A Definitive Guide To Data Warehousing: What It Is, Uses, Approaches and Example

By Indeed Editorial Team

Updated March 25, 2021

Published February 25, 2020

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Integrating large amounts of business data into a computer system can involve a highly-detailed process. Oftentimes, businesses who implement business intelligence analysis find it more effective to store their business data in a large database, called a "data warehouse." Data warehousing aggregates all of a business's data into one central location, instead of storing specific information in multiple databases. In this article, we explore what data warehousing is, who uses it and several different approaches for data warehouse testing.

What is data warehousing?

Data warehousing is the aggregation of a business's data from similar sources. Data warehousing can allow companies to store large amounts of business intelligence data in a single system and can involve the integration and consolidation of analytical report data and data from ad-hoc queries to aid business professionals in evaluating and making important business decisions.

Using a data warehouse can enable businesses to collect and analyze data that can influence decision processes like product testing, target market analysis and other decisions requiring specific business intelligence data. Some instances when a business might use a data warehouse include when developing product strategies, analyzing customer markets and analyzing business operations.

Read more: Learn About Being a Business Intelligence Analyst

Who uses data warehousing?

Many different business industries can benefit from integrating data warehousing into their data management systems. The following examples help illustrate the types of professionals who might use data warehousing:

  • Data warehousing specialists

  • Lead decision-makers

  • Sales and marketing teams

  • Production and project managers

  • Financial analysts and planners

Data warehousing specialists

A key professional in building and maintaining a data warehouse is a data warehouse specialist. These IT professionals work with businesses to integrate and implement data warehousing to help streamline business processes. For instance, a data warehousing specialist may work with a healthcare facility to aggregate various data like financial and insurance data, patient records, treatment plans, medication information and medical supply inventory.

Lead decision-makers

Data warehousing is used across a wide variety of industries, and professionals who act as lead decision-makers for their companies may also be the types of professionals who use data warehousing. For instance, a team leader who makes decisions regarding product development might use a data warehouse to find and analyze historical product information to ensure new product ideation adheres to company standards.

Sales and marketing teams

Sales and marketing teams may also use data warehousing. For instance, a sales team might track and report data for transactions, referrals and lead conversions in a data warehouse. This data can then be analyzed to assess the sales team's efficiency in selling company products. The team can then develop new sales strategies depending on the data analysis.

Marketing teams can use data warehousing in much the same way, where strategies, marketing budgets and conversion rates for different strategies can be stored and analyzed through the data warehouse. This data can allow marketing teams to streamline or improve their approaches to promoting company products or services.

Production and project managers

Production and project managers might also use data warehousing as a means for analyzing product development plans as well as for evaluating the processes in project management. For instance, a project manager might search within a data warehouse to evaluate historical data such as past project scopes, budgets and other project data to aid in making decisions about future projects. Production managers might use data warehousing similarly, however, the data gathered on production operations might include different product development stages and information from past product launches.

Business and financial analysts

Business analysts might use data warehousing as a means for evaluating past business data such as funding, product development, customer relations and other important business intelligence to help make decisions for further growth and development. Financial analysts may also use the information within a data warehouse to analyze a company's past financial trends such as revenue, past sales data or other financial data like funding, expenses or liabilities.

Related: How Analyzing Data Can Improve Decision-Making

Different approaches to warehouse testing

Depending on the needs of a business, data warehousing can be implemented using a variety of different approaches within each level of data warehouse implementation. They include:

  • Query testing approach

  • Functional testing approach

  • Quality assurance testing

  • Engineering testing approach

  • Acceptance testing approach

Query testing approach

When integrating a data warehouse, businesses might perform query testing as an approach to ensure that the data warehouse is extracting and relaying the data that users search for. After entering data into a warehousing system, sampling, reporting and ad-hoc queries are tested to ensure that the data warehouse shows the required outputs to a user's search query.

Functional testing approach

Functional approaches to data warehouse testing generally assess the overall reliability and functionality of a data warehouse. Functional testing validates the program's responsiveness, capabilities and other functional traits that enable data warehousing systems to operate.

Quality assurance testing

Quality assurance testing in a data warehouse can involve assessing the overall performance, load times and other quality assurance tests like regression testing to ensure the data warehouse is running efficiently.

Regression testing can also be used in quality assurance approaches to data warehouse testing to ensure any new data or program code that is entered into the system does not affect the functionality of the data warehouse. Essentially, quality assurance approaches to data warehouse testing can allow businesses to analyze the effectiveness of integrating data warehousing to track and document various business intelligence data.

Engineering testing approach

At the software engineering level, data warehouse testing can include efficiency testing, structural testing and documentation for various test cases. Efficiency testing typically assesses the efficiency of the data warehouse in searching for and loading query data. Structural tests allow engineers to assess the format and overall foundation of the data warehouse to ensure its structural features are running optimally.

Acceptance testing approach

The acceptance testing approach can be used in data warehouse testing to verify the completeness of different data models to ensure they meet the reporting criteria of various projects. Acceptance testing typically involves reviewing table and chart designs and upload processes, validating input data in a production data warehouse and analyzing application reporting.

Learn more: Guide To Quality Assurance

Data warehousing example

A good example of integrating data warehousing into business analytics can be evident in the healthcare industry:

In this example, let's assume an outpatient surgery center is transferring aggregated data into a single data warehouse system. Some of the data the facility integrates might include financial information like insurance and billing reports, patient medical data, medical procedure data, medical supply data and scheduling data. A data warehousing specialist is contracted to build and implement a system to increase the efficiency of the surgery center's processes.

The specialist might integrate various levels of data warehouse testing to ensure the system performs at an acceptable operational level. The specialist might perform various tests including quality assurance testing, regression testing and functional testing to ensure the system's success when the facility's staff performs query searches within the data warehouse. If the specialist finds any issues with the functionality of the data warehouse, changes can be made to improve the performance, quality and reliability of aggregating the facility's data into one warehousing system.

Integrating data warehousing in the outpatient surgery center can essentially streamline the staff's reporting procedures and improve the efficiency and organization of documenting patient treatments. The surgery center can also improve the way it stores and retrieves any financial data like insurance claims, remaining deductible balances and other billing statements.

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