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Application Integration vs. Data Integration: It’s Not Either/Or

The complexity of modern IT environments is increasing exponentially. Hybrid integration, data warehouse solutions, and analytics tools have all become vital to managing data in real-time and delivering the business intelligence leaders need to make decisions. With the proliferation of applications, systems, and data sources, it’s become crucial to integrate them seamlessly to optimize processes and enhance efficiency.

Application integration and data integration are two critical areas of focus for businesses to achieve these objectives. While data integration focuses on bringing together data from disparate sources into a unified view, application integration is all about connecting software applications and systems to work together seamlessly.

But it’s not a one versus the other situation. Instead, it’s about leveraging both approaches’ unique benefits and capabilities as part of a comprehensive data management strategy.

In this article, we dive into the importance of application and data integration and why modern business IT environments need a combined approach.

What Is Application Integration?

Application integration involves connecting different software applications within an organization. It ensures that these applications can communicate, exchange data, and cooperate effectively. The goal of application integration is to create a unified ecosystem where disparate systems work harmoniously.

Consider an e-commerce website where a customer’s order triggers automatic updates across the inventory, shipping, and CRM systems. Application integration allows these different systems to communicate instantly, streamlining the order fulfillment process and enhancing customer satisfaction without manual data entry.

Application integration leverages middleware and APIs to link disparate systems. Middleware serves as a connective layer, ensuring applications can interact and share data securely and efficiently. APIs facilitate standardized communication between applications, simplifying the creation of a cohesive software ecosystem. From a data analytics perspective, application integration is crucial for businesses to gain insights into their operations and make informed decisions based on real-time data.

Advantages and Disadvantages of Application Integration

Application integration offers several benefits. Businesses can make faster, more informed decisions by automating processes and enabling real-time data synchronization. Integrated applications can share data seamlessly, removing the need for duplicate data entry and reducing the risk of errors.

Benefits of Application Integration

Improved efficiency: Application integration streamlines operations and eliminates manual tasks, resulting in improved efficiency.
Reduced manual effort: Integrated applications share data seamlessly, reducing the need for manual data entry and saving time and effort.
Enhanced data accuracy: With real-time data synchronization, businesses can ensure that information is up-to-date and accurate across all integrated applications.

In addition to these benefits, application integration presents challenges that businesses must address. One of the main challenges is the complexity of integrating legacy systems. Legacy systems often use outdated technologies and may not have built-in integration capabilities. This requires businesses to invest in additional tools and resources to enable integration with modern applications.

Another challenge is the need for proper security measures. When integrating applications, businesses must protect sensitive data throughout the integration process. This includes implementing secure communication protocols, encrypting data in transit, and applying access controls to prevent unauthorized access to integrated systems.

Furthermore, application integration can sometimes result in system downtime during the integration process. This is especially true when migrating from one system to another or significantly changing existing integrations. Businesses must carefully plan and execute integration activities to minimize disruptions to their operations.

A Case Study in Application Integration

Carnival Cruise Line holds an excellent example of the power of application integration. They faced the monumental challenge of managing worldwide travel partner integrations with their own reservations system. They also needed to manage shipboard service partner integrations with onboard property management and POS systems. Synchronizing data shore-to-ship pre-cruise and ship-to-shore after the cruise was essential.

To manage all these challenges, the cruise line repeatedly had to decouple and reset different integrations in processes that consumed valuable time. However, with application integration powered by API management, they were able to onboard partners at a rapid pace around the globe. Simply put, application integration provided Carnival Cruise Line with the optimization it needed to give guests a fun and seamless experience.

What Is Data Integration

Data integration is the process of consolidating data from disparate sources into a unified and consistent view. This process involves extracting, transforming, and loading data from databases, files, and applications. Data integration lets businesses comprehensively understand their information assets and empowers them to derive valuable insights.

Consider a multinational corporation that operates in multiple countries. Each country may have its own database, storing customer information, sales data, and financial records. Without data integration, getting a holistic view of the corporation’s performance and making informed decisions would be challenging.

By implementing data integration, the corporation can merge data from all its sources into a single source of truth, like a data warehouse, data lake, or lakehouse. This unified view of data allows the organization to analyze trends, identify patterns, and make data-driven decisions. For example, the company can analyze sales data across different regions to identify emerging markets or optimize its supply chain based on demand patterns. Data integration also plays a crucial role in enhancing data accuracy and consistency, ensuring businesses have access to reliable data they can trust.

Finally, data integration is essential for advanced analytics and artificial intelligence (AI) initiatives. By bringing together diverse data sources, organizations can train AI models, uncover hidden insights, and drive innovation.

Challenges and Benefits of Data Integration

Data integration has numerous advantages for businesses. Integrating data into a common location means everyone’s using the freshest, most accurate data. This can help enterprises accurately identify trends, patterns, and opportunities, and make the right data-driven decisions.

Benefits of Data Integration

Improved data quality: Data integration helps businesses improve data quality by identifying and rectifying inconsistencies, errors, and duplicates.
Increased data accessibility: With data integration, businesses can easily access and retrieve the information they need from a central repository, saving time and effort.
Enhanced decision-making: By having a holistic view of their operations, businesses can analyze data from different sources and gain valuable insights, leading to better decision-making.

However, data integration also poses challenges that businesses need to address. One challenge is data consistency. When integrating data from different sources, ensuring consistency can be a complex task. Data may have different formats, structures, and definitions, making it difficult to reconcile and integrate.

Another challenge is the complexity of integrating disparate data formats. Different systems may use different data formats, such as CSV, XML, or JSON. Transforming and mapping these formats can be time-consuming and require specialized skills.

Furthermore, data integration requires robust data governance. Businesses need to establish data governance policies and procedures to ensure data quality, security, and compliance. This includes defining data ownership, access control, and privacy measures.

A Case Study in Data Integration

SamTrans is the San Mateo County public transportation agency. The key challenge they faced arose from the fact that their IT teams span across a variety of different agencies and departments and had various non-sharable processes for integrating data.

When they launched a business intelligence reporting project, it became apparent that they  needed a better way to do their data analytics. To overcome this challenge, SamTrans turned to StreamSets. They used the platform to break down the walls between the different IT departments and successfully combined the distributed data onto a single dashboard for streamlined decision-making.

Key Differences Between Application and Data Integration

Having explored application and data integration individually, let’s now examine their key differences.

Comparing ‌Techniques and Tools

Application integration primarily focuses on connecting and synchronizing applications through middleware solutions. It involves establishing communication channels, defining data mappings, and ensuring seamless interaction between systems. On the other hand, data integration revolves around consolidating and harmonizing data from multiple sources, transforming it into a common format, and loading it into a centralized repository.

Understanding the Impact on Business Operations

Application integration drives operational efficiency by automating processes, reducing manual effort, and facilitating real-time data exchange. It empowers organizations to operate cohesively and respond rapidly to changing market demands. Data integration, on the other hand, enables businesses to derive insights and make informed decisions based on a comprehensive and unified view of their data.

A Birdseye View of Application Integration vs. Data Integration

Application Integration
Data Integration

Definition
Connecting software applications to enable communication and workflow
Consolidating data from different sources into a unified view

Goal
Automate processes and workflows across systems
Create a “single source of truth” for analytics, migrate data to the cloud, allow real-time/streaming data ingestion, and more

Approach
APIs, middleware, service buses
Extract, transform, load (ETL) process

Tools
APIs, middleware, enterprise service bus
ETL/ELT, data pipelines

Data Focus
Data mapping between systems
Bringing together various data formats

Key Benefit
Increased operational efficiency
Advanced analytics from integrated data

Challenges
Legacy system constraints, change management
Data consistency, transformation complexity

Use Cases
Linking sales and CRM systems, order processing
Building 360-degree customer view, business intelligence

Application Integration vs. Data Integration: Why Your Enterprise Needs Both

Enterprise organizations need both application integration and data integration to manage their IT environments effectively. Both approaches are critical to achieving comprehensive data management and gaining insights into business operations.

Data integration brings the information into one place, and application integration enables you to act on that data. For instance, you could integrate the data from both your ordering platform and your inventory system so that if an order goes through for a product that’s out of stock, your data platform can track that error and make it easier to see if this has been a consistent problem and what you might do to fix it.

But what if that data was sent straight to the ordering system, which can see that data and automatically cancel the order and send an email notification to the customer explaining what happened? That’s the power of application integration and data integration combined.

See It in Action

As they say, a picture is worth a thousand words. A video? Even more… See how data integration and application integration complement each other in this short demo.

Watch and learn how StreamSets’ Transformer for Snowflake allows you to build pipelines that run natively on Snowflake by leveraging Snowpark. And see how application activation can be implemented using webMethods.io Integration to close the loop of data and application integration.

The post Application Integration vs. Data Integration: It’s Not Either/Or appeared first on StreamSets.

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