A Complete Guide To Data Integration In Data Mining


The process of combining data from various sources into a single, unified view is data integration in data mining. Integration starts with the process of ingestion and includes steps such as cleansing, mapping ETL, and transformation.

In the end, data integration allows analytics data integration tools to generate effectively, actionable business intelligence. The client sends a request to the server system for data in a traditional data implementation phase. The master server then receives the information necessary from internal and external sources.

Even if a business receives all the data it wants, the information also remains in a variety of different sources of data. 

Data Integration Example

For instance, the data that must be integrated for a typical consumer 360 view use case can include data from their CRM systems, web traffic, and software for marketing operations

For analytical needs or operational behavior, knowledge from all these various sources also needs to be brought together and it cannot be a small job for data engineers or developers to get them all together.

Importance of Data Integration in Data Mining:

The importance of data integration is illustrated by performing all these operations as effectively as possible. It also shows the important advantages of a well-thought-out approach to data integration in data mining:

  • Enhances systems communication and unification

Employees progressively need access to the company’s data for joint and individual projects in every department. Information Technology needs a safe solution for the distribution of data across all business lines through self-service access.

Besides, employees in almost every department produce and develop data that is required by the rest of the organization. To facilitate cooperation and unification within the enterprise, data integration needs to be collaborative and unified.

  • Saves time and improves performance

When a business takes steps to better integrate the data, it greatly reduces the time it takes to plan and analyze the data. Automating unified views removes the need for manually collecting data, and when they need to run a report or create an application, workers no longer need to build links from scratch.

How to do data integration in data mining

Also, using the correct software returns much more time (and money overall) to the dev team rather than hand-coding the integration.

With more hours available for research and implementation to make an organization more efficient and successful, all the time saved on these projects can be put to other, better uses.

  • Reduces errors (and re-work)

Whenever it comes to the data capital of a business, there’s a lot to keep up with. Employees need to know every location and account they may need to explore to manually collect data and have all the appropriate software installed before they start to ensure that their data sets are complete and correct. They would have an incomplete data set if a data archive is introduced, and the employee is unaware.

  • Delivers more precious details

In reality, data integration activities increase the value of data from an organization over time. When information is incorporated into a centralized framework, quality concerns are detected and required changes are made, eventually resulting in more detailed data, the basis for quality analysis.

Data Integration in the modern organization

The data integration in data mining is not a one-size-fits-all solution; based on various business requirements, the correct formula will differ. For data integration software, here are some common use cases:

  • Large Data Leveraging

Data lakes can be highly complex in volume and huge. For example, companies such as Facebook and Google process a non-stop stream of data from billions of users. This level of consumption of information is widely known as big data. 

When big data companies grow, more knowledge becomes accessible for companies to exploit. That means that for many organizations, the need for sophisticated data integration activities is essential to operations.

  • Creating data centers

Initiatives for data integration, particularly among large organizations, are often used to build data warehouses that combine multiple sources of data into a relational database. Data warehouses allow users to run queries in a consistent format, compile reports, produce research, and retrieve data. For example, to generate business intelligence from their data, many businesses rely on data-warehouses such as Microsoft Azure and AWS Redshift.

  • Business Intelligence (BI) streamlining

Data integration tools simplify the business intelligence (BI) processes of analysis by offering a single view of data from various sources. To extract actionable knowledge on the current state of the company, companies can easily access, and quickly understand, the available data sets. 

With data integration, without being overloaded by large volumes, analysts can collect more information for more precise assessment.

Business Intelligence does not use predictive forecasting to make future predictions, unlike market analytics; instead, it focuses on explaining the current and past to help in strategic decision-making. 

This use of data integration is well-suited for data storage, where high-level summary information aligns perfectly in an easily consumable format.

Data integration tools

There are many ways of combining information that depends on the size of the organization, the need to be addressed, and the resources available.

  • Manual data integration 

It is the process by which, by interacting interfaces manually, an individual user gathers appropriate data from different sources, then cleans it up as required, and integrates it into one warehouse. 

This is incredibly inefficient and inconsistent and makes no sense for all organizations with limited data capital, even the smallest.

  • Integration of middleware data 

It is an integration technique where a middleware program serves as a mediator, helping to normalize and carry data into the master data pool. (Think of adapters with obsolete attachment points for old electronic equipment). 

When a data integration device is unable to access data on its own from either of these applications, Middleware comes into play.

  • Uniform access integration 

It is a method of integration of data that seeks to create a front end that, when viewed from various sources, makes data appear consistent. However, the data is left inside the initial source. Using this approach, it is possible to use object-oriented database management systems to establish the appearance of uniformity between databases that are different.

Business intelligence, analytics and strategic benefits are all at play when it comes to data integration in data mining. That’s why getting full access to every data set from every source is crucial for your business. The Talend Cloud Integration Platform allows organizations to consolidate and prepare data from practically every source for review in any data warehouse.