Data integration is the crucial process of combining data from disparate sources into meaningful, unified information that drives business value. As data volumes and sources continue expanding exponentially, integrating this data efficiently is growing increasingly vital for organizations.
In this comprehensive guide, we’ll explore what exactly data integration entails, walk through its key components, examine a real-world use case, outline best practices for implementation, discuss the transformative role of machine learning, and provide additional resources for putting data integration to work powerfully.
What is Data Integration and Why Does it Matter?
Data integration refers to the tools, techniques, and strategies for unifying data across an organization’s various systems and applications into one cohesive whole. It enables deriving enhanced business intelligence by connecting fragmented data points into a single source of truth.
The overarching goal is to eliminate data siloes and deliver complete, consistent insights to inform strategic decision-making across the enterprise. Data integration powers functions like analytics, reporting, forecasting, and more by harmonizing data and distilling it into accessible, consumable formats.
As data volumes explode and sources multiply, integrating information efficiently becomes crucial for:
- Creating a unified view of organizational data
- Enhancing analysis, projections, and decision making
- Understanding a comprehensive profile of the business and its customers
- Making data readily available and usable across the company
With sound data integration, teams access reliable, unified data to drive everything from day-to-day operations to long-term innovations. Ultimately, it lays the data foundation for extracting maximum business value.
4 Key Components of Data Integration
Now that we’ve covered the essentials of what data integration entails and why it matters, let’s explore four of its core components central to modern data architectures:
1. Data Migration
Data migration focuses specifically on moving data between locations, formats, applications or storage architectures. Common migration drivers today include:
- Transferring data into cloud platforms
- Upgrading storage infrastructure
- Transitioning applications
Data integration encompasses data migration, but focuses more holistically on unifying data from all sources into usable business insights.
2. Enterprise Application Integration (EAI)
EAI enables connecting the numerous applications and systems across an enterprise portfolio into a cohesive, interoperable whole. This requires:
- Managing differing data formats, system languages, etc.
- Creating consistent data flows across applications
- Ensuring flexibility as business needs evolve
Modern EAI leverages integration platforms to seamlessly unite data across disparate systems.
3. Master Data Management (MDM)
MDM focuses specifically on the critical business entity data that powers key functions. This includes customer, product, supplier and other domains. MDM synchronizes this data across systems into a unified point of reference.
It also institutes proper data governance for stewarding master data as a strategic asset. MDM lays the foundation for a trustworthy single source of truth used across the business.
4. Data Aggregation
Data aggregation entails consolidating data from multiple sources into one centralized repository to enable holistic analysis. It helps paint a complete picture of business performance by removing data siloes. There are two primary aggregation approaches:
- Data Warehousing: Pulling data into a dedicated analytical database optimized for reporting and business intelligence.
- Data Federation: Creating a virtual view that access distributed data sources through one interface without moving the data.
Each approach serves specific use cases and user needs.
Data Integration in Action: Case Study
To see how data integration delivers value, let’s examine the example of Behavior Analyst Certification Board (BACB). BACB used outdated, siloed CRM and web systems that broke easily, obstructing business processes and data flows.
They implemented an integration platform from Adeptia to:
- Sync data between their Microsoft CRM and NetSuite environments
- Enable seamless third-party integrations
- Connect internal systems to their website
The outcomes included:
- Increased ROI from optimized IT spend
- Accelerated processes from real-time data availability
- Enhanced customer experiences from unified web/CRM data
- Proactive versus reactive IT posture
This example demonstrates how even relatively straightforward integration approaches drive tremendous business results.
Best Practices for Data Integration
When implementing data integration, a few key tenets enable success:
- Consider it an ongoing process that incrementally adds value
- Institute integration alongside data governance foundations
- Focus on enhancing sustainability from accurate, united data
- Ensure collaboration between technical and business teams
Additionally, match integration approaches to use cases. Broadly, these entail:
- Analytics: Enabling business intelligence and reporting
- Operations: Making data readily consumable across systems
- Master data: Creating unified customer, product and other data
Using the proper techniques for each objective helps ensure solid data integration.
The Role of Machine Learning
Integration platforms are increasingly infused with machine learning to automate repetitive tasks like ETL scripting and drive more impactful insights. Key capabilities include:
- Automatic data harmonization from diverse sources
- Accelerated data processing speed and accuracy
- Enhanced analytics from integrated data lakes
- Uncovering hidden correlations and patterns
As integration needs scale, machine learning will become integral for taming complexity.
Continue Your Data Integration Journey
For those interested in learning more about holistically leveraging data integration, a few recommended starting points:
Additionally, feel free to explore leading data integration platforms. Our experts can help assess solutions tailored to your environment and use cases via a quick 15 minute discussion.
I hope this guide has shed light on the immense power of data integration and gotten you thinking bigger about what it makes possible for your organization! Please reach out directly if any questions at all.
