Data warehousing and data mining are two crucial processes associated with compiling, organizing, and extracting useful data. In Data warehousing, data is compiled and organized into a common database, while in data mining, useful data is extracted from the databases.
This post will take you deep into the world of data warehousing and data mining to help you understand their difference. So, let us get started
Table of Contents
What is Data Warehousing?
The term Data Warehousing refers to the combination of several data pieces from various sources. Along with the compilation of data, there is a complete check on the quality, accuracy, and data consistency.
Multiple databases join hands to provide data that flows into the respective data warehouse. Moreover, the data stored in the warehouse is arranged in a specific layout and schema. It further enhances the definition of the type of data and uses it accordingly.
Data warehousing is the process that helps in decision-making by conducting data cleaning, data consolidation, and data integration. The procedure is completely digital, and the results are quick as compared to the traditional ways.
Data Warehouse Definition
A Data Warehouse is an environment where key data from different sources are stored in a particular schema. A data warehouse is a relational database that is used for reporting and analysis purposes of data.
Features of Data Warehousing
Data Warehouse is made mainly for the query and analysis instead of transaction processing. A Data Warehouse is generally made for supporting different management related management functions.
The trend of data warehousing has changed a lot in the past few years. All the users have started knowing the importance of the need to handle data properly. Therefore, it is important to have a look at some of the key features of Data Warehousing.
1. Collaboration
Analysis of data is not an easy job, especially if it sources from different locations and types. The process mainly focuses on integrating data from non-homologous sources. This eventually boosts up the data analysis efficiency in the long run.
2. Time-Variant
Data is incomplete without a precise timestamp available on it. This helps in determining the timeline during which the data set was uploaded in the data warehouse. This assists with the historical view while managing data.
3. Subject Oriented
Data warehousing depends on a particular orientation that is considered a key feature. It concentrates on several subjects such as revenue, sales, advertising, etc. The process of data analysis gets huge assistance due to the same.
4. Non-Volatile
The data present in the warehouse is non-volatile. In other words, the previous data is not erased after updating the new data sets in the system. Other operational databases are undoubtedly prone to frequent changes.
Benefits of Data Warehouse
Some of the notable benefits of a data warehouse that you need to pay heed to are-
- A Data warehouse is consistent and offers quality data
- Data warehouse plays a key role in cost reduction
- Data warehousing offers more timely data access
- A data warehouse ensures optimized performance and productivity
What is Data mining?
On the other hand, Data Mining is the process of analyzing a huge amount of data with the help of various tools in the computer system. It is used for extracting useful information as well as patterns from the data.
You can do data mining on any traditional database, however, a data warehouse has data of top-notch quality, it is suggested to perform data mining over the data warehouse system. It is crucial in supporting knowledge discovery, as it finds hidden patterns and associations, designs analytical models, and conducts classification, and prediction.
Data Mining Definition
Data mining is a computer-supported process used for the analysis of data. Data mining techniques analyze huge sets of data compiled by computer systems. When you run a data mining process, your computer will analyze the data and extract useful information from it.
Calculating data sets without specialized digital tools is a tedious job. There are two different methods in which data analysis can be conducted. The first one being the situation where the computer system itself does data mining.
On the other hand, data can be downloaded on a separate computer. The computer tools examine the data and sorts out the useful information present in it. The hidden patterns are also surfaced using special data mining methods.
Features of Data Mining
Data mining techniques look for the hidden patterns within the data set and try to do future predictions. It helps in discovering and indicating relationships among the data sets.
It is important to understand the features that make data mining a priority for many successful businesses. So, let us have a look at features of a data mining process-
1. Manage Datasets
Data mining can manage huge databases easily and generate appropriate results from them. The common trends between the data can be highlighted, and develop new relationships between them.
2. Prediction
The data is mostly used to predict the obvious outcomes by analyzing the previous results. Data mining is considered to be quite effective in this job as it can manage a large chunk of information with ease.
3. Business Enhancement
Several businesses are dependent on data mining to calculate their behaviours in the market. This will make them aware of the strategies that they might feel are not much effective.
4. Large Calculation
There is no doubt in the fact that mathematical calculations are prone to several mistakes. Data mining is dependable in these situations as the computer itself does everything.
Benefits of Data Mining
- Data mining is crucial in direct marketing by predicting who is most interested in your products
- Mining delves into large data sets and does effective trend analysis
- Mining data also helps in fraud detection of large data sets
- Data mining assists in effective forecasting in financial markets
Key Differences Between Data Warehousing and Data Mining
The aforementioned analysis of data warehouse and data mining would have helped you understand all the major and minor aspects of Data Warehousing vs Data Mining.
Their working procedures are different from each other in several ways. A handful of the key differences between data warehousing and data mining are mentioned below-
1. Collection & Extraction of Data
Data Warehouse is collecting the data files at a single location and managing them for future reference. Whereas Data Mining refers to process data to extract useful information from it. The unknown patterns available in the data can be highlighted and make the calculation easy.
2. Data Warehouse Vs Data Mining on the grounds of usability
A Data warehouse lets an organization have a mechanism to store a huge amount of data while on the other hand, data mining is used on a data warehouse for finding out useful patterns.
3. Sequence of using Data Warehousing and Data Mining
Data warehousing should be used before data mining as it helps in compiling and organizing data into a common database, while data mining needs compiled data for its extraction into useful patterns.
4. Users
Data warehousing is the process that should be channelized by engineers while data mining is carried out by entrepreneurs, business owners, etc.
When should you choose data warehousing?
Many users prefer a Data warehouse over a regular database that makes data management more effective. It is advisable to use data warehousing if the data source is not required in the later stages.
Moreover, the process is quite efficient in collecting data from heterogeneous sources. It also comes in handy to separate the transactional data from the available analytical data. The tool will reduce the workload on the user and will entertain the issues by itself.
Eventually, the overall performance of the analytical choirs will experience a sudden hike. In the end, this is the best way to have the important data secured for future reference.
When should you choose data mining?
As the term Data mining comes into the picture, several situations can be tackled using this data management structure.
Large companies can easily calculate their revenue and customer database in a single go. If there is a need to manage and extract information from large data, data mining is the most viable option. Moreover, a market campaign can be based on the results generated by the tools used in data mining.
Overall, there is no harm in getting to know more about a business’s progress in the market. Data mining can be a useful tool to have an overview of implemented strategies.
Summing Up!
On the concluding note, all your doubts about data warehousing vs data mining would have been cleared. Data mining and data warehousing processes are two different techniques used for utilizing and optimizing data usages for making favourable business decisions.
From storing data to pattern recognition to analytical processing, fraud detection, and future predictions, these two are inevitable for contemporary businesses.
What are your differences between data warehouse and data mining?
