In today’s world, businesses focus on gathering more data to extract valuable insights from and optimize business processes. In short, incorporating Data Integration in Data Mining help businesses collect data efficiently, extract insights, and understand their customers by analyzing commodity purchase data, and new behavior nudges.
Today, we augment business capabilities by leveraging standard SaaS applications. With the help of Data Integration in Data Mining, companies can integrate all their business data stored in several Databases.
In this article, you will learn about Data Integration and the role of Data Integration in Data Mining. You will also read about different approaches and techniques used for Data Integration in Data Mining. An organization needs to stay ahead of its competitors and make smarter business decisions.
Table of Contents
What is Data Integration?
Data Integration is a process of combining data from multiple sources in one place to provide a holistic view of the data. It is an essential process in both domains, the commercial domain where there is a need to merge two similar Databases, and the scientific domain for combining data from multiple bioinformatics repositories. Data Integration aims to make data freely available and easier to consume and process by users and systems.
Correct implementation of the Data Integration process can help companies bring down IT costs, improve data quality, and free up resources. enhance changes and innovation without affecting existing applications or data structures. Data Integration comes into the picture when there is an increase in the frequency of data volume and the need to share the existing data becomes essential for businesses.
Advantages of Data Integration
Some of the key benefits of implementing Data Integration in business are listed below:
- Data Integration reduces the need to manually transform and combine data sets which significantly increases operational efficiency.
- Data Integration improves data quality by automating the data transformations that apply business rules to data.
- Data Integration provides a unified view of data that allows companies to analyze data easily and generate valuable insights from it.
What is Data Integration in Data Mining?
Data Integration is a process of merging data from multiple data sources. Data Integration in Data Mining is a record preprocessing method that involves merging data from heterogeneous data sources into coherent data to provide a unified view of the data. These data include several Databases, record cubes, or flat documents. The statistical strategy in Data Integration in Data Mining is formally stated as a triplet of the (G, S, M) approach, where G is a global schema, S is a heterogenous source and M represents a mapping between source and global schema queries.
You have to deal with several issues such as Data Redundancy, Inconsistency, Duplicity, and many more. One of the most well-known implementations of Data Integration in Data Mining is building an enterprise’s Data Warehouse. Data Warehouses allow organizations to perform analysis on the data.
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Why Data Integration in Data Mining is Important?
Many companies incorporate Big Data and Data Analytics to stay ahead of their competitors. One of the most common applications of Data Integration in Data Mining is market and consumer data collection from various data sources such as Ad platforms, Sales platforms, Social Media platforms, etc. Data Integration helps companies in monitoring their activities and performance in real-time and perform Data Analytics on data for future predictions and improve strategies.
Data Integration in Data Mining is also essential in the healthcare industry as it enables organizations to collect patient records from various sources and integrate them for identifying medical disorders and diseases and extract useful insights. Enterprise Data Integration feeds integrated data into data centers to enable enterprise reporting, predictive analytics, and business intelligence. Also, data collection and data integration in Data Mining helps in improving the accuracy of medical insurance claims processing. It ensures that the patient’s name and other personal information are saved accurately and consistently.
What are the Different Approaches to Data Integration in Data Mining?
Data Integration in Data Mining is mainly categorized into two types of approaches. The different approaches to Data Integration in Data Mining are listed below:
Tight Coupling is a process of combining data from various data sources using ETL (Extraction, Transformation, and Loading) into a single storage system such as Data Warehouse. Here, Data Warehouses are treated as a data retrieval component.
In the Loose Coupling, the data is kept actual data source Databases. Using this approach users get an interface to send a query, which transforms into a format suitable for the data source, and the query is received by the source and it sends the data back to the user as per the query.
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- Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
- Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision-making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
What are the Data Integration in Data Mining Techniques?
The various techniques used in Data Integration in Data Mining are listed below:
Manual Integration is widely used by data analysts for collecting, cleaning, and integrating data to extract valuable information. This method avoids using automation during Data Integration. Manual Integration is best suited for an organization with a small or limited dataset. It is a time-consuming task and dealing with huge datasets will be a tedious task.
In Middleware Integration, middleware software is used to collect data from multiple data sources, normalize it, and store it in the destination data set. It is used whenever an organization wants to transfer or migrate data from legacy systems to modern Databases. Middleware Data Integration in Data Mining act as a medium or interpreter between legacy and modern systems.
Application-based Integration uses software to extract, transform and load data from data sources. It saves time and effort but building such software applications requires technical understanding. Although, this technique saves time and effort but complicated to implement.
Uniform Access Integration
Uniform Access Integration technique integrates data from various data sources but it doesn’t change the location of the data, it stays in the original location. With this Data Integration in Data Mining technique, users can integrate data to create a holistic view without the need for separate storage space.
Data Warehousing is a Data Integration in Data Mining technique that is similar to Uniform Access Integration but the only difference is it stores data in certain storage, Data Warehouse enables Data Analysts, Data Scientists, and other users to handle more complex queries with ease. It delivers high query speed and a safe place to store business data.
In this article, you learned about Data Integration, What is Data Integration in Data Mining, and its importance. Also, you read about different approaches to Data Integration in Data Mining and various techniques. Users need to explore and choose the right Data Integration technique for Data Mining as per their needs and business requirements.
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