A data warehouse is a digital environment for data storage that provides access to current and historical information for supporting business intelligence activities. Consequently, data warehousing is the process of periodically archiving and reshaping data for business intelligence purposes. 

We can use a data warehouse to store user interactions and user journeys from a website or application. Storing and organizing information in this way can help stakeholders make data-informed decisions about changes in a product. Within any data warehouse we can expect to find customer lists, product data, mailing lists, sales volumes, sales forecasts and any other metrics considered valuable for a business. For example, we can use machine learning to create a sales forecast and store it in a data warehouse; later, data professionals can visualize the stored forecast using a business intelligence tool.

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Why Is Data Warehousing Important?

Data warehousing is a crucial aspect of data analytics and business intelligence — disciplines that enable stakeholders to access company insights in order to improve their data-informed decision making. The process of data warehousing allows companies to build a historic repository of fine-tuned data for analytics purposes such as product performance, feedback on product updates, sales forecasts, the popularity of certain features within a product and more.

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Data Warehousing Example

Here’s an example of the data warehousing process: An ETL (extract, transform, load) pipeline that runs SQL and NoSQL queries on a variety of sources receives all the information and reshapes the data into several structured tables within a cloud-based data warehouse, such as Google’s Big Query or Amazon’s Redshift. Subsequently, you can integrate these data warehouses with a business intelligence software tool such as PowerBI or Looker to provide data visualization and insights. Ultimately, you can use these insights to make stronger data-driven decisions and monitor the success of changes within a product. For example, we can track how many clicks we get per day on a web component before and after a design update; we can use this information to infer whether our customers are receiving the update positively or negatively.


How Does Data Warehousing Work?

The process of data warehousing starts with the streaming of data from one or multiple sources. These sources might include front-end tracking from a website, external REST APIs, back-end tracking from a mobile application, data streaming from an external cloud service or any other compatible data source. This data passes through a process known as ETL, which reshapes and merges data from multiple data streams into a consistent format. A data warehouse receives this processed data and stores it in multiple databases with predefined schemas. A preset schema is a key element of the structured databases and allows for easier data querying. The warehousing process occurs periodically to build a historic data set and to provide businesses with useful insights such as sales volumes, best performing products and peak hours for web traffic.


Data Warehousing Risks

There are a few risks associated with data warehousing. For one, errors in data sources and ETL pipelines can corrupt the data’s integrity. Receiving unexpected data or a coding error on the codebase in charge of user tracking can cause a data pipeline to fail and create data outages. We can also face issues when similar data come from multiple sources.  This could cause us to have inconsistencies amongst our data sets.

For example, when implementing front-end tracking on a website we can track only those users who accept the tracking cookies. This could mean we end up with one data set built from front-end tracking with more detailed individual user data while missing some user events completely. Our other data set could come from our back end with complete information on every user event, but less specific user data.

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Data Warehousing Benefits

While there are risks associated with data warehousing, on the whole the benefits outweigh the costs. Data warehousing can help us easily integrate with business intelligence products such as Looker or PowerBI, which are built for an easy and swift integration with data warehouses. These tools allow us to build reports that showcase our data visually through charts, graphs, histograms and tables.

Data warehousing also allows for high-performance data interrogation. In practice, this means the process of data warehousing can reshape data from multiple tables and store it in a data warehouse. Instead of joining multiple tables in a query, we can perform a faster query without SQL join statements and by using a structured table in our data warehouse. Finally, data warehouses have a preset schema, which means they enforce a data structure and thereby favor integration with data analytics.

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