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Struggling to tell your APIs from your CDNs? Read our comprehensive cloud computing glossary covering the most common terms.
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A data warehouse is a very powerful and highly-consolidated library of data for a business. It stores all data relevant for a business to analyze for the purposes of searching, reporting, and decision-making. A data warehouse consolidates data in one place instead of duplicating data across multiple different systems; it is purposed designed to allow for very fast searching and decision-making, rather than simply storing data like a database.
The primary function of data warehouses is to have the capability of ingesting, storing and organizing large volume of data from varying sources, such as customer databases, sales histories, marketing collaterals, and financial systems. Once the data is ingested to a data warehouse, the data is cleaned, transformed, and structured, to allow for efficient analysis of the data.
Data warehouses differ from standard databases; like a standard database avoids Union operations on an SELECT statement, since it is optimized to be used with fast read or write operations (e.g., log into account); while a data warehouse is normally used when speed is needed to perform iterative analysis against lots of data; it is optimized to provide fast feedback time even against complex queries, for example, “What products sold the greatest volume last quarter?” or “What is our average customer retention rate by region?”
Data in a warehouse, for the most part is historical data, and therefore the data can represent changes over time. By taking an historical data approach, organizations can identify trends, forecast future behaviors, thereby allowing for better decision-making.
Centralized Data: With a data warehouse, all your scattered data is centralized in an organized environment and it’s easier to source it for reporting or analysis.
Performance: Data warehouses are structured to allow organizations to have fast processing capabilities on a high volume of data.
Improved Decision Making: The effort spent in cleaning and structuring the data means teams can generate reports and dashboards based on the data with much more confidence, and the team can make better decisions as a result.
Scalability: If your organization adds more data, data warehouses will grow as well. If you are using a cloud-based data warehouse, it’s even easier to scale.
Security and Control: Data warehouses allow you to control who has access to specific pieces of data. You can block sensitive information while still providing teams with the insights they need.
For example, consider a national retail chain. Each store tracks data points including sales, inventory, employee hours, and customer survey data. If each data set is managed separately, it’s going to be disconnected, and ineffective. If the data is structured into a data warehouse, all of these metrics from all stores can now be reported on.
For instance, executives can run reports on which stores are performing the best, what products were sold the most in any given area, or if a new marketing campaign led to increased sales. Without a data warehouse, this information would either not be available, or take forever to assemble.
When creating a data warehouse, you will need to invest time and thought. You will need to choose the best tools, figure out how frequently, if at all, you will need to update datasets, and always remember to keep your datasets formatted similarly. You will also want to build in processes for maintaining data accuracy and timely updates, otherwise, you may find yourself reporting on stale or inaccurate data.
Cloud-based data warehouses such as Amazon Redshift, Google BigQuery, and Snowflake have recently made the setup of a data warehouse accessible and powerful, all without the hassles of managing the physical server.
