Applications of Data Warehousing and Data Mining

In recent years, data warehousing and data mining are not new concepts as big industries have a lot of issues with data manageability and complexity. These are updated day-by-day, providing outstanding facilities to their users. Due to the eagerness for data in real life, it is essential to design and implement applications of data warehousing and data mining in different areas of life. They have attracted a great deal of interest in real-life applications, especially in banking and finance, business, healthcare, and many more.

In this article, let’s crack down on some fundamental differences between data warehouse and data mining, along with their applications.

What is Data Warehouse?

Since the 1980s, it has been designed to support and enable the activities of BI (business intelligence), especially analytics. It helps to maintain historical data that can be used for future predictions, trend analysis, and informed decisions. In a data warehouse, the extensive collection of data comes from sub-departments, which include finance, marketing, and sales, along with other external systems. Its analytical capabilities help to derive more improved decisions and provide a deep understanding of business. Due to these capabilities, it is considered a company’s “single source of truth”. No one can deny the fact that data warehouses are deeply rooted in every industry where structured or unstructured data is involved.

Types of Data Warehouses

Three main types of data warehouses

• Enterprise Data Warehouse (EDW)
• Operational Data Store (ODS)
• Data Mart

Enterprise Data Warehouse (EDW)

An enterprise data warehouse is a centralized warehouse that contains an organization’s business data as well as clients’ confidential information. The EDW has analytical capabilities that provide worthy and actionable business insights. Actually, EDWs are not only a collection of databases that provide a structured approach, but it also help organize random data automatically, thus saving crucial time for the organization. So, organizations and other business sectors seem to be in a better position in the future as their data is organized in a highly systematic and automated fashion.

Operational Data Store (ODS)

An operational data store (ODS) has been defined by Imhoff and Inmon (1996) as a volatile, integrated, subject-oriented, and valued data store. The ODS is a kind of document database that contains historical information as well as associatively recent data and also aggregated data. It is also considered a complementary component of an enterprise data warehouse used for decision-making and operational reporting.

Unlike an EDW used for tactical and analytical decision support, an ODS works in real-time and is considered more appropriate for routine activities like storing employees’ salary information.

Data Mart

A data mart is a subtype of data warehouse oriented to maintain a particular region, department, or business line. It ensures to provide of essential data more quickly and in a defined way with deep understanding. This subject-oriented data mart enables the organization to get their desired data in no time without wasting time searching through the entire warehouse. ODS stores data periodically in a data mart, and then EDW receives data from ODS, where it is stored, and organizations use it as per their need.

Data mart has further three types: independent data marts, hybrid data marts, and hybrid data marts. These three types are recognized on the basis of data sources that are used to design systems and their relationship to the data warehouse.

What is Data Mining?

Data mining, or knowledge discovery in data (KDD), is the process of uncovering valuable data from multiple sources to organize deep insights. In technical terms, data mining is the process of analyzing and managing data from different dimensions, aspects, and angles and then categorizing it into worthy and meaningful information.

The overall process includes the analysis of historical datasets and extracting new data from pre-existing datasets. The highly effective algorithms of data mining allow organizations to convert raw data into valuable data by categorizing complex datasets, following the latest trends, and predicting future outcomes. It can be used for many data types, such as relational databases, spatial databases, data warehouses, the World Wide Web, and time-series databases.

How does it Work?

The process of data mining involves several essential steps, from data collection to unpacking meaningful information from large datasets. Data experts define data through the observation of trends, correlations, and associations.

Read out the following steps to explore the process of data mining that changed the industry and continue to do so.

• Set the business goals: This is the most important but the most challenging step of data mining because companies do not take it seriously. It is a need of the hour that business owners and data scientists spend some extra time to identify problems. This will help to organize the parameters and data questions for the desired results. Experts also suggest additional research for the understanding of business pitches and context.
• Data preparation: Once the problem is identified, the next step is to prepare the data to be mined. It is a comparatively easy step because it is easier for scientists to know which type of dataset will provide a relevant answer to the business questions. After collecting the data, all noise, such as missing values, repeated information, or outliers, will be removed in order to clean the data. Once the data is cleaned, data transformation is also done unless the expert wants to analyze the unfiltered data for specific purposes.
• Data mining: Data experts may examine the exciting relationships between data, such as association rules, sequential patterns, and other correlations. High-frequency patterns contain more comprehensive applications; sometimes the deviations make the data even more attractive by highlighting fraud ranges.
• Evaluation and Implementation: Once the data is cleaned and analyzed, the results have to be evaluated as per the given commands and business questions. The acquired data should be precise, practical, valid, and of course, understandable. When organizations get the required data, they can use it to implement profitable strategies and for their intended goals.

Applications of Data Warehousing and Data Mining

Data warehouse and Data mining have already been trendy in all industries as it helps to analyze large databases. Instead, it would be too messy and complex.

Without further ado, here are the applications of Data warehouse and data mining

Application of Data Warehousing

Let’s take a look at the beneficial applications of a data warehousing that make our life much easier.

Education and Government Sectors

As it manages complex data, the government uses it to analyze and maintain policy records, tax records, and military records. The state’s data warehouse is connected to the criminal law database so that all illegal activities are analyzed by following the trends and patterns of historical data.

In the education department, all institution’s financial departments are totally dependent on a data warehouse, and this dependency reduces the need for a workforce. Universities also use the warehouse for management of human resources, demographics of students, and also for information on deserving students’ scholarships.

Banking and Finance

In the banking industry, bankers and other staff manage their resources due to data warehousing solutions. It can help to analyze government reports and regulations, customer data, market trends, and, most of all, facilitate decision-making. Almost all banks use the perfect data warehousing solution for analysis of market research and to design marketing campaigns.

The applications of data warehousing are similar to those in the banking industry. It involves the evaluation and analysis of customer expenses that enables them to aid in earning the maximum profits at both ends. This solution also helps maintain transparency in business dealings and analyzes various aspects of stocks and businesses. 

Retailing

Actually, retailers are the intermediaries between manufacturers and consumers, and that’s why retailers need to maintain the records of both ends. Here come the applications of data warehousing as it helps them to store and manage complex data in an organized manner.

Through the help of a data warehouse, it tracks its items, analyzes buying trends in the market, and advertises campaigns and promotions. Their purchasing, selling, and shelf line of products are totally dependent on a data warehouse.

Telecommunication Industries

Telecommunication industries work in both modes, online and offline, and are troubled with the previous data that has to be analyzed and integrated. So, in this case, a data warehouse is the best way to lighten up the telecommunication industry from handling massive and complex data as it helps in achieving higher profits and competitive trademarks.

 Unlike the traditional warehouse, DW platforms provide the facilities of analysis of client calling trends for sales, analysis of fixed assets, and interacting with customers to answer their queries.

Manufacturing and Distribution Industry

Manufacturing and distribution are essential for a country’s income. They are closely related to the tech department along with other crucial departments like finance, sales, material searching, and product distribution department. All these departments specifically need highly-advanced IT systems.

This industry has to take several do–or–die steps that can make or break the industry, so that’s why they use OLAP (online analytical processing) tools to predict the market trends and view and analyze current business scenarios in the community. Automatically, this leads to vital decisions.

Applications of Data Mining

Education

Educational institutions have started to benefit from data mining in understanding the student population and their performance at the time of enrolling. So, that management will develop suitable strategies and an appropriate student environment. Due to the advancement in technology, almost all courses are going to transfer to online platforms, and they use several aspects and dimensions to relate to traditional classrooms in terms of assessment and evaluation.

Here are some educational tasks; we can perform with the help of EDM (Educational Data Mining).

• Predict the students’ performance for higher studies.
• Performance of teaching staff
• Evaluate students’ profiles for scholarships.
• Curriculum development

Manufacturing and Marketing

Data mining plays an essential role in manufacturing units as it analyzes the need and cost of raw materials, material types, availability, etc. It also estimates how much time is needed for manufacturing the products by analyzing the historical trends.

The same is the case with the marketing of the product. Data mining makes marketing campaigns more effective. The marketing team better understands their target audience, digital platforms, and their marketing deals like two-in-one just because of data mining.

Banking and Finance

Banks and other financial companies use data mining tools to detect potential fraud, investigate credit and loan applications, and develop economic risk models. It also helps companies to control market risks and get the maximum profits on their investments. The advanced algorithms provide the opportunity to understand their customer’s needs and also analyze millions of transactions in the financial systems.

Telecommunication Industry

The telecommunication industry is growing fast with the revolution of the internet. Internet-associated techniques, especially data mining, help to improve the quality of their service by analysis and interpretation of data. Spatiotemporal databases’ research is essential in upgrading the telecommunication and information industries. The techniques like OLAP and outlier analysis can help in comparing information and also detect fraud risk, respectively.

P.S. Although the applications of data warehouse and data mining are most important, we may observe that data warehouse applications have deeply rooted in almost all aspects of life, such as the transportation industry, entertainment, hospitality industry, insurance, etc.

Final Verdict

Data is the key to every industry, as companies covert every data into their desired profits. The applications of data warehousing and data mining helps the organizations to understand the business insights of the following patterns and upcoming trends of the market and maintain existing customers or gain new ones. This all is very frustrating and impossible without the services of data mining and a data warehouse.