Top data integration challenges and solutions

Data integration is the process of combining data from various sources into a consistent, unified view. The ability to do so effectively (and across diverse and distributed environments) has become a strategic imperative in the age of rising data volumes and accelerating artificial intelligence (AI).

Market trends underscore this urgency: Global spending on data and analytics is projected to reach USD 134.6 billion in 2025 and climb to USD 219.4 billion by 2029.¹ AI investment is also accelerating, rising from 12% of IT spending in 2024 to a projected 20% by 2026, reports the IBM Institute for Business Value (IBV).²

However, these investments don’t automatically translate into success. Integration gaps remain a major barrier. More than half (53%) of surveyed executives in a study conducted by the IBV said difficulties integrating AI infrastructure with legacy systems derailed target outcomes.³

The challenges extend beyond AI infrastructure. Cybersecurity research from the IBV found that nearly 67% of surveyed executives believe their organization needs better integration across hybrid cloud, AI and security platforms.⁴ Without a solid modern data integration strategy and the right data integration tools, these obstacles can lead to time-consuming processes, frustrated stakeholders and unreliable insights that hinder business performance.

While data integration offers clear benefits such as breaking down data silos, improving data quality, streamlining workflows and enabling better decision-making, it also comes with significant challenges.

As IBM Product Marketing Manager Chandni Sinha recently wrote, “data integration is the circulatory system of your business. If it’s slow, fragmented or fragile, every business initiative suffers, from AI to analytics to customer experience.”

Below are some of the most common data integration challenges—and practical ways to address them.

• Poor data quality
  
• Incompatible data formats and structures
  
• Cloud-only integration in hybrid environments
  
• Managing large data volumes
  
• Real-time data requirements
  
• Security and compliance concerns

Collecting and ingesting raw data from different sources can introduce data quality issues. These issues—such as discrepancies, duplicates, inconsistent data or missing values—can compromise the integrity of integrated datasets.

Organizations may also encounter outdated records, conflicting information across systems and diverse data captured in different formats or with varying levels of completeness. Without strong data quality management throughout the integration process, the integrated environment can perpetuate and even amplify existing errors—ultimately affecting analytics and reporting downstream.

• Implement data profiling, cleansing and standardization procedures to identify and correct discrepancies, duplicates, missing values and ensure uniform formats.
  
  
• Establish data quality rules, validation checks and governance frameworks, including master data management (MDM) to enforce consistency and accountability.
  
  
• Conduct regular data audits and use data quality tools for deduplication, normalization and enrichment.
  
  
• Define automated processes for monitoring and correcting data issues across integrated systems.

Organizations pull information from various data sources such as cloud services, social media platforms, customer relationship management (CRM) systems and more—each with unique formats, data structures and schemas.

For example, legacy systems often use older file formats such as XLS or proprietary database files while modern applications use JSON, XML or cloud-native structures. This structural heterogeneity requires extensive mapping and integration efforts that can be time-consuming and error prone.

Additionally, organizations must contend with unstructured data such as text files and images, which adds another layer of complexity to integration. Unstructured datasets generally lack predefined schemas, making it harder to parse, categorize and standardize them.

• Use integration platforms with transformation engines and data mapping tools to automate conversions.
  
  
• Maintain a data dictionary or metadata repository for consistency.
  
  
• Adopt standardized data models or industry schemas to simplify transformation efforts.

Organizations that adopt cloud-based integration tools for their elasticity and speed might encounter challenges when operating in hybrid environments if those tools are not designed for on-premises or edge systems. Many enterprises have regulated on-premises workloads, mission-critical legacy systems and edge devices generating time-sensitive data—all systems that typically exist outside of cloud solutions.

Additionally, using multiple data integration tools for different environments leads to tool sprawl and fragmented processes, making it harder to maintain consistency and control.

• Select a unified integration platform that works across cloud, on-premises and edge systems.
  
  
• Use data virtualization to create a unified view of data from various sources, including on-premises and edge data, without physically moving it, enabling real-time access and integration across environments.

Data integration workflows often struggle when faced with massive datasets, unless they are designed to be scalable. Large volumes of data—especially when combined with diverse formats from multiple source systems—can overwhelm data ingestion pipelines, slow processing and increase the risk of errors. Without strategies to efficiently handle bulk data movement and transformation, organizations may experience delays, inconsistent outputs and higher infrastructure costs.

• Optimize for bulk data processing and choose the right approach: extract, transform, load (ETL) or extract, load, transform (ELT) to design pipelines that efficiently handle large batches of data. (ETL transforms data before loading, while ELT leverages the power of target systems such as cloud platforms or data warehouses for data transformation at scale.)
  
  
• Use high-throughput connectors to move data from multiple sources quickly and reliably.
  
  
• Implement parallel processing and partitioning to break down large datasets into manageable chunks for faster integration.
  
  
• Apply query optimization and indexing to speed up data retrieval and transformation for big datasets.
  
  
• Monitor data flow and storage capacity regularly to prevent bottlenecks and ensure smooth handling of growing volumes in systems such as a data lake or data warehouse.

Many businesses depend on real-time or near-real-time data synchronization to support immediate decision-making—such as fraud detection—and operational workflows. This becomes especially critical for AI workloads, which require continuous streams of timely data.

However, achieving real-time data integration is technically challenging. High data volumes require low-latency processing for optimal performance. Many legacy systems lack support for real-time operations and distributed architectures introduce additional latency and network reliability issues. These factors, and the need for continuous synchronization, increase system demands, affecting performance and fault tolerance.

• Implement event-driven architectures, message queues or change data capture (CDC) mechanisms.
  
  
• Use streaming platforms like Apache Kafka for efficient real-time data movement.
  
  
• For systems that can’t support true real-time, adopt micro-batch processing for near-real-time updates.

Integrating data across multiple systems adds more access points and layers that are vulnerable to attack, increasing data security risks where sensitive information could be exposed or compromised through unauthorized access. Organizations must ensure that data flows between systems comply with regulations such as GDPR, HIPAA, PCI DSS or industry-specific requirements.

As integration projects scale, maintaining proper access controls, encryption standards and audit trails across connected systems becomes significantly more complex. When data moves between different regions, jurisdictions or cloud environments, it may also be subject to differing legal and data residency requirements, adding another layer of compliance complexity.

How to solve:

To mitigate security and compliance risks, organizations should:

• Implement encryption for data in transit and at rest, along with strong authentication and authorization at every integration point.
  
  
• Establish a comprehensive data governance framework defining access rights and conditions.
  
  
• Conduct regular security audits and compliance assessments and apply data masking or tokenization for sensitive information.
  
  
• Use integration platforms with built-in security features and compliance certifications to reduce manual overhead.
  
  
• Consider client-managed data integration platforms that allow local hosting instead of cloud deployment, which gives greater control over security protocols, compliance enforcement and infrastructure management.

When data integration challenges arise, they can ripple across the organization, impacting everything from user productivity to strategic outcomes. Problems caused by data integration failures include:

A successful data integration process goes beyond technical efficiency; it transforms how an organization operates. When data integration solutions work effectively, people, processes and technology align within a unified ecosystem, enabling seamless information flow across systems. As a result, silos can be reduced, data-driven decision-making becomes easier and data quality issues are managed or eliminated.

Other key signs include:

• Heightened collaboration across the organization as teams gain shared access to consistent, trusted and high-quality data drawn from diverse sources, supported by standardized data entry practices.
  
  
• Deeper insights into customers and operations, enabled by integrating customer data from different systems and diverse datasets for more informed strategies and personalized experiences.
  
  
• Accelerated innovation initiatives that leverage integrated data insights to design and launch new products or services that meet evolving customer needs.
  
  
• Improved agility in responding to market changes, supported by timely, accurate information that originates from streamlined data collection processes.
  
  
• Enhanced governance and security, ensuring compliance while preserving data access for those who need it.