Data integration refers to the process of taking data from different sources—whether databases, applications, files, or services—and making it usable through transformation, orchestration, consolidation, and more. As data volumes and sources continue expanding, having the right integration approach is critical.
This guide will provide an in-depth look at data integration, including the purpose and value of key capabilities, evaluation criteria for major tool categories, and advice for undertaking an integration initiative. We’ll specifically explore:
- Data warehousing tools for aggregation and BI
- Data migration tools for moving between environments
- Enterprise application integration (EAI) tools and architectures
- Master data management (MDM) software
For each area, we’ll define the approach, analyze product offerings and tradeoffs, and provide direction on selecting solutions tailored to your needs. We’ll also discuss common data integration challenges and best practices around tackling them.
Let’s dive in.
The Growing Role of Data Integration
More data exists today than ever before. Consider that just a few years ago, 90% of the world‘s data didn‘t exist. And data volumes are projected to continue doubling every two years moving forward. However, despite this abundance of data, leveraging it remains difficult. Relevant information often sits across disconnected databases, applications, cloud services, files, and more. Even when centralized into data warehouses or lakes, key context may be lost.
This is where the right data integration approach enters the picture, offering consolidated and harmonized access to distributed data. Integration powers the collective ability to extract insights for improved decision making across the business. It can also drive compelling customer experiences through unified profiles.
Below are a few examples of how data integration delivers value:
- Centralized business intelligence (BI) – By aggregating data into dedicated warehouses, holistic analysis becomes possible through BI tools.
- Minimized middleware – Middleware allows communication between disparate apps and systems but adds complexity. With robust integration, less custom connectors are required.
- Master data domains – Master data like customers, products, suppliers, etc. can be defined once via MDM then synced across downstream systems.
- Cloud migration – Movement from on-premises environments to the cloud leverages specialized data migration software and services.
The catch is that with growing data comes growing integration complexity. More applications, continued M&A activity introducing new systems, and paradigm shifts like IoT and cloud all require adaptable integration.
Carefully selecting the right tools and partners lays this foundation. When armed with the proper integration strategy and platform, the business is equipped to activate distributed data rather than being disrupted by its fragmentation across silos.
4 Key Data Integration Capabilities
There are four primary capabilities delivered by data integration solutions:
- Data warehousing – Aggregation of data for business intelligence and analytics use cases
- Data migration – Movement of data between locations, formats, and applications
- Enterprise application integration (EAI) – Connectivity between applications and the orchestration of processes
- Master data management (MDM) – Management of core business entities as master records synchronized across systems
While integration projects may focus on just one area to start, leading with a broader data integration strategy ensures extensibility to additional capabilities over time. Below we explore each approach including key requirements, sample tools and vendors, and evaluation considerations when selecting solutions.
Data Warehousing Tools
Data warehousing consists of ingesting, consolidating, transforming, and storing data from transactional systems, applications, files, and other sources for business intelligence (BI) and analytics use. Warehouses allow historical trending, advanced aggregation for insights, and a single version of truth.
Key capabilities include:
- Ingestion from diverse data sources
- Transformation and enrichment
- Integrated metadata management
- Schema management
- Scalable storage optimized for analytics
- BI integration and embedded analytics
- Data lifecycle management from ingest to archive
Data warehouses have traditionally relied on relational database management systems (RDBMS) but new options have emerged for analytics-optimized systems. Appliances also package software with servers and storage. Finally, data warehousing as a service (DWaaS) delivers a managed offering.
Below are examples of leading data warehousing platforms across deployment models:
Cloud data warehouse vendors:
- Snowflake
- Google BigQuery
- Amazon Redshift
- Microsoft Azure Synapse Analytics
- Oracle Cloud Data Warehouse
On-premises data warehouse vendors:
- Oracle Exadata
- Teradata
- IBM Db2 Warehouse
- SAP HANA
- Vertica
Data warehousing appliances:
- Oracle Exadata Cloud@Customer
- Teradata IntelliFlex
- IBM Netezza
- Hitachi Solutions
Criteria for evaluation generally focuses first on cloud vs. on-premises deployment. Performance, scalability, security, data governance, and ease of use are also important. And with data warehouses fueling business intelligence, embedded BI and interoperability are key.
Data Migration Tools
While data warehousing tackles ongoing integration needs, data migration delivers one-time movement from one system or location to another. Common scenarios include consolidating data centers, shifting applications to the cloud, merging datasets after an acquisition, and more.
Data migration breaks down into a few subcategories:
- Database migration – Movement of databases and schemas between RDBMS platforms
- Storage migration – Transfer of data volumes from one storage system to another
- Application migration – Porting of full application environments across domains
Products that assist across these areas include:
Database migration tools:
- AWS Database Migration Service (DMS)
- Azure Database Migration Service
- Oracle GoldenGate
- Informatica PowerCenter
Storage migration tools:
- StarWind V2V Converter
- Carbonite DoubleTake
- Zerto Virtual Replication
Application migration tools:
- CloudEndure Migration
- Racemi DynaCenter
- Movere
- Turbonomic Application Resource Management
Given the one-time nature of migration initiatives, products focus heavily on minimally disruptive movement. Performance, reliability, and automation take priority over ongoing management as with data warehousing tools.
Enterprise Application Integration
While data migration tackles bulk data transfer, enterprise application integration (EAI) handles real-time sharing between systems. EAI minimizes the connections and middleware required for apps to communicate.
Core capabilities include:
- Connecting apps, data, IoT devices
- Message routing
- Data mapping and transformation
- Orchestrating business processes
- Applying business rules and logic
- Monitoring and analytics
EAI is made possible through tooling that serves as middleware for standardizing communication. The most common architectures include:
- Enterprise service buses (ESBs) – A messaging bus that provides shared plumbing between connected points
- Integration platform as a service (iPaaS) – Cloud-based integration with prebuilt connectors and automation
- API management platforms – Tools for publishing, securing, transforming, and monitoring APIs
Leading examples of EAI platforms include:
- MuleSoft
- Jitterbit
- Oracle Integration Cloud
- Celigo Integrator.io
- SnapLogic
- Microsoft BizTalk
- TIBCO
- Software AG
Selection criteria often weighs the ability to handle increasing complexity from new integration points without disruption. Scalability, embedded data services, and API and IoT connectivity are also key features.
Master Data Management Software
While the other integration disciplines focus on broader datasets, master data management (MDM) centralizes key business entities like customers, products, suppliers, locations, assets, and more. MDM solutions create master records that serve as a single version of truth synchronized across source systems.
Typical MDM capabilities include:
- Master data domain models
- Hierarchical master data relationships
- Workflow for data stewardship
- Matching algorithms that consolidate duplicates
- Data quality and validation
- Synchronization of master data back to systems
- Audit logging for compliance
Leading MDM software vendors include:
- Informatica
- SAP
- Oracle
- IBM
- TIBCO EBX
- Riversand
- Semarchy
Key aspects to evaluate include data model flexibility, automation capabilities, built-in data quality, and extensibility options. With master data powering business processes, having an MDM platform that scales is critical.
Challenges and Solutions in Data Integration
While modern data integration tools and cloud platforms have provided more accessible options for tackling fragmentation, few initiatives come without obstacles. Below we explore key challenges that can surface and approaches to overcome them.
Siloed data across domains and brands – Mergers, growth, and decentralized teams can lead to siloed applications and data. An enterprise data strategy coupled with data governance helps guide integration. New roles like Chief Data Officer can also drive alignment.
Legacy constraints and technical debt – Monolithic legacy systems often lack APIs and interoperability. While modernizing core platforms is ideal, interim solutions like application wrappers and ETL can enable integration.
Disjointed planning and lack of alignment – Integration that focuses solely on technical aspects will inevitably miss the mark. Cross-functional collaboration and executive sponsorship ensure stakeholder buy-in.
Underestimating complexity – Even targeted projects like master data management require understanding dependencies and data lineage. Thorough analysis and roadmap planning prevent scope creep.
Lack of skills and resources – From cloud architecture to MDM administrators, specialized skills help guide disciplined integration. Education, centers of excellence, and outside partners provide coverage where needed.
While certainly not exhaustive, these common issues should provide a sense of technical and organizational considerations required for data integration. Weaving business context with platform decisions remains critical for sustainable and value-generating integration capabilities over the long-term.
Evaluating Data Integration Solutions
With an abundance of tools and solutions now available—whether commercial software, open source, or fully managed cloud services—selecting the path forward has its challenges.
Determining requirements and business objectives is essential before evaluating options. Common criteria include:
- Use cases – Prioritizing business goals like customer 360 insights vs. operational reporting will help guide tool selection and roadmaps.
- Time-to-value – The learning curve and level of involvement in managing integrations varies. Reliance on IT and technical specialists is a consideration.
- Data profile – The types and volume of data needing integration, along with sources and security aspects, impacts options.
- Hybrid vs cloud-only – Most vendors support hybrid environments connecting cloud and on-premises systems but some remain cloud-native.
- Budget – Available budget influences the ability to leverage pre-built solutions requiring less internal development.
These should provide a baseline for aligning tools to the organization’s needs and resources. With data integration democratizing over time to make more capabilities accessible, the options for moving forward continue expanding.
Embarking on Your Data Integration Initiative
Developing an intentional data integration strategy lays the foundation for eliminating silos and powering analytics. But given the breadth of the space, determining where and how to get started can feel overwhelming.
The good news is that beginning with one capability area or project allows learning iteratively. Quick wins build confidence and alignment for broader efforts. For instance, starting by centralizing customer data into a master record hits urgent needs while proving value that supports expanding over time.
No matter where you focus first, bringing IT, business, and executive stakeholders together early in the planning process ensures shared vision. This also helps secure the budget, resources, and organizational adoption needed for success.
With the right business champions, use case prioritization, platform selections, and incremental roadmap, data integration can transform previously fragmented data into an integrated asset with outsized business impact.
