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A data integration strategy serves as a blueprint for your organization to combine data from various sources with the ultimate aim of making the most out of your data to inform decision-making, boost operational efficiency, and foster innovation. It involves defining your goals and principles, establishing methodologies, and selecting technologies for effective data integration across the enterprise.
Data integration strategies consider such factors as data types and sources, your software platforms, and use cases for integration to help you determine and implement methods for data extraction, storage, and connectivity. To succeed you’ll need to collaborate with stakeholders across the organization to develop strategies to ensure that data quality aligns with business requirements as outlined in the 10 essential steps detailed in this guide.
Table of Contents
1. Set Business Goals
Your data integration plan should support your organization’s overall business goals—understanding those goals lays the foundation for your plan to succeed. Identify key challenges, opportunities, and priorities that data integration can address. Here are some of the most common business objectives data integration can help achieve:
- Facilitate mergers and acquisitions
- Eliminate data silos
- Leverage new sources of data
- Develop analytics and business intelligence (BI) for organizational growth
- Increase data accuracy for better data quality
- Use data assets as a product or service
- Prevent cyber attacks and data breaches and minimize operational risks
- Meet industry regulatory requirements
- Decrease general operating expenses
- Streamline business processes
- Enable data-driven decision-making
Establish concrete business goals to be sure that your data integration strategy aligns with broader organizational objectives and focuses on delivering measurable results.
2. Evaluate Existing Data Infrastructure
Evaluating your existing data infrastructure and processes includes assessing the quality and cleanliness of the available data to uncover inconsistencies, redundancies, or inaccuracies, helping you avoid problems down the line. Asking the following questions can assist you in thoroughly reviewing your existing data structure:
- What are your organization’s existing data sources?
- How are these data sources currently managed and maintained?
- What types and volumes of data —structured, unstructured, and semi-structured, for example—are stored in each data source?
- How often is your enterprise data updated or refreshed?
- What are the data retention policies for each data source?
- Are there any data governance processes or frameworks in place?
- Are there any security concerns or vulnerabilities in the existing data infrastructure?
3. Identify Stakeholders
Key stakeholders involved in the data integration strategy from across the organization should have clear roles and responsibilities and know how they will contribute to the strategy development process. Each group plays a key role in the process and ensures that integrated data meets your business requirements. Generally, stakeholders and roles include the following:
- Business leaders and executives—Provide strategic direction, define business goals, allocate resources, and check alignment with organizational priorities.
- IT professionals—Implement and manage technical aspects, design and develop integration processes, select tools and technologies, and check data integrity and security.
- Data governance teams—Establish and enforce policies, standards, and processes related to data quality, security, and compliance.
- Business and data analysts—Identify data requirements and priorities, define business rules and metrics, and make sure that the integration strategy meets the needs of various functional areas.
- End-users—Provide feedback on usability and functionality of the integrated data systems or applications used for data integration.
4. Define Scope And Purpose
Clearly outline the scope, purpose, and anticipated outcomes of the data integration initiative. Specify the types of data to be integrated, including customer data, sales data, and operational data. Define the geographic, organizational, and functional boundaries of the integration effort.
Your outcomes should be measurable, concrete, and aligned with organizational goals. The following questions should be addressed during this phase:
- What exactly are types of data to be managed?
- Are there any geographic, organizational, or functional boundaries that need to be considered?
- Are there any data sources or systems that should be excluded, and if so, why?
5. Establish Governance Framework
Governance frameworks manage data security, privacy, compliance, and overall quality throughout the integration process. Establishing them entails defining policies, standards, and processes for controlling data access for privacy and protection.
In this step, you must clarify how the data governance responsibilities will be assigned and enforced within the organization. You should also be clear on the following:
- The types of enterprise data you will retain
- The retention periods for each data type
- Data archiving or deletion processes when the retention period ends
- Data retention regulatory requirements
6. Select Integration Technologies
Choose appropriate data integration tools and technologies based on such factors as costs, scalability, security, and your customization requirements. Keep vendor support, cost-effectiveness, and ease of integration with existing systems in mind
When selecting data integration technologies, consider the volume, variety, and velocity of enterprise data to be handled, along with the primary use cases or business processes that require integration. It is important to choose technologies that match the technical capabilities of your business and are compatible with your current systems.
Exploring different integration solutions, comparing features, capabilities, and pricing models, while considering future business growth and innovation goals are also major factors in making informed decisions in this stage.
7. Design Integration Architecture
Craft a structured plan for data flow between your corporate systems. This includes defining data models, schemas, and integration patterns. It also entails incorporating data cleansing and standardization processes for consistency and quality across integrated systems.
By addressing errors in the data through cleansing and establishing rules for standardizing formats and values, you can raise enterprise data quality and reliability. The goal is to build a robust framework that supports seamless data integration while meeting business objectives.
8. Plan Implementation
Provide clear details about your strategy’s implementation to ensure it is both practical and executable. Create a comprehensive roadmap to put it into action that breaks it down into smaller tasks such as data mapping, system configuration, and the actual data integration process including extract, transform, and load (ETL) development.
Additionally, tasks such as testing and deployment should be outlined. Include benchmarks or milestones, like completing data cleansing or achieving system integration, and task deadlines for the execution to remain on schedule.
Specify the resources necessary for a successful execution, like budget provisions for technology investments, personnel, training, and any required external services or consultants. Make sure everyone knows what they’re supposed to be doing and how they fit into the big picture. Obtain the necessary tools, software, and infrastructure to support data integration activities.
9. Test And Validate
You should meticulously evaluate the performance of your data integration processes. Define criteria and metrics to assess the quality, accuracy, and reliability of combined data.
Conduct data validation, functionality testing, and performance testing to check if the integrated data meets predetermined standards and objectives established during the initial stages of developing the strategy. These tests must simulate real-world scenarios to accurately reflect the actual conditions and environments in which the integrated data systems will operate.
10. Monitor And Iterate
The final step of creating a data integration strategy involves establishing monitoring mechanisms to track integration performance and gauge its ongoing impact. This necessitates deploying processes to gather feedback, find issues, and make continuous enhancements to the strategy in response to evolving business needs.
Monitor key performance indicators (KPIs) related to data quality, system performance, and business outcomes. Carry out regular reviews and assessments to unearth any issues or areas for improvement, so you can make timely adjustments and refinements to your strategy.
Frequently Asked Questions (FAQs)
What are Common Data Integration Methods?
There are a wide range of data integration methods available depending on the specific requirements and characteristics of your data sources involved.
Manual Data Integration
This method involves manually moving data around using basic tools like spreadsheets or simple copy-paste methods. While it’s straightforward, it can be time-consuming and prone to errors. This method is often used for small-scale data integration tasks or ad-hoc data transfers between systems.
Change Data Capture (CDC)
This method tracks real-time changes in data using specialized CDC tools, so you always know what’s happening across varying systems or databases without having to constantly check. This is common in environments where real-time data replication and synchronization are imperative, such as in financial services for tracking stock market changes.
Extract, Load, Transform (ELT)
ELT encompasses extracting data from source systems and loading it into a target system without significant transformation. Transformation tasks are then performed within the target system, often using SQL queries or data processing frameworks. ELT is typically employed in data warehousing and analytics environments where large volumes of raw data need to be loaded quickly into storage systems.
Application Programming Interface (API) Integration
API integration entails connecting to external systems or services to exchange data in a structured and controlled manner. It’s used for integrating cloud-based applications, web services, and third-party platforms with internal systems.
Extract, Transform, Load (ETL)
ETL is a traditional data integration approach that involves extracting data from multiple sources, transforming it to meet the desired format or structure, and loading it into a target system such as a data warehouse or database. ETL is widely used in enterprise data integration; specifically data warehousing, BI, and data migration projects.
Data Virtualization
This method brings a unified and virtual view of data, enabling you to analyze information as if it were stored in a single location. While it offers agility and flexibility, it may require more sophisticated technology and expertise compared to other methods. Data virtualization is common in integrating patient records from separate electronic medical record systems in healthcare organizations, or for aggregating data from multiple banking platforms for reporting and analysis in financial services.
What Are Data Integration Tools?
Businesses rely on a range of tools to manage their data integration needs. Here are some of the most common:
- ETL tools—Extract, transform, and load data from various sources into a consistent format—for example, Informatica Intelligent Data Management Cloud and Talend Data Integration.
- Enterprise Service Bus (ESB)—Middleware platform that integrates systems and applications through centralized messaging— for example, Oracle Service Bus and Mule ESB.
- Data integration platforms—Manage and merge data across an organization—for example, SnapLogic and Oracle Data Integrator.
- API management platforms—Expose and manage APIs for connecting internal data and services with external partners—for example, Zapier and Jitterbit.
- Data virtualization tools—Access and combine data from varied sources without moving or replicating the data—for example, Denodo Platform and AtScale.
- Master data management (MDM) solutions—Consolidate master data to ensure consistency and accuracy—for example, SAP Master Data Governance and IBM MDM.
- Cloud integration platforms—Integrate data and applications in cloud, on-premises, and hybrid environments—for example, Dell Boomi and Microsoft Azure Logic Apps.
Bottom Line: Strategically Integrate Data For a More Holistic View
A well-crafted data integration strategy can empower your organization to consolidate and harmonize data from diverse sources, ensuring consistency, accuracy, and completeness. Without a coherent strategy to integrate this disparate data, you risk siloed data, leading to inefficiencies, errors, and missed opportunities for insights.
A streamlined strategy lays the groundwork for successful data science and data engineering initiatives by providing access to high-quality data, facilitating data preparation and feature engineering. It also ensures following data governance best practices and maintaining compliance. There’s no one perfect answer to what exactly you should include in your data integration strategy because every organization has unique data requirements, objectives, and constraints, but this guide outlines the steps you’ll need to develop an actionable strategy tailored to your business—one you can actually use.
Ready to put your data integration strategy into action? Read our recommendations for the best data integration tools to find reliable solutions that can meet your needs.
