What is a data warehouse?
Data warehouse explanation
A data warehouse is a data management system that works to support business intelligence (BI) activities. It acts as a centralized location that aggregates and stores structured and semi-structured data from a variety of sources for easy analysis.
How does a data warehouse work?
A data warehouse collects data from multiple sources, cleans, organizes, and stores it, and then allows users to query its data set to answer business questions.
As you can see in the image below, a data warehouse takes different data sources like external data and sales or marketing databases and processes them in a way that cleans and organizes the data.
The data warehouse then stores the data from each source in the following forms:
- Metadata is information that defines the data, allowing analysts to classify, locate, and direct queries to the data.
- Summary data is highly summarized data generated by the warehouse manager to speed up query performance.
- Raw data is data that hasn’t been processed and is being loaded into the data warehouse.
Marketers can then use the data in the data warehouse to create business-specific reports, perform deep-dive analyses, and provide predictions (data mining) to ask questions like “Which factors are influencing my app’s user retention rate?” or “What will be the expected number of app downloads in the next quarter?”
What is the primary purpose of a data warehouse?
The primary purpose of a data warehouse is to enable companies to analyze and generate reports on all of their data to gather business insights. A data warehouse makes it easy to examine vast amounts of data, including historical data. This helps companies monitor trends over time and forecast future outcomes, particularly when used in media mix modeling (MMM).
How to build a data warehouse?
A data warehouse architect builds data warehouse solutions by identifying a business’ needs, determining the data sources needed to meet these needs, and then constructing a data warehouse architecture based on these requirements. As needs and data sources vary significantly from business to business, many different data warehouse designs, sizes, and types exist.
A typical data warehouse includes the following elements, according to Oracle:
- A relational database to store and manage data
- An extraction, transformation, and loading (ETL) solution that preps data for analysis
- Reporting, data mining, and statistical analysis capabilities
- Visualization tools to present insights
- Other analytical applications using artificial intelligence and data science algorithms, etc.
Benefits of a data warehouse for app marketers
App marketers use data warehouses in tandem with a mobile measurement partner (MMP) or other analytics solutions to gather campaign performance insights and then make informed decisions.
The top benefits of a data warehouse are:
- Enhanced business intelligence
- Greater efficiency in processing mass amounts of data
- Better data quality management
- Storage and use of historical data for long-term strategizing
Adjust and your data warehouse solutions
The best part?
Unlike other MMPs, Adjust doesn’t charge you for extra data pulls. We believe your data is yours, so we don’t limit access to your historical data. Having unlimited lookback windows when sending historical data from Adjust to your data warehouse can prove invaluable as you investigate patterns to strategize for your app’s long-term success.
To learn more about how Adjust’s robust analytics platform can power your app marketing, request your demo now!
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