What is data warehousing and data mining? What is the difference between data mining and data warehousing? These processes can be confused, but there are key differences between data warehousing and data mining techniques. Here’s what you need to know about data mining vs. data warehousing.
What Is Data Warehousing?
Data Warehousing is just like it sounds: the place where data is stored before it is analyzed and used. Data warehousing also includes the process of aggregating data from various database sources into one place for efficient access and analysis. Another aspect of data warehousing is the architecture of the data—that is, how it’s structured so that it can be joined, even if the sources have different fields and schema. As a record of a company’s past operational and transactional information, especially given the proliferation of data sources, volume, and density, data warehousing is a key strategic differentiator in today’s global marketplace.
What Is Data Mining?
Data Mining, like gold mining, is the process of extracting value from the data stored in the data warehouse. Data mining techniques include the process of transforming raw data sources into a consistent schema to facilitate analysis; identifying patterns in a given dataset, and creating visualizations that communicate the most critical insights. There is hardly a sector of commerce, science, or technology that isn’t using data warehousing and data mining techniques to accelerate its objectives. From fraud detection and inventory management to cancer research and marketing campaigns, data mining is changing decision making around the globe.
Cooking With Gas: How Data Warehousing and Data Mining Work Together
Before you can cook, you must go to the grocery store, but you will also use items from your pantry. Data warehousing is the process of taking all those new groceries and organizing them in the context of your pantry, before you even know what you will cook. Once your ingredients are prepared in the data warehouse, you can begin to cook, or start your data mining. Together these two processes—data warehousing and data mining techniques—work together to create a warehouse of data and extract valuable insight from it. The trouble occurs when the step in between data warehousing and mining is skipped, and analysts jump straight to processing the data.
With an incomplete, messy, or outdated pantry, you might not have the baking powder for perfect biscuits, and so it is with the relationship between data warehousing and data mining. A great cook needs a well-organized pantry, and a great data analyst needs well-organized data structured in a way that allows for efficient insight. Without it, like most analysts, they’ll spend 80% of their time organizing the pantry, instead of focusing on their cooking technique—data mining. The step of structuring and cleaning the data is crucial before analysts move onto data mining techniques. With a well-organized database or “pantry” following the data warehousing stage, analysts are better able to extract valuable information with data mining techniques.
The Designer Cloud Solution for Data Warehousing and Data Mining
Data warehousing and data mining techniques are important in the data analysis process, but they can be time consuming and fruitless if the data isn’t organized and prepared. Data preparation is the crucial step in between data warehousing and data mining. Once the data is stored in the warehouse, data prep software helps organize and make sense of the raw data. When the data is prepared and cleaned, it’s then ready to be mined for valuable insights that can guide business decisions and determine strategy.
With an orderly data warehouse, and a well-honed data mining tool like Alteryx Designer Cloud, every analyst regardless of technical skill can now focus on the work they were trained to do: provide meaningful, transformational insight for the business or organization. With intelligent data transformations, automatic data visualization and easily repeatable and shared components, Designer Cloud has helped organizations big and small fulfill the promise of their investment in data warehousing and data mining operations.
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