What is Data Integration? Process, Tools, and Examples

Creating a unified view of data residing in different systems in disparate formats is called data integration.

Last Updated: December 1, 2022

Data integration means creating a unified view of data residing in different systems, applications, cloud platforms, and sources to aid business and scientific analysis without risks arising from duplication, error, fragmentation, or disparate data formats. This article explains the meaning of data integration, its tools, and its various examples. 

What Is Data Integration?

It is defined as the process of creating a unified view of data residing in different systems, applications, cloud platforms, and sources to aid in business and scientific analysis without risks arising from duplication, error, fragmentation, or disparate data formats. 

Even if an organization receives all the required information, that data often exists in various distinct data silos. For something like a conventional customer 360 view use case, one must integrate the data — it may include information from cloud contact center systems, online activity, marketing and sales software, customer-facing apps, advertising, and customer success systems, as well as data from other various stakeholders. Inputs from these numerous sources must be aggregated for analytical purposes or operational activities, which may be challenging for data scientists or developers.

Data integration is the process of merging data from several sources into a unified, cohesive perspective. The integration process starts with data input and comprises cleaning, ETL, data analysis, and transformation. Data integration allows analytics tools to provide practical, actionable business insights.

ETL is an abbreviation that refers to extract, transform, and load. This relates to data extraction across several source systems, its transformation into a new framework or form, and its transfer to a destination. ETL includes both data and application integration.

Data integration is strongly linked to the notion of application integration. It includes exchanging data across programs to maintain them in synchronicity. Typically, each application emits and uniquely accepts data, which flows in smaller amounts. Application integration is well suited for supporting operational use cases.

There is no general strategy for data integration. Nevertheless, it generally consists of a collection of data sources, a parent server, and clients that access the data from the central or master server. There are essentially two principal approaches to data integration — one is the tight coupling strategy, and the other is the loose coupling methodology. 

First, a data warehouse serves as a component for retrieving information. Through the ETL (Extraction, Transformation, and Load) process, data from several sources are merged into a single physical place in close coupling.

Conversely, an interstitial layer accepts the user’s query, turns it into a format the source database may comprehend, and then transmits it straight to the source database to elicit the response. In data integration with loose coupling, the data stays exclusively in the source databases.

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How did data integration evolve?

The idea of data integration originated in the 1980s. In 1991, the University of Minnesota built the first data integration system powered by structured metadata for the Integrated Public Use of Microdata Series (IPUMS).

This strategy included the ETL of material from heterogeneous data sources to render information compatible. This movement in the 1990s was the first use of a technology that removed the need to add data whenever it was sent across systems manually.

Nonetheless, this data integration technique presented several infrastructure- and complexity-related challenges. As with any other technology, therefore, data integration solutions needed refinement. Today’s data integration technology utilizes self-service and automation to consolidate data and establish interconnections swiftly, securely, and with minimal effort.

Benefits of data integration

By building a robust data integration capability, organizations can:

  • Enable inter-department and inter-system collaboration: Employees in all departments and geographically dispersed locations must have access to the business’s data for shared and individualized initiatives. In addition, almost every department produces information that the entire organization needs. Data integration may promote data coordination and unification throughout the enterprise.
  • Unlock time and effort savings: When a business integrates its data effectively, it dramatically reduces the time required to compile and evaluate it. The automated management of centralized views eliminates the need to collect data manually. Professionals no longer need to manually establish links every time a report needs to be pulled out or an app-design scenario comes into play.
  • Get ready access to reports: In the absence of a data integration system that seamlessly integrates information, reporting must be redone periodically to accommodate any modifications. With automatic updates, however, reports may be performed whenever necessary in real-time.
  • Maximize the value of information: Over time, data integration activities increase the value of enterprise data. Qualitative deficiencies are detected as information is assimilated into a centralized system, and the required adjustments are performed, resulting in much more accurate data – the cornerstone for quality analysis.
  • Obtain value from big data sets: Data lakes are often highly intricate in their structure and voluminous. For example, companies such as Facebook and Google continuously process data from billions of individuals. This substantial volume of typically unstructured data is called “big data.” This implies that intelligent data integration becomes vital for large data operations.
  • Empower business intelligence (BI) apps: Data integration streamlines business intelligence (BI) procedures by providing a consistent and uniform view of data from several sources. Organizations may quickly deploy datasets to generate meaningful insights around and about existing business situations.

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Data Integration Process

In a traditional data integration activity, the client requests data from the master server. The master server subsequently collects the necessary data from both external and internal sources. The data is retrieved from the sources and then aggregated into a unified data collection. This is returned to the user for their consumption. This is returned to the user for their consumption.

Every day, companies collect an increasing amount of data in various forms from a growing range of information sources. Organizations need a methodology for personnel, clients, and customers to extract value from this information. This implies that enterprises must be able to gather relevant data from wherever it sits at this time to support their reporting and company operations.

However, essential data is often dispersed among apps, datasets, and other sources housed locally, on the cloud, inside IoT devices, or provided by third parties. Organizations no longer retain data in a single database; instead, they maintain conventional transactional and master data, in addition to new forms of structured and unstructured information across numerous sources. For example, a company may have the material in a flat file or need to retrieve data through a web service.

The conventional data integration method is referred to as the physical and logical integration technique. This includes the physical transportation of information from its source repository to a staging point, where cleaning, mapping, and transformation occur before the data is physically transported to a destination system, such as a data warehouse or data mart.

The alternative method is data virtualization. Using virtualization infrastructure to access physical data archives is entailed by this method. Data virtualization, as opposed to physical data integration, includes the production of simulated or virtualized representations of the core physical surroundings without requiring physical data transportation.

Extract Transform and Load (ETL) is a standard data integration approach in which data is physically taken from several source systems, converted into a new layout, and loaded into a centralized data storage.

To better understand the process of data integration, let us look at the different methods, approaches, and techniques you can use: 

1. Consolidating data

Data consolidation physically combines data from several systems, generating a copy of the consolidated data in a single data repository. Typically, the objective of data aggregation is to decrease the number of storage sites for data. Data consolidation is supported by ETL technology.

ETL extracts data from various sources, converts it into an understandable format and transports it to a different warehouse or database. Before populating the new source, the ETL process cleanses, organizes, and transforms data and implements business rules afterward.

2. Manually integrating data

Hand-coding, often known as manual data integration, is one of the most fundamental methods for data integration. This strategy is only practical for integrating a limited quantity of data streams. Building code to gather the data, modify it if required, and integrate it may be advantageous. Although hand-coding may not necessitate any software investment, it may be time-consuming, and expanding the integration process to encompass new data sources may be challenging.

3. Using middleware for data integration

Middleware data integration is a strategy in which a middleware application operates as a mediator, assisting in the standardization of data and assimilation into the master data pool. (Consider adapters for obsolete electrical equipment and their connecting points) Frequently, legacy programs do not work well with others. Middleware is utilized when the data integration systems cannot independently access the information from any of these applications.

4. Adopting federation

Federation utilizes a virtual data repository and develops a unified and common data architecture for heterogeneous materials culled across several systems. Data is compiled and accessible via a single entry point. Enterprise information integration (EII) is an enabling technology for data federation. It provides a unified representation of data from several sources using data abstraction. Applications may then show or analyze this data in innovative ways. Federation and virtualization are effective workarounds for instances in which data consolidation would be prohibitively expensive or cause excessive security and compliance concerns.

5. Propagating data

The use of apps to replicate information from one place to another is data propagation. It may be conducted either synchronously or asynchronously and is event-driven. Enterprise application integration (EAI) and enterprise data replication (EDR) solutions facilitate data propagation.

EAI connects application systems for messaging and transaction interchanges. Integration platform as a service (iPaaS) is a current integration strategy for EAI. Instead of applications, EDR often transports massive volumes of data across databases. Logs and base triggers are utilized to detect and communicate data fluxes between the source and other downstream databases.

6. Leveraging data virtualization

Data virtualization is noteworthy because users may still receive a unified representation of the data despite the information being in separate systems. Data virtualization is simply a layer of logic that combines data from all source systems and transmits it in real-time to business users. An advantage of virtualization is that it eliminates the need to transfer data physically. Users do not have to bother about the extra storage expenses involved with keeping numerous versions of their information since the data remains in the original source systems.

7. Uniform access vs. common storage integration of data

Uniform access integration is a method of data integration that emphasizes the creation of a front end that makes data from multiple sources appear uniform. However, the data remains inside the original source. Using this technique, one may utilize object-oriented database management systems to create the impression of homogeneity amongst otherwise dissimilar databases.

In data integration, common storage integration is also a standard storage method. The integrated system maintains a copy of the information as it was in the actual source and processes it for a consistent and coherent perspective. This differs from uniform access, where data remains in its original location. The classical method for data warehousing is based on the principle of shared storage.

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Top 7 Data Integration Tools

According to Introspective Market Research, the global data integration market was worth $9.15 billion in 2021, which is expected to grow to $21.53 billion by 2028. In this rapidly burgeoning market, here are the top data integration tools to try:

1. XPlenty (now Integrate.io)

The graphical user interface (GUI) for constructing secure data pipelines in XPlenty is reportedly intuitive, simple, and low-code based. The Xplenty platform synchronizes with over 140 data sources and offers businesses limitless assistance around the clock. And the flat-rate cost makes this data integration tool even more attractive.

2. Mulesoft

MuleSoft offers a data integration platform that enables enterprises to link data, apps, and hardware across on-premises and cloud settings. Since 2018, with Salesforce’s acquisition, it has been included in the Salesforce Integration Technology. AnyPoint by MuleSoft provides many services and tools that make it easier for developers to incorporate cutting-edge technologies. This comprises the API Designer, Anypoint Studio, Anypoint Runtime Manager, and other applications.

3. Keboola

Keboola is a data platform that works as an as-a-service model and includes over 250 pre-built connectors for data integration. The connectors allow you to automate data pipelines that integrate everything from SaaS applications to data archives, APIs, and webhooks. With its inbuilt ELT capabilities, users may transfer data that has been enhanced directly to tools that will render this actionable. The robust and versatile data management stack in structured query language (SQL), Python, R, or dbt provided by Keboola enables users to configure integration pipelines completely.

4. Dataddo

Dataddo is a cloud-based, no-coding ETL platform that lets technical and lay-users work with completely configurable data integration. With over 100 connections but also highly configurable metrics and properties, Dataddo offers robust data pipelines for every use case. The platform integrates effortlessly with your current data stack. The clear UI and straightforward configuration of Dataddo enable users to work on consolidating their data rather than spending time learning ancillary activities.

5. Oracle Data Integrator

Oracle Data Integrator 12c, the most recent edition of Oracle’s strategic Data Integration product, is a best-in-class data integration platform targeting enterprises that already use Oracle applications and platforms and wish to integrate data with these systems seamlessly. Oracle Data Integrator is freely accessible and standards-based, allowing it to integrate with third-party applications in addition to Oracle’s own.

6. SnapLogic 

SnapLogic is an integration platform as a service (iPaaS) that provides organizations with rapid integration services. It has an intuitive, browser-based interface with over 500 pre-built connections. Using the click-and-go capability, a person from any industry may easily merge data from two apps with the assistance of SnapLogic’s artificial intelligence-powered assistant.

7. Astera Centerprise

Astera Centerprise is an on-premises data integration system comprising assimilation, conversion, quality validation, and profiling. It allows users to pick from various integration scenarios and manage their interpretations of data integration. Featuring in-built profiling, quality monitoring, and data cleansing, the system enables the construction of data integration tasks.

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Data Integration Examples

Here are a few examples of data integration projects that have an immediate business objective or goal:

1. Integrating customer data to unlock marketing insights

Assimilating customer data is among the most critical use cases for data integration. Consolidating client data from all accessible sources, like contact information, account details, customer lifetime value (CLV) ratings, and information gathered from customer inquiries, website views, direct sales initiatives, surveys, social media postings, and other interactions.

2. Integrating IoT data to optimize industrial operations

Organizations are increasingly moving to combine data generated by many sensors deployed on internet-connected industrial equipment, such as manufacturing machines, automobiles, elevators, pipelines, electricity grids, and oil rigs (i.e., the internet of things). One can utilize integrated sensor data sets to evaluate business processes and operate preventative maintenance simulations that anticipate potential equipment issues before they occur, reducing unscheduled repair downtime.

3. Integrating store data to operate retail businesses

Both traditional and online stores deal with a considerable amount of data. Users must centralize all of this information to track performance, regardless of which retailer or team member submitted it. Data integration enables retailers to manage inventory, workforce person-hours, revenue data, and other critical variables across their channels and locations.

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Takeaway 

Data integration is now crucial for most modern business applications. Instead of looking at information in silos, it makes it possible to consider multiple data sources and types together to understand the big picture. For example, instead of just looking at the customer’s location, data integration brings together demographic information, social media activity, web browsing history, and much more to create a detailed customer persona. This is one of the many data integration uses that organizations can explore and monetize. 

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Chiradeep BasuMallick
Chiradeep is a content marketing professional, a startup incubator, and a tech journalism specialist. He has over 11 years of experience in mainline advertising, marketing communications, corporate communications, and content marketing. He has worked with a number of global majors and Indian MNCs, and currently manages his content marketing startup based out of Kolkata, India. He writes extensively on areas such as IT, BFSI, healthcare, manufacturing, hospitality, and financial analysis & stock markets. He studied literature, has a degree in public relations and is an independent contributor for several leading publications.
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