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Data Analytics - May 11 2021

How Do Data Warehousing and Data Mining Work Together?

Data warehousing and data mining are both tools or techniques for business intelligence. While both play a role in analyzing data and generating insight, the processes and methods involved make them very different.

In the context of an overall business goal, data warehousing and data mining work together to create a cohesive, versatile workflow for in-depth data analysis and interpretation.

Are data warehousing and data mining the same?

While both data warehousing and data mining deal with processing data, they are fundamentally different in terms of the methods and processes involved.

Data Warehousing

Data warehousing is the sum of collecting and storing data. It includes collecting and storing data from various disparate sources for storage at a central location. Experts who work with data warehousing consider the physical and conceptual relationality of data from different sources, then determine how to analyze and extrapolate from it.

Another important part of data warehousing is the architecture of the data storage. Data from different sources need to coexist in a data warehouse, even if the sources are expressed in different fields and schema. It is the task of data warehouse specialists to create an architecture that can maintain the relationality and relevance of the disparate data, while storing it in a way that facilitates easy processing and manipulation.

Data Mining

Data mining, on the other hand, refers more directly to the process of data analysis. In essence, businesses mine the data in order to find the value in it, to process it to generate business insight. Usually, this process involves breaking down and categorizing data in a meaningful way, so that standard analytical procedures can be carried out with it. Experts create a consistent schema, establish important patterns in the data, and create visualizations that can communicate insight.

How do data mining and data warehousing work together?

Data warehousing and data mining work together because both are sequential and essential steps of making use of data. Data warehousing deals with meaningfully collecting and storing data in a central location, while data mining deals with analyzing that data. This means that, for the most part, the success of data mining depends squarely on how well the data warehousing part of the process goes.

In order to make the process of data mining fast and efficient, the data needs to be properly warehoused. For example, if the data being mined sits on a few different physical networks or in different locations, the process of mining can take longer and involve more complications. 

Similarly, if a data analysis process has to analyze very large volumes of data that are spread across multiple databases, and may be in multiple formats, the process can be inefficient.

Therefore, in essence, the data warehousing process is meant to make the process of data mining easier, smoother, and more efficient. When the data warehousing process can easily access and process data, then the data mining process can be faster, simpler, and yield more accurate results. The end result is the generation of accurate business insights, which can be a tremendous competitive advantage in creating the right plans and strategies for your company, going forward.

Have questions? We help companies like yours, every day.

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Read More

Should My Business Use Open-Source Data Integration Tools?
The Enterprise Guide to Integrating Multiple Data Sources

 

About NextPhase.ai

NextPhase.ai is a cloud data management and analytics services provider. For 10 years, we’ve helped global companies harness the power to turn data into insights that drive growth. Whether you’re migrating to the cloud or implementing a cloud data warehouse, contact us to schedule a workshop.

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