What is data integration?
Data integration is the process of merging data from various sources within an organization to create a comprehensive, precise, and current dataset. This unified data is essential for business intelligence, data analysis, and other applications or processes.
The integration process involves replicating, ingesting, and transforming diverse data types into standardized formats, which are then stored in a target repository like a data warehouse, data lake, or data lakehouse.
How does data integration work?
Organizations face a significant challenge in accessing and making sense of the vast amounts of data they capture daily. This data comes in various formats and from numerous sources. To create value from this data, organizations must find ways to bring relevant information together, no matter where it resides, to support reporting and business processes.
However, the necessary data is often scattered across multiple platforms, including on-premises applications, cloud databases, IoT devices, and third-party providers. Instead of storing data in a single database, organizations now manage both traditional master and transactional data, along with new forms of structured and unstructured data, across multiple sources. For example, an organization might store data in a flat file or need to access data from a web service.
There are two primary approaches to data integration:
- Physical data integration: This traditional method involves physically moving data from its source to a staging area. Here, the data undergoes cleansing, mapping, and transformation before being transferred to a target system, such as a data warehouse or data mart.
- Data virtualization: This approach uses a virtualization layer to connect to physical data stores, creating virtualized views of the underlying data environment. Unlike physical integration, data virtualization does not require the physical movement of data.
One common data integration technique is Extract, Transform, and Load (ETL). In ETL, data is extracted from multiple source systems, transformed into a different format, and then loaded into a centralized data store. Businesses can optimize their operations and gain valuable insights by employing advanced data integration techniques to unify information from various sources into a cohesive dataset.
The Importance of Data Integration
The data integration process is crucial for businesses aiming to stay competitive and relevant in today's data-driven world. As companies embrace big data and the opportunities it brings, data integration becomes essential for handling large datasets, enhancing business intelligence, customer analytics, data enrichment, and delivering real-time information.
SyncMatters is an iPaaS solution designed to streamline the data integration process, especially for businesses using CRM systems. With support for over 45 different CRMs, including major platforms like HubSpot, Salesforce, and Microsoft Dynamics, SyncMatters ensures that your data moves seamlessly between platforms, enhancing both operational efficiency and accuracy.
A key use of data integration methodologies is managing business and customer data. By feeding integrated data into data warehouses or virtual data integration systems, companies can support enterprise reporting, business intelligence, and advanced analytics. This helps business managers and data analysts get a full view of key performance indicators (KPIs), financial risks, customer behaviour, supply chain operations, regulatory compliance, and other vital business processes.
In the healthcare industry, data integration plays a significant role by merging data from various patient records and clinics. This process helps doctors diagnose conditions by providing a unified view of patient information. It also improves the accuracy of claims processing for insurers and ensures consistent and correct patient records, enabling smooth information exchange between different systems, known as interoperability.
Five Data Integration Approaches
There are five main approaches, or patterns, for executing data integration: ETL, ELT, streaming, application integration (API), and data virtualization. Data engineers, architects, and developers can either manually create an architecture using SQL or, more commonly, use a data integration tool to automate and streamline the process.
These five primary types of data integration include:
- ETL (Extract, Transform, Load): ETL is a traditional data pipeline process where raw data is extracted from sources, transformed in a staging area, and then loaded into a target system, usually a data warehouse. This method is best for smaller datasets that require complex transformations, allowing for quick and precise data analysis. A related method, Change Data Capture (CDC), identifies and captures changes made to a database, which can then be applied to another data repository or used by ETL tools.
- ELT (Extract, Load, Transform): In an ELT pipeline, data is immediately loaded into the target system, such as a cloud-based data lake or data warehouse, and then transformed. This method is more suitable for large datasets where speed is crucial. ELT can operate on a micro-batch or CDC basis, with micro-batching loading only the data that has changed since the last load, and CDC continually updating data as changes occur.
- Data Streaming: Unlike batch processing, data streaming involves continuously moving data in real-time from the source to the target system. Modern data integration platforms support real-time data delivery to streaming platforms, cloud environments, data warehouses, and data lakes, making it ideal for real-time analytics.
- Application Integration (API): Application integration connects different applications by synchronizing data between them, ensuring consistency across systems like HR and finance. This approach relies on APIs to move data between applications, and tools like SaaS application automation can help manage these integrations efficiently at scale.
- Data Virtualization: Data virtualization provides real-time access to data without physically moving it. Instead, it creates a unified view of data from different systems, delivering information on demand. Like streaming, this approach is well-suited for high-performance transactional systems that require quick data retrieval.
Each of these five data integration methods continues to evolve with advancements in the modern data ecosystem. While the classic ETL pipeline is still relevant for smaller datasets needing complex transformations, the rise of Integration Platform as a Service (iPaaS), along with new data architectures like data fabric and data mesh, has shifted the focus toward ELT, streaming, and API-based integration to support real-time analytics and machine learning projects.
Benefits of Data Integration
Developing a strong data integration capability offers organizations several key advantages:
Facilitate Collaboration Across Departments and Systems
Data integration ensures that employees across different departments and locations can access the necessary business data for both collective and individual projects. Since every department generates information valuable to the entire organization, data integration fosters the coordination and unification of data across the enterprise.
Save Time and Effort
Effective data integration significantly reduces the time required to gather and analyze data. By automating the management of centralized data views, it eliminates the need for manual data collection. Professionals no longer need to establish connections manually each time they need to generate a report or develop an application.
Access Reports Quickly
Without a seamless data integration system, reporting needs to be redone frequently to reflect any updates. However, with automatic updates, reports can be generated in real-time whenever needed, ensuring that information is always current.
Enhance the Value of Information
Over time, integration of data increases the value of enterprise data. As data is consolidated into a centralized system, any qualitative issues are identified and corrected, leading to more accurate and reliable data, which is essential for high-quality analysis.
Leverage Big Data Effectively
Data lakes often contain complex and massive amounts of data, such as the vast amounts processed by companies like Facebook and Google. This unstructured data, known as "big data," requires smart data integration to manage and extract value from it effectively.
Strengthen Business Intelligence (BI) Applications
Data integration streamlines BI processes by providing a consistent and unified view of data from multiple sources. This allows organizations to quickly deploy datasets to generate meaningful insights, helping them better understand and respond to current business situations.
Challenges of Data Integration Process
Complexity of Using Data Integration Platforms
Finding skilled data professionals is challenging and expensive, yet these experts are often needed to implement most data integration platforms. Business analysts, who require data for decision-making, frequently rely on these specialists. The process of integrating data from enterprise sources can take up to six months, delaying the benefits of data analytics.
Managing Data at Scale
Organizations struggle to make high-quality data easily discoverable and accessible for analytics. As the number of data sources and silos increases, companies face tough choices: either move and duplicate data across silos to enable advanced analytics or keep the data distributed, which limits agility.
Integrating Data with Various Delivery Styles
There is a growing demand for multiple data delivery methods, such as batch, streaming, and event-based, all within a single platform. As more business activities leave digital traces, organizations are increasingly seeking real-time data integration and analysis to improve business outcomes.
Data Semantic Challenges
Data can exist in multiple formats or versions that represent the same information but are organized differently. For instance, dates might be recorded as "dd/mm/yy" or spelled out as "month, day, year." The "transform" step in ETL processes and master data management tools help address these inconsistencies.
High Costs of Data Integration Infrastructure
The capital and operational expenses required to purchase, deploy, maintain, and manage the infrastructure for large-scale data integration can be substantial. Cloud-based data integration services offer a solution to reduce these costs by providing managed services.
Data Tied to Specific Applications
In the past, data was often so closely tied to particular applications that it couldn't be easily accessed or used elsewhere in the business. However, today, we're seeing a shift toward decoupling the application and data layers, allowing for more flexible use of data across the organization.
Best Practices for Data Integration
Effective data integration goes beyond just merging data from different sources and storing it in a centralized location. Success requires thoughtful planning and following data integration steps and best practices.
Define Clear Objectives
Data integration often involves complex processes, varied data sources, and significant investments in resources. Therefore, it’s crucial to establish clear objectives at the beginning of the project. Setting clear goals provides direction and purpose, helping to manage expectations and ensure the project delivers meaningful business value.
Choose the Right Integration Method
There are several integration methods available, such as ETL, API-based integration, and real-time data streaming. It’s important to choose the approach that aligns best with your organizational goals and data sources. For instance, a financial institution may need to consolidate data from multiple branches and systems to detect fraud in real time. In this scenario, real-time streaming would enable rapid detection, safeguarding the institution against financial losses and reputational risks.
Prioritize Data Quality
The effectiveness of your integration efforts depends on the quality of the data being integrated. The principle of "garbage in, garbage out" applies here. It’s essential to implement data quality checks, cleansing, and validation processes to ensure consistency and accuracy across the integrated data.
Ensure Scalability
Consider your organization’s scalability and performance needs. As data volumes increase, your system architecture should be capable of handling the growing load without compromising performance. Opt for a scalable integration architecture that can accommodate data growth without causing performance issues. This might involve using distributed systems, cloud-based solutions, or data warehousing technologies designed for scalability.
Focus on Security and Compliance
Implement strong security measures, including encryption and access controls, to protect data privacy and ensure compliance with relevant regulations such as GDPR and HIPAA. Your organization must adhere to industry and regulatory standards when integrating data.
Use Cases of Data Integration
Data integration is widely used across various industries to meet diverse business needs and solve different challenges. Some of the most common data integration examples include:
- Data Warehousing: When constructing a data warehouse, data integration is essential for creating a centralized repository that supports analytics and basic reporting.
- Data Lake Development: In big data environments, data integration is used to move structured, unstructured, and semi-structured data from siloed on-premises systems into data lakes. This makes it easier to perform advanced analytics, including artificial intelligence (AI) and machine learning (ML), to extract valuable insights.
- Customer 360° View: By consolidating customer data from various sources like CRM systems, marketing databases, and support platforms, organizations can create a unified view of each customer. This well-integrated data helps companies enhance their marketing strategies, identify cross-sell and upsell opportunities, and improve customer service.
- Business Intelligence and Reporting: Data integration is important for generating comprehensive BI reports and dashboards, which offer insights into different areas of a business, such as sales, marketing, finance, and operations.
- Processing IoT Data: Integrating data from Internet of Things (IoT) devices enables organizations to monitor and manage connected devices, analyze sensor data, and automate processes based on real-time insights.
Key Takeaway
Data integration management has become essential for today’s business operations. Rather than analyzing data in isolated segments, it allows for the combination of multiple data sources and types to gain a comprehensive view. For instance, instead of focusing solely on a customer's location, data integration can merge demographic details, social media activity, browsing history, and other relevant data to build a complete customer profile. This is just one example of how organizations can leverage data integration to explore new opportunities and generate value.