Data Integration: Make the Right Moves for Success

Modern businesses’ foundation is data integration, which ensures that data flows smoothly between systems, leading to better decisions and more efficiency. Poor business data integration costs companies an average of $12.9 million annually, says Gartner, from waste and lost opportunities. Businesses suffer from breaking data silos, inaccurate reports, and lost potential for expansion without a well-organized data integration plan.

A well-planned business integration system makes data syncing, merging across platforms, and connecting data from many sources possible. This results in automated processes, fewer hand mistakes, and improved team collaboration. For instance, cloud data sharing provides fast access to the most recent consumer insights, enhancing response times and raising customer happiness. Companies that make quick, data-driven decisions using smart data gain an impressive competitive edge.

With the rise of AI-powered automation, low-code platforms, and hybrid cloud solutions, combining data has never been easier. These innovations help businesses scale, improve security, and uncover new revenue opportunities. In this blog, we’ll explore why data integration is essential, the risks of not having a proper strategy, and the best steps to create a seamless business data integration system.

1. Key Benefits of Effective Data Integration

1.1. Real-time Data Updates

Real-time data updating is the process of synchronizing and updating data between systems, ensuring that information is always accurate and up-to-date. This process helps businesses make quick and accurate decisions based on the latest data.

For example, when transferring data between on-premises storage systems and Amazon cloud storage services (S3, EFS, FSx), the DataSync service uses jobs to synchronize the data. This can be configured to transfer only the data that has changed, reducing the load when moving large amounts of information and ensuring timely updates. DataSync can handle up to 50 million files and folders per job, maintaining constant synchronization and updates between systems.

Benefits for businesses: Real-time data updates help businesses maintain information accuracy, optimize operational processes, and make accurate business decisions, thereby improving work efficiency and enhancing customer experience.

High-level overview of DataSync transferring data between AWS storage services and another cloud provider. Source: AWS

1.2. Enhanced Efficiency

Enhanced efficiency through data integration means streamlining processes by reducing manual work. Businesses save time on tasks like data entry, validation, and reporting by connecting different systems and automating workflows.

Example: Spotify employs cloud data sharing and smart data use by integrating data from users’ listening habits, device preferences, and social media activities. Spotify automatically syncs the data, personalizing the recommendations to enhance the efficiency of its content delivery and customer engagement. 

This merging of data from various sources boosts Spotify’s efficiency in maintaining user satisfaction and growing its customer base.

ELT pipeline of Spotify. Source: Medium

Why it matters for your business: By combining data from different touchpoints and automating tasks, businesses can reduce human error and labor costs while improving productivity across departments. This makes the company more agile and capable of handling larger workloads with fewer resources.

1.3. Better Customer Experience

A better customer experience comes from personalized interactions powered by data insights. Through business data integration, businesses can analyze and apply insights from various sources, allowing for tailored experiences for each customer.

Example: Netflix is a prime example of how data integration can enhance the customer experience. Netflix uses smart data by analyzing watching patterns, search behaviors, and user ratings to recommend personalized shows and movies to each user. Then use some tools like Data Vault (Amazon S3), Real-Time Stream (Apache Kafka), and Data Historian (Apache Hive) to synthesize data in huge data warehouses, ensuring data is always updated promptly without taking too much real time.

Massive data sources from users of Netflix. Source: Litslink

Why it matters for your business:

Personalization has become a key driver in customer loyalty. Integrating customer data allows businesses to break data silos and offer highly relevant services, which in turn leads to better customer retention, satisfaction, and increased sales.

1.4. Scalability & Flexibility

Definition: Scalability and flexibility in data integration refer to the ability to adapt and grow as the business expands. By consolidating data into a unified system, businesses can seamlessly scale operations, integrate new systems, and meet changing market demands without significant infrastructure changes.

Example: Tesla uses data integration to manage the large volumes of data generated by its cars and production systems. By merging data from electric vehicle performance, customer feedback, and production line metrics, Tesla is able to scale operations across the globe while ensuring that systems remain flexible and responsive to new market demands. This allows them to innovate continuously and meet the growing demand for electric vehicles.

Some data integration tools Tesla uses. Source: Medium

Why it matters for your business: Companies that invest in connecting data across platforms and integrating systems are better positioned for future growth. Scalable systems can easily expand as needed, allowing businesses to adjust to new challenges and opportunities without disruption.

2. Top Data Integration Tools for 2025

2.1. AWS Glue

AWS Glue is a serverless data integration service that helps businesses simplify ETL (Extract, Transform, Load), discover, prepare, and move data from multiple sources. It offers benefits such as AI-powered generative processing, automatic scaling, reduced operational costs, and consolidation of the entire data processing workflow into a single platform. 

AWS Glue is especially suitable for businesses that need to process big data for analytics, machine learning (ML), and application development, helping to speed up the cleaning and preparation of data accurately, saving time and costs.

Diagram showing how AWS Glue users can choose from interface options to create task workloads using various data integration tools. Source: AWS

2.2. Informatica PowerCenter

Informatica PowerCenter is a popular ETL (Extract, Transform, Load) tool that helps businesses build powerful data warehouses with seamless integration from multiple sources, including relational databases, cloud applications, XML, JSON, and more. 

The uses of the Informatica ETL tool. Source: Edureka

It offers many benefits, such as flexible integration, complex data processing, analytics support, and efficient data flow management. Informatica PowerCenter is especially suitable for businesses that need to integrate, transform, and load data from multiple sources, supporting data analysis processes and strategic decision-making.

2.3. SQL Server

SQL Server is a relational database management system (RDBMS) developed by Microsoft, helping businesses store, manage, and process data securely and efficiently. This tool brings many benefits, such as flexible scalability, reporting support (SSRS), data analysis (SSAS), data integration (SSIS), and data cleansing (DQS). 

The uses of the SQL Server tool. Source: Quest

SQL Server suits medium and large businesses, supporting small applications with SQL Server Express to complex systems with SQL Server Enterprise. In addition to on-premises deployment, SQL Server also has an Azure SQL version for cloud environments and Azure SQL Edge for IoT devices, helping businesses deploy flexibly on any platform.

2.4. FME

FME (Feature Manipulation Engine) is a powerful data integration platform that helps businesses transform, consolidate, and automate data flows from over 450 different formats without the need for programming skills. This tool offers many benefits, such as fast system integration, improved data quality, optimized decision-making processes, and time savings by automating data processing processes. 

FME comes in three versions: FME Form (drag-and-drop data processing workflow design), FME Flow (data processing automation and scheduling), and FME Flow Hosted (supports cloud-based deployment). FME is suitable for businesses with multiple data sources that need to be integrated, especially in areas such as GIS, Big Data, IoT, CRM, ERP, and enterprise management systems, helping to improve work efficiency and optimize data infrastructure.

What is the FME Platform? Source: DataBuilders

2.5. Fivetran

Fivetran is a cloud data integration platform that automates extract, load, and transform (ELT) data from multiple sources into a data warehouse (Snowflake, Google BigQuery, Amazon Redshift). 

How the Fivetran tool works. Source: Fivetran

It helps businesses consolidate data, automate ETL/ELT, synchronize data in batches, support analytics, and integrate data from SaaS applications without deep technical intervention.

With over 300 built-in connectors, Fivetran is suitable for businesses that want to offload data engineering work and focus on analytics and strategic decision-making, especially in the fields of e-commerce, finance, and SaaS.

2.6. Comparing Data IntegrationTools for 2025

3. How to Make the Right Moves for Data Integration Success

How Business Adapting Data Integration

Step 1: Define Clear Integration Goals

Before diving into data integration, define your business objectives: What do we want to achieve? Whether it’s faster reporting, better decision-making, or seamless system connectivity, setting clear goals will keep you on the right track. Identify your key data sources, from CRM and ERP to cloud applications, to ensure smooth and effective data flow.

Common Integration Touchpoints of CRM & ERP. Source: DCKAP

Step 2: Choose the Right Data Integration Approach

• ETL (Extract, Transform, Load) vs. ELT (Extract, Load, Transform)—Which is better?

The choice depends on your needs. ETL works well for structured, predefined reports, while ELT is better for big data and real-time analytics. API-based integration enables instant cross-platform data exchange, and cloud solutions provide scalability and flexibility without the hassle of on-premise maintenance.

Process of ETL and ELT. Source: Striim

Comparison Between ETL and ELT. Source: AWS

Step 3: Adopt Cutting-Edge Technology Trends

Modern businesses need modern solutions. AI-driven integration automates workflows, reducing manual work and errors. 

Low-code and no-code platforms let you manage enterprise data without deep technical skills. Even blockchain is revolutionizing secure data sharing, ensuring transparency and trust in transactions.

Further reading: Comparing Low-code and No-code Programming. Explore Case Studies

Step 4: Ensure Data Governance & Security

Data governance refers to the process of defining internal data standards and usage policies and then using technology and processes to maintain and manage the confidentiality, integrity, provenance, usability, and availability of data.

These policies and standards enable users to easily find, prepare, use, and share their own trusted data sets without relying on IT.

Example of Data Governance Framework. Source: Capella

Further reading: What Is Data Governance? 7 Exciting Data Governance Trends

Step 5: Continuously Monitor & Optimize

AI-powered analytics help monitor performance, identify bottlenecks, and optimize sync intervals based on business needs. Regular review of data mappings and transformation rules ensures accuracy as the system evolves. By continuously optimizing, businesses can turn raw data into actionable insights that drive smarter decisions and long-term success.

How AI can help businesses in data integration. Source: Leeway Hurtz

4. Real-World Examples of Successful Data Integration

4.1. Starbucks

Before implementing a comprehensive data integration strategy, Starbucks faced many operational challenges due to fragmented data systems between departments such as supply chain, inventory management, purchasing, and finance. This disconnect led to many inefficiencies, such as inefficient inventory management, wasted materials, and inaccurate demand forecasting.

As Starbucks expanded globally, data missynchronization between departments became a major barrier, making it difficult to optimize processes and control performance across thousands of stores. To solve this problem, Starbucks implemented SAP ERP, a powerful data integration system that helps consolidate information from multiple sources. This resulted in better decision-making and improved operational oversight.

Starbucks Analytics Suite — Azure Architecture

This ERP system provides real-time data on inventory, sales, and supply chain, helping Starbucks improve accuracy in demand forecasting, optimize material tracking, and streamline operations. Eliminating data silos and sharing data on the cloud platform helps the brand improve operational efficiency, reduce waste, and deliver a consistent experience to customers globally.

Azure Data Lake Storage

In addition, Starbucks also leverages AI and machine learning to analyze data, thereby personalizing the experience for each customer. For example, the mobile app can recommend drinks based on purchase history or weather conditions and send appropriate incentives to increase customer engagement.

Thanks to a well-planned data integration strategy, Starbucks has not only optimized operations but also driven strong growth. From 2011 to 2019, the application of ERP has contributed to helping the brand achieve 60% revenue growth, affirming its leading position and creating the premise for future innovations.

4.2. McDonald’s

McDonald’s, a global foodservice leader, faced high costs and limited agility when using a legacy ETL platform (IBM DataStage) to manage data from more than 37,000 restaurants worldwide.

To address this, McDonald’s decided to move to the cloud (AWS) and migrate to Talend for data integration, reducing costs and increasing agility.

Wavicle experts partnered with McDonald’s to execute the migration, dividing it into three phases: analysis, development, and testing. Wavicle used proprietary ETL transformation accelerators and testing tools to automate tasks, reducing development and analysis time.

The result was a 95% reduction in analysis work and an 81% reduction in development effort, while reducing ETL software licensing costs by 75%. Additionally, moving to Talend provides stronger support, easier maintenance, and reduced infrastructure costs, while improving global data integration and shortening system upgrade times.

Comparison table of analysis and development time when McDonald’s uses ETL Transformation Tool

Key Takeaways:

• Data integration is essential for smooth data flow, better decision-making, and operational efficiency.
• Poor integration costs businesses an average of $12.9 million annually due to inefficiencies and lost opportunities.
• Real-time updates, automation, and scalability improve business agility and customer experience.
• AI, low-code, and cloud solutions simplify integration, enhance security, and unlock new revenue streams.
• Choosing the right tools and strategy—ETL vs. ELT, on-premise vs. cloud—ensures seamless data management.

To Wrap Up

Effective data integration is no longer a luxury but a necessity for businesses looking to stay competitive. By leveraging the right tools, automation, and AI-driven solutions, companies can enhance efficiency, reduce costs, and unlock valuable insights.

If you’re ready to optimize your data integration strategy, Kyanon Digital is here to help. Our experts specialize in seamless data solutions tailored to your business needs. Contact us to future-proof your data management!