In today’s world, businesses focus on gathering more data to extract valuable insights from and optimize business processes.
In short, this 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.
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.
What is Data Integration in Data Mining?
This 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 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.
Why Data Mining is Important?
- Many companies incorporate Big Data and Data Analytics to stay ahead of their competitors.
- One of the most common applications is market and consumer data collection from various data sources such as Ad platforms, Sales platforms, Social Media platforms, etc.
- Data Integration helps companies monitor their activities and performance in real-time and perform Data Analytics on data for future predictions and improve strategies.
- This 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, 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.
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What are the Different Approaches in Data Mining?
Tight Coupling
- 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.
Loose Coupling
- In Loose Coupling, the data stays in its source. With this approach, you get an interface to send a query. This query is then transformed into a format that the data source understands. Once the source receives the query, it processes it and sends the data back to you as you requested.
Here’s a table comparing Tight Coupling and Loose Coupling:
| Aspect | Tight Coupling | Loose Coupling |
| Data Storage | Combines data from various sources into a single physical location. | Keeps data in the source database. |
| Data Processing | ETL (Extraction, Transformation, and Loading) processes data before storing it in a centralized location. | Queries are processed at runtime by interacting directly with the source databases. |
| Flexibility | Less flexible as all data must be pre-processed and stored centrally. | More flexible as data remains in its original location, allowing real-time querying and processing. |
What are Data Mining Techniques?
Manual Integration
- 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.
Middleware Integration
- 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
- 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.
- Users can integrate data to create a holistic view without the need for separate storage space.
Data Warehousing
- Data Warehousing – 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.
Additional Resources related to Data Mining
Conclusion
- In this article, you learned about Data Integration, What is Data Integration in Data Mining, and its importance.
- Also, you read about different approaches and various techniques. Users need to explore and choose the right Data Integration technique for Data Mining as per their needs and business requirements.
FAQ on Data Integration in Data Mining
What is data integration with an example?
A company might have customer data in a CRM system, sales data in an ERP system, and marketing data in a separate tool. Data integration would involve merging these datasets so that all relevant information about a customer is available in one place, enabling better analysis and decision-making.
What is data cleaning and data integration in data mining?
– Data Cleaning: This involves detecting and correcting errors, inconsistencies, and inaccuracies in the data to ensure high quality. It may include removing duplicates, filling in missing values, and correcting data formats.
– Data Integration: In data mining, data integration combines data from multiple sources into a coherent dataset. This is essential for analyzing large datasets spread across different platforms or formats. Integrated data provides a complete and accurate basis for mining insights.
Why is data integration required in a data warehouse?
Data integration is crucial in a data warehouse because it combines data from multiple sources to create a comprehensive view of an organization’s information. Without data integration, a data warehouse would merely be a collection of isolated data silos, leading to fragmented insights and potentially incorrect conclusions.
Sarthak is a skilled professional with over 2 years of hands-on experience in JDBC, MongoDB, REST API, and AWS. His expertise has been instrumental in driving Hevo's success, where he excels in adept problem-solving and superior issue management. Sarthak's technical proficiency and strategic approach have consistently contributed to optimizing operations and ensuring seamless performance, making him a vital asset to the team.
