The labyrinth of enterprise data has many tentacles. Data originates in various business units, from supply chain operations to customer service and human resources. All too often, critical information is siloed off, leading to poor business decisions. As companies increasingly leverage Artificial Intelligence (AI), robotics, data analytics, and other digital technologies, data are at risk of becoming even more fragmented. To overcome this challenge, technology providers like Amazon Web Services (AWS), Microsoft Azure, IBM, Denodo, Qlik, and Informatica offer data integration tools.
Data integration software—a core component of an enterprise data fabric—integrates data across an organization. Once collected, these data construct a data pipeline that can be used for trend analysis, equipment maintenance, predictive analytics, enhanced customer support, supply chain optimization, and many more applications.
ABI Research anticipates significant uptake in enterprise data integration tools. Growing at a robust Compound Annual Growth Rate (CAGR) of 22.6%, data integration revenue will increase from US$12.7 billion in 2023 to US$53 billion by 2030 across the manufacturing, healthcare, and education industries.
To successfully tap into this booming market, data software solution providers must solve the specific pain points that enterprises face. As a couple of notable examples, enterprises have to manage diverse data formats and often lack the necessary skills to properly manage data lakes/warehouses.
In this context, ABI Research Principal Analyst Yih-Khai Wong walks you through the capabilities of the best data integration platforms, ensuring solutions align with enterprise requirements.
1. Support for Complex and Diverse Architectures
Enterprise data originates in various environments and comes in many formats. That being so, it is essential that a data integration tool can extract data across diverse departments and data types to unify critical information. For example, enterprises must be able to collate and interpret both JavaScript Object Notation (JSON) and Extensible Markup Language (XML) data to ensure data flows between various applications, programs, and platforms. Without support across broad data sources and formats, enterprises would not see the complete picture of their operations.
A notable case study is the partnership between Belgian Fast-Moving Consumer Goods (FMCG) company InBev and data management vendor Qlik. After acquiring numerous entities, InBev struggled to integrate various systems and data from these newly acquired firms. The ETL-supported Qlik Talend Data Integration and Data Preparation solution enabled InBev to consolidate 100+ systems into a single platform. As a result, the company reduced data development time from 6 months to 6 weeks.
It is also vital that an integration platform supports new architecture standards, which are rapidly evolving. For instance, Extract, Transform, Load (ETL) is championed by many data integration vendors. However, forward-thinking companies like Informatica are devising new standards to accommodate complex cloud deployments.
2. Data Governance and Data Lineage
Data governance ensures consistency and standardization across the organization. Data lineage plays a critical role in tracking the flow of data—from its origin to its final destination. This allows businesses to assess the impact of data movement on systems and processes.
For instance, implementing a lineage tracking platform can capture metadata during a data mapping process, enabling analysts to pinpoint discrepancies and correct errors during the data flow analysis phase.
3. Seamless Integration of Legacy Data Sources with Cloud-Native Systems
The core strength of data integration tools lies in their ability to bridge traditional infrastructure and modern cloud-native systems, ensuring no valuable data go unnoticed. A robust integration software tool must seamlessly connect to on-premises legacy databases, while also integrating with cloud-based data.
This can be done through Application Programmable Interface (API) calls, creating an integrated virtual data layer. A unified framework allows analysts to access a consolidated view of data from both legacy and cloud platforms without disruption.
4. Real-Time Data Streaming and Transfer Workloads
Real-time data processing capabilities are becoming a defining feature for competitive data solution providers. For example, manufacturing integration tools must be able to collect and process diverse data types—such as sensor readings, imagery, and production metrics—using advanced platforms like Apache Kafka or Redis. The output from these systems powers dynamic, real-time dashboards and alerts, offering manufacturers immediate insights to optimize production workflows.
5. Self-Service User-Driven Integration Tools
An easy-to-navigate interface is essential for enhancing user experience and driving tool engagement. While most traditional data integration tools require users to have technical expertise and familiarity with Structured Query Language (SQL), modern solutions should be designed for simplicity.
Incorporating drag-and-drop capabilities and natural language queries helps streamline the integration process, making it accessible to a wider audience. This intuitive design minimizes the need for customer support and ultimately improves user satisfaction.
Tap into ABI Research’s Data Integration Expertise
As the demand for seamless, real-time data management continues to proliferate, organizations must prioritize data integration tools that bridge legacy systems with modern cloud-based platforms. With the right strategies in place, businesses can unlock new efficiencies, build inter-department synergies, enhance decision-making, and ensure a unified view of their operations. However, creating one of the best data integration software offerings requires careful consideration of various factors—from governance and data lineage to real-time streaming and user-friendly interfaces.
To dive deeper into how you can create a robust data integration tool, explore key use cases, and examine real-world examples of successful implementations, refer to ABI Research’s following resources:
- Data Fabric Use Cases, Benefits, and Examples for Enterprises [Blog]
- The Rise of Cloud Observability Platforms: How Observability Providers Can Leverage Enterprise Data [Insight]
- Integrating Enterprise Data With A Hybrid Cloud Approach [Whitepaper]
About the Author
Yih-Khai Wong, Principal Analyst
Yih-Khai Wong is a Principal Analyst in the Strategic Technologies team. He is responsible for the distributed & edge computing research service, covering the evolution of processing platforms that handles various IT and OT workloads across the public, private, edge, and on-premises cloud.
