Why Data Integration Is Important

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Why Data Integration Is Important Data Integration
Table of Contents

Introduction

A business or organization can collect data from various sources, and it’s critical to ensure that this data is being used in the most efficient manner possible. Without integrating the company’s data with other sources, the information can become lost or disorganized, making it impossible to make clever business decisions based on the data. 

Data integration will ensure your company has access to all of its information in one place and in a way that makes sense to everyone who needs it. Easily integrating the business data from different systems can be crucial to the company’s success, so understanding what data integration means and how it works will help to get the most out of your efforts to enhance the business operations.

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What is data integration?

Data integration is a process of gathering and compiling data from various systems into one place to be processed, analyzed, and shared. It ensures business applications in a large organization can share data efficiently. 

By definition, data integration is a technical process of merging two or more individual data sets into one common data environment. It includes combining data from various internal and external sources into a single, consistent view. It is sometimes used interchangeably with data synchronization, but in most cases, data integration refers to information coming from multiple sources.

The main objective of data integration is to combine and consolidate data from a wide range of sources into one coherent form. The end goal is to have all relevant information from each source ready for analysis, in one place.

Read how Actify helped Fincare in data integration.

Evolution of Data Integration

The concept of data integration emerged from 1960s mainframe computing when companies began purchasing multiple business application packages—each with its proprietary database management system. Without an efficient way to consolidate all these databases, different applications couldn’t work together seamlessly or share data across departments.

To address this problem, companies began investing in computer-based tools and one of the data integration processes called Extract Transform Load (ETL) software that could pull information out of one database or program and load it into another. This would free up programmers’ time so they could focus on developing new features instead of writing complicated ETL programs again and again.

Nowadays, businesses use cloud data integration, leverage data lakes, data warehouses, and big data for data integration.

For instance, Cloud data integration allows users to consolidate different data sources into a single format, no matter where they are stored. Storing and processing multiple sets of information in one place offers flexibility, performance, and cost-efficiency. 

Cloud technology offers several key features that facilitate seamless data integration: scalability, elasticity, and self-service capabilities. Today, companies use cloud providers to get more out of data integration.

Why is data integration important?

Data integration is so much more than just data synchronization. Businesses today are collecting vast amounts of data from many different sources: transactions, video, social media, and so on. Every second, new data comes into the organization — and for data to be useful, it must be available for analysis at all times. 

This can only happen when data from various systems can communicate promptly and in a standard way. That’s what data integration does best!

Businesses require access to accurate, relevant, and up-to-date information to be competitive. By integrating data from various sources organizations can elevate their performance. 

  • Better Decision 

Businesses can make better decisions that help drive their bottom line forward. In addition, data integration applications often have added value for business users by providing additional functions such as analytics tools that generate actionable insights.

  • Data integration reduces errors 

This may seem minor, but when you consider that a good amount of an organization’s resources go into sourcing and correcting these errors, data integration solutions become a valuable resource. 

When different departments or even divisions within departments all have access to one another’s data, they can find errors faster and have all information in one place.

  • Accesses to valuable data 

In a connected world, data has become more valuable than ever. Connecting the applications and cloud services to an enterprise data hub can help gain greater insight into the data and leverage it for more strategic business initiatives.

  • Improves employee efficiency

With data spread across a variety of systems, employees waste valuable time looking for data in different systems. Data integration technology saves billions of dollars in lost productivity each year by centralizing the company’s most important data.

  • Increases competitiveness 

Companies that ensure data integration in their core business can take advantage of all the data assets and make a positive impact on efficiency by creating more relevant data products.

Techniques of Data Integration

  • Uniform Data Access 

Uniform Data Access allows access to enterprise data from a variety of sources and presents it uniformly while keeping the data in its original location.

  • Common Data Storage

Common Data Storage assists in integrating data from numerous sources, consolidating it in a central location, and managing it. The most common example is the Data warehouse.

  • Application Based Integration

An application-based integration allows to request, retrieve, store and manipulate data in a variety of ways. It’s usually accomplished by using an Application Programming Interface or API. Application-based integration connects to a variety of data sources and provides the user with a uniform set of results.

  • Common User Interface 

Common User Interface is also known as manual integration. In this approach, the user manually collects the data from different sources from interfaces and then cleans the data. Lastly, this cleaned data gets integrated into one warehouse. 

  • Middleware Data Integration 

In the Middleware Data Integration approach, the middleware acts as a mediator and assists in the normalization of data and its inclusion into the master data pool.

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Data Integration Challenges

One of the top challenges in any data integration project is data transformation, where converting one type of data into another so it can be read by an application. This process is both time-consuming and labor-intensive and involves many steps.

In data integration, determining which components should be transformed, gathering component details, creating a mapping document to convert information, importing or extracting component details, and then testing to ensure accuracy is quite a lengthy process. 

Additional challenges in data integration include incompatible APIs (application programming interfaces) between different applications and scalability issues that arise as companies grow.

In today’s business world, you cannot afford to be information locked in. Being able to seamlessly access and share enterprise data across boundaries and platforms is essential for driving productivity and growth, improving customer service, reducing costs, increasing revenue, and raising profits. 

That’s why companies need solutions that seamlessly integrate data from any source, including your industry-leading Business Intelligence applications into an easy-to-use platform that supports all of the data processing needs.

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Hindol Basu 
GM, Voxco Intelligence

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