Difference Between Data Warehousing and Data Mining? Details Inside

“Torture the data, and it will confess to anything.” — Ronald Coase

This quote emphasizes a very real aspect of data, without which we would not be able to bring the data revolution in the world. Two terms often come to mind when thinking of data- data mining and warehousing. This article will elaborate on the difference between data warehousing and data mining to help you better understand these concepts. But before moving forward, it is crucial to understand which term comes first, data mining or data warehousing. So we’ll begin this discussion by looking into the details of what is data warehousing and data mining. We will then discuss data mining vs data warehouse.

What is Data Warehousing?

Data warehousing is an electronic way of extracting and storing a huge amount of data in a common database from varied heterogeneous sources, such as social databases, relational databases, level documents, etc. It is a periodic process which means that the data is being gathered & stored in a repeated process. Data Warehouse is a relational database that is designed for query and analysis rather than for transaction processing. Understanding this core definition will help you grasp the difference between data warehousing and data mining. We will further look at the process involved in data mining and warehousing, and then point out the key differences between the two.

Why Data Warehousing?

It gives much easier and more accurate access to data which helps business organizations to make data-driven decisions.
It improves productivity and performance using market trend analysis techniques.
It is a cost-efficient process.
The data gathered are consistent, and precise future predictions can be made using the data trends.
Data warehouse improves system performance by separating analytics processing from transnational databases.

In simple terms, data warehousing solutions comprise a set of analytical tools that allow this stored data to be queried in order to derive insights and hidden trends that eventually drive business decisions that empower everything ranging from businesses to stock markets to healthcare, etc.

Processes involved in Data Warehousing

In the process of data warehousing, we extract essential data from multiple sources, all of which may or may not be relevant to each other. We then store it under a single schema,i.e., transform that data into a general look and size, and finally deliver it to the target location for further mining processes. This points to the fact that while data warehousing and data mining go hand in hand, they are not the exact same thing.

Key features of Data Warehousing

Subject Oriented: The data that we gather are always gathered keeping in mind the problem statement. This problem statement can be of any field, for example, business trends, customer feedback on any product, sales, distribution, marketing, etc. It provides useful data about a subject instead of the company's ongoing operations, i.e., these subjects can be customers, suppliers, marketing, product, promotion, etc.
Time-Variant: Time variance implies that the data that is being stored in the database gets organized in the form of time intervals, i.e., weekly, monthly, annually, etc. and pertains to certain time periods. The time in any data may be present implicitly or explicitly. This helps in analyzing the trends in the data properly.
Non-Volatile: As the name itself suggests that the data present in the database aren't temporary, rather they are permanent as well as un-alterable. The features such as update, erase, and insert are not there in the database.

This compiles all you need to know about data warehousing. In the sections ahead, we will look at the difference between data warehousing and data mining, after looking into the details of data mining.

What is Data Mining?

Data mining is the process of mining that involves analyzing data patterns, and applying pattern recognition logic, which is being carried out on the huge data sets, present in the warehouse. To perform the above-involved tasks, data mining tools utilize AI, statistics, database technologies, machine learning systems, and query tools.

Why Data Mining?

The mining techniques are very important when we want to target any customer for any product or service, hence bringing greater leads in businesses.
Their mining tools help in fraud detection, for example, they can help to find which cellular phone calls, insurance claims, and credit/ debit card purchases are going to be fraudulent, and hence report it beforehand to the customers for prevention purposes.
The data mining techniques can help while analyzing the current existing trend in the marketplace for providing strategic benefit because it helps in cost reduction and manufacturing process as per market demand.
Data mining techniques are widely used to help Model Financial Market.
Data mining also comes in handy during management and analysis of the market and corporate analysis- along with risk management and detection of impending fraud.

Process of Data Mining

The mining process involves a cycle of phases through which the data has to go, once the problem is defined. The phases include gathering the required data, analyzing it, preparing illustrative models, verifying the model with the problem statement, and finally getting useful Insights. Data mining has proved to be useful for knowledge discovery by finding hidden patterns and associations, constructing analytical models, and performing classification and prediction.

Key features of Data Mining

Automatic discovery of patterns: This means that through the discovering logic, we are able to automatically discover the trends present in our data set.
Prediction of likely outcomes: As we organize the data in the database are time-variant, hence it becomes very intuitive for the software to predict the forthcoming outcomes in the data.
Creation of actionable information: The information that is derived after mining is so well thought out and calculated that it becomes actionable to the organization.
Focus on large data sets and databases: Data mining results become more and more accurate and precise when we have large sets of data.

It is evident through the above elaboration that in the case of data mining vs data warehousing, data warehousing comes first. That is because the data mining process depends on the data compiled in the data warehousing phase to recognize meaningful patterns. Now that we know what is data warehousing and data mining, let’s differentiate between the two.

The Difference between Data Warehousing and Data Mining

In the table below we have prepared a concise data mining vs data warehouse comparison. Have look:

Data Warehousing	Data Mining
It is the first process of data processing.	It is the process followed just after data warehousing when a problem statement is defined.
Data is stored in a periodic manner.	Data is analyzed regularly.
It is just a process of pooling relevant data together.	It is a disciplined and organized process for extracting business insights from large data sets.
This process is solely carried out by engineers.	This process is being carried out collectively through business users and engineers.
Most of the work that will be done on the user’s part is inputting the raw data.	The data mining methods must necessarily involve efficient approaches to gather meaningful and useful insights.
The data warehouse is built to support management functions.	Data mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, and performing classification and prediction.

How are data warehousing and data mining correlated?

Data warehousing and data mining are correlated in the sense that both these processes are carried out for providing a solution to a problem statement, wherein warehousing is the first process, and mining is the second, and both have to work together for deriving the business insights.

If we don’t have the data relevant to the problem, then we cannot analyze them anyway. Similarly, if we have all the data but it is not precisely processed then it won’t turn out as significant information for the enterprise.

We do know that Data mining can even be carried out with any traditional database, but since a data warehouse contains quality data, it is good to have data mining over the data warehouse system as better input would result in better outcomes.

It must be noted that the data mining techniques are not 100 percent accurate and can lead to data leaks and piracy if not done correctly.

Summing Up

In layman's terms, we can say that in the process of data warehousing we collect data from various varied sources and put them all in the database, which after proper processing and analytical reasoning (i.e, data mining) comes out as a piece of very meaningful information or business insights. We hope that this gives you a clear idea of the difference between data warehousing and data mining.

Shreeya Thakur

Sr. Associate Content Writer at Unstop

I am a biotechnologist-turned-content writer and try to add an element of science in my writings wherever possible. Apart from writing, I like to cook, read and travel.