The 21st century has brought with it the age of digital gold. Data is the new bread and butter of globalization, technological progress, and soon business too. But, even though it’s important for making big business decisions, 66 percent of organizations don’t have a clear plan for keeping their data accurate and organized. Many of these companies need access to the technology to gain the right data insights. According to Tech Jury, an average human generates about 1.7 megabytes of data per second, and more than 3.7 billion people use the internet today. And with every usage of the internet, a tremendous amount of data is being generated. Thus it is imperative to understand data integration in data mining and employ it for your business.

What is Data Integration in Data Mining?

Data integration in data mining is a method of processing data from multiple heterogeneous sources of data and combining them coherently to retain a unified view of the information. These data sources may include multiple data cubes, databases, or flat files. The data integration strategy is formally known as a triple (G, S, M) approach. G represents the global schema, S represents the heterogeneous source of schema, and M represents the mapping between source and global schema queries.

Read What Is Data Integration & understand the concept & its benefits.

Data Integration Approaches

There are mainly two approaches to data integration – one is the “tight coupling” and another is the “loose coupling”

Tight Coupling

  • In this method, a data warehouse is treated as an information retrieval component
  • Data are combined from various sources into a single physical location via the process of ETL- Extraction, Transformation, and Loading

Loose Coupling

  • In this method, users are provided an interface to input their queries and this interface then transforms it in a way that the source database can understand the queries and then send them directly to the source databases to obtain results
  • In loose coupling, the data only remains in the actual source databases.

Issues in Data Integration

There are a few issues that you may encounter when you perform data integration in data mining. 

Entity Identity Trouble

Since data is collected from heterogeneous sources, matching the real-world identities from the data becomes a problem. Analyzing the metadata information of an attribute prevents errors in schema integration.

Structural Integration and Functional Dependency

Ensuring that the functional dependency of an attribute in the source system and its referential constraints match with the functional dependency and referential constraint of the same attribute in the target system is a key aspect of effective data integration solutions. This alignment is instrumental in achieving structural integration.

Redundancy and Correlation Analysis

One of the big issues during data integration is redundancy. These redundant and unimportant data are no longer needed and can arise due to attributes that can be derived using another attribute in the data set. 

The level of redundancy can also be raised by the inconsistencies in attributes and can be discovered using correlation analysis. Here, the attributes are analyzed to detect their interdependence on each other, thus being able to detect the correlation between them.

Triple Duplication

Data integration also has to deal with duplicated tuples. These may become a part of the resultant data if a denormalized table is used as the source for data integration.

Data Conflict Detection and Resolution

Data conflict happens when the data merged from various sources do not match. This could be caused by varying attribute values in different data sets. It could also be caused by different representations in different data sets. Issues such as this are meant to be detected and resolved in data integration.

Also read, why should we adopt Data integration in healthcare & Data integration in Insurance


Why Choose Intone Data Integrator (IDI)?

The Global News Wire reports that the data integration market is estimated to reach $19.6 billion by 2026, a significant jump from $11.6 billion in 2021 following a CAGR of 11%. The value of data has been ever-increasing. Forested reported that Fortune 1000 companies could generate incredible outcomes with just a small increase in data visibility. With just a 1% increase in data visibility, these companies could earn more than $65 million in additional income. 

Intone is proud to present a state-of-the-art data management service & integration solution in Intone Data Integrator. IDI is a tried and tested product by industry pioneers and leaders alike. We offer,

  • Data encryption at every stage
  • Centralized password management
  • Supports unlimited heterogeneous data source combinations
  • Real-time, streaming & batch processing of data
  • Lineage capturing at every data integration
  • Eye-catching monitoring module that gives real-time updates
  • 600+ Data and Application connectors
  • Generates knowledge graph for all data integrations done
  • Distributed In-memory operations.
  • A no-code low-code platform.

Check out how Intone can help you streamline your manual business process with Robotic Process Automation solutions.