Data Warehouse overview

Data warehouses store data that is collected for mining purposes where the storage is usually in a large capacity. The process in data warehouses is the collaboration of data from many sources to ensure accuracy, consistency, and quality (Data warehousing, 2022). Data is sorted into formatted patterns as needed and by type in a data warehouse and the system execution is boosted from traditional database by differentiating the process of analytics. Data is examined from using query tools that use several patterns.

There are several features of a data warehouse. It is integrated with several heterogeneous sources that are put together to build a data warehouse. The data warehouse is subject oriented in that it provides a user with important data about subjects like products and customers. This helps organizations make data-driven decisions because data warehousing handles the modeling and analysis of the data (Data warehousing, 2022). A data warehouse is nonvolatile which means that new data is added but earlier data is not deleted.

 A few applications of a data warehouse are the retail sector, manufacturing, consumer goods, and banking services. Data warehouses help senior executives organize, analyze, and use data to make business decisions. Data warehousing offers accurate data consistency and access. Data warehousing is cost-efficient and offers data quality as well as improved productivity and performance (Suer, 2021).

Data Mining Overview

Data mining is defined as a process where data is extracted and analyzed to provide useful information (Data warehousing, 2022). Future behavior is predicted by researching the hidden patterns in data mining, and the data is also used to discover and indicate relationships. Hidden patterns are found in data mining as it uses artificial intelligence, other databases, statistics, and machine learning systems. Data mining helps support queries related to business that can be time-consuming to resolve.  

 There are several features of data mining. For instance, it creates actionable insights, utilizes automated pattern discovery, and predicts future results. Data mining easily manages large datasets and databases. There are five major elements in data mining which are as follows (Data warehousing, 2022):

• It has a useful data format.
• It provides full data access to users.
• It manages and stores data in a multidimensional database.
• Data is analyzed by application software.
• Transactional data is extracted, transformed, and loaded into the data warehouse system.

Techniques of data mining include Gaussian Naïve Bayes, K-nearest neighbors, and Support Vector Machine (Data warehousing, 2022). These techniques are used to provide useful and appropriate information from datasets and to segregate different classes in the datasets. From these techniques organizations can practice fraud detection to weed out which insurance claims, credit card purchases, and phone calls are fraudulent. Organizations can see an existing marketplace trend which helps reduce marketing costs because the organization will know where to best focus their marketing campaigns. 

Now that a data warehouse and data mining have both been defined, and features, techniques, and advantages have been listed, the rest of this document will be focused on an example of a data warehouse and how data mining works within it. An example will be listed showing a data warehouse and how data mining works and the techniques it offers shown.

How Data Warehouses and Data Mining Work Together

Role of Data Warehouses in Data Mining 

Data warehouses are important utilities for data mining and bring specific advantages to data mining such as unity of data, historicity, responsiveness, and preservation of insights. Data warehouses consist of a data storage system that offers data mining the ability to perform query performance on large and varied data sets. Listed below is more information about the advantages that data warehouses offer data mining (How do data warehouses , n.d.):

1. Historicity – a primary way data warehouses achieve data unification and responsiveness is with temporally- organized data series. Historical trends are easier to find, along with giving unrelated data a common context, by uniting data on a common timeline.
2. Responsiveness- data warehouses allow industrial-strength queries that unblock bottlenecking that will allow faster query results. Data mining algorithms require precise data.
3. Preservation of insights- data warehouses offer the feature of storage stability and organization of disordered data while retaining the output of data trawling from data mining tools. A data warehouse provides a full-spectrum view of the data mining process because it unifies the source of data mining and output results.
4. Unity of data- data warehouses contain assortments of data from many sources and placing it in a single location. It is due to this that makes a data warehouse perfect for machine learning algorithms. This makes integration of analysis and input easy for an analyst.

Data warehouses bring several advantages to data mining. A data warehouse makes large volumes of convoluted data much easier to manage as it reduces algorithmic analysis difficulties. A data warehouse contains query schedulers and queues designed to prevent system lockups during intensive queries done during data mining. The advantages that a data warehouse offers to data mining is appliable to any type of data mining operations.

Data Mining Within the Data Warehouse

Data mining in a data warehouse can be compared to physical mining because miners use heavy machinery to extract materials by breaking up rock formations. A data warehouse is the machinery in data mining because it pulls in raw data from several sources and stores it in a standardized, cleaned form ready for analysis (Data mining 101, n.d.). Data mining follows precise techniques to sift through raw data to find insights that would help organizations to make good business decisions.

While the data warehouse collects data, transforms, and organizes it into a standard structure, it also optimizes the data for analyzing and processing. Data mining analyzes data looking for relationships and insights and unknown patterns. In an organizational role, data warehouses do the "plumbing" part of data mining that is done by IT or data engineers. Business analysts or data scientists perform the data mining part as they will understand the data analysis.

As far as objectives in the business are concerned, data warehouses provide the business with a reliable data source that will provide the business with several types of analysis. Data mining is to provide the business with insights that it might not have easily seen already.

There are three data mining principles. Those three principles are valid data, information must be previously unknown if it is discovered, and the information needs to be actionable or interesting (Data mining 101, n.d.). To make sure the data is valid, data scientists or professionals need to aim to make the best possible fit between analyzed data and the model. Data scientists or professionals should look for information that is not intuitive as the further from obvious it can be, the more it is valued. Businesses need to find structures or patterns that are more interesting for their business goals that are based on statistical measures.

Example Analysis: Data Mining in Retail

 An industry that generates a large volume of data is the retail industry. A lot of information is gathered on client purchasing patterns and sales. Retail data mining is used to track customer activity and to find purchasing trends and patterns, increase the happiness of the customers, boost product consumption rates, and enhance customer service (Bhattacharyya, 2022). Retailers have also discovered that data mining provides them with a competitive advantage. They found that data mining helps them make proactive decisions.

Retail Data Mining Applications

Retailers have been successfully applying data mining for many years now. They have found the benefits outweigh any data mining costs. Below is a list of areas in a retail business when retailers have used data mining (Bhattacharyya, 2022):

1. Market analysis – data mining finds affinities between various items. For example, if a customer buys a particular item, they will likely buy this other item. Retailers then know more about where to market certain products, and to whom. Retailers also can see where to direct sales campaigns.
2. Customer Relationship Management (CRM) – CRM focuses on keeping consumers as well as attracting them. Retailers use CRM to provide the best customer service and focus on the customers’ product demands with the mindset that happy customers equal more profit.
3. Customer retention and acquisition – retailers use this to keep customers by studying their shopping and purchasing habits. They can also see if a customer left them to go to a rival and use this to keep present customers from doing the same thing. Retailers also target customers with promos of products they will likely buy.
4. Risk minimization – retailers use data mining to identify what products are susceptible to marketing risks and any customer purchasing pattern shifts, Retailers then know what products not to target customers with.
5. Minimize fraud cases – retailers use data mining to detect fraud at the point-of-sale. An alleged transaction can be extracted by data mining to see what exactly happened during the transaction.

Retail is one of the industries where data mining is extremely helpful. There is a lot of data about sales, movement of products, customer purchase patterns, and consumption. Because of the internet and websites, more data will be collected and at a rapid capacity.

Retail, Data Warehousing, and Data Mining

The process which data is collected and stored before evaluation is called data warehousing, and data is collected from several sources and place in a common archive before its used for business analysis (Data mining 101, n.d.). This is a process that occurs before the data mining process. There is a three-stage process known as extract, transform, and load (ETL) that data goes through before being loaded into a data warehouse. The data extract process involves data being copied and moved from whatever source into the warehouse staging area and it can be structured or unstructured data. The data transform process is when data is cleaned, validated, filtered, and any errors are removed. It is during the transform process that data is formatted to fit the warehouse. In the data load process, the transformed data is uploaded in the data warehouse. Whenever data is updated, the three-stage process starts over again.

Retail businesses find the ETL process very helpful in cleaning and validating data before analysis. Retailers know that any technique they wish to use to analyze the data will produce the best results. They can view customer purchasing behavior and design a marketing strategy that will best reach all of their customers while addressing their product needs. When customers are happy, the retail business gains more profit. In today’s times, customers want to be able to find products they need easily. When products are easy to find, the customer is likely to continue shopping with that retailer.

In conclusion, data mining has proven useful in all industries. Data warehouses are needed when large volumes of data exist and businesses need a central place to draw data from in order to make good business decisions through data mining techniques. The three-stage process ETL makes data validated and clean which makes it much easier for businesses to perform data mining and get an accurate analysis.

References

Bhattacharyya, S. (2022, July 29). 8 applications of data mining in retail. Analyticssteps. https://analyticssteps.com/blogs/8-applications-data-mining-retail

Data warehousing and data mining 101. (n.d.). Panoply. https://panoply.io/data-warehouse-guide/data-warehousing-and-data-mining-101/#:~:text=The%20data%20warehousing%20stage%20involves,unknown%20patterns%2C%20relationships%20and%20insights

How do data warehouses enhance data mining? (n.d.). Rudderstack. https://www.rudderstack.com/learn/data-warehouse/how-do-data-warehouses-enhance-data-mining/

Suer, M. (2021, August 5). What Is data quality and why Is It important? Alation. https://www.alation.com/blog/what-is-data-quality-why-is-it-important/#:~:text=Without%20accuracy%20and%20reliability%20in,conclusions%20based%20on%20those%20findings.

Data warehousing and data mining. (2022, February 2). Topcoder. https://www.topcoder.com/thrive/articles/data-warehousing-and-data-mining