develop a conceptual data model. Doing so will include identifying key performance indicators for each business process and the format for storing the data.

A data framework will ensure that the data you hold in the warehouse is complete. The framework can take a long time to construct, but it makes the data warehousing process relatively easy. The framework may also be challenging to maintain, so restructuring may be necessary.

Moving The Data

Once you know the data you need and have a framework to store it, you have to locate the data sources and plan the data transformations.

In short, you have to transfer the data into a consolidated data structure which will depend on the company’s current data storage.

Every business has databases and backups, which will be the primary source of information. You will have to ensure the data is complete from the source or program it for completion before transfer.

The key is to ensure any reports generated from the data warehouse match the reports from the data source.

It would be best if you also planned when the data transfer would occur to ensure minimal impact on the data’s users.

Plan Implementation

The final stage will be implementing the plan. Once you move the data and successfully track its importation into the data warehouse, you must ensure that the data fits well into the predetermined structure or framework. If it is a large project, it will include developing phases and schedules for data delivery.

Summing Up

Managing and using Big Data has never been as straightforward as it is now with data warehousing.  For non-techies, there’s no denying data warehousing is a complicated process with many moving parts and things that can go wrong. However, following the outlined stages, your business should have a smooth transitioning process with minimal interruptions.

Please note – that the steps will vary according to the business and the data.  Therefore every company must determine what they want from their data warehousing project so activity can be appropriately measured.  Starting with the end in sight, you’ll have what you need for decision-making and determining the future direction of the business.