Data is vital to modern organizations, but it’s equally important to get the right data to the right decision-makers in the correct format and configuration. Creating a secure system to share, process and effectively utilize this information is far more critical than simply moving data to the right place.
This requires a data integration strategy. In this guide, we’ll take a closer look at how it can be put into practice.
What Is a Data Integration Strategy?
A robust data integration strategy provides a number of benefits to your organization:
1. It simplifies the analysis, management, and storage of raw data.
2. Instead of wasting resources, data becomes an asset.
3. Data is exchanged and transferred securely and in full compliance with regulatory requirements.
4. Integration platforms simplify future processes by creating robust data ecosystems.
5. Once cleaned, processed and simplified, data is ready for use at its destination.
Common Mistakes to Avoid When Integrating Data
The most common problems in data integration, such as those listed below, can usually be avoided without significantly delaying the project.
1. Incorrect data structure or format. Data converters can help avoid this situation by organizing and cleaning the data to make it usable.
2. Data integration methods result in regulatory violations. Your compliance officer can verify that each solution meets your specific requirements. You may need to reconfigure or use a different integration tool.
3. Solutions cannot coexist. Incompatibility issues are solved with robust third-party integration tools and platforms to create a secure data integration ecosystem.
4. Data integration is cumbersome and slow. Consider updating integration tools or revisiting the data source and eliminating redundant values.
Developing a Successful Data Integration Plan Step by Step
Familiarize yourself with the nine key actions to take when developing a plan.
Step 1: Define Project Goals
For a data integration plan to be successful, you need to understand your organization’s overall business goals. Identify the key challenges, opportunities, and priorities that data integration can help solve. Here are some of the most common business goals that data integration can solve:
1. Collecting and analyzing business data
2. Providing information as a
3. service Compliance with standards and regulations
4. Data cleansing
For data integration to deliver measurable results and align with overall business objectives, you need to identify specific business objectives.
Step 2: Examine Data Sources
Data integration often links source decisions and goals. Analyzing these data source decisions is critical.
When selecting a source, consider where the data is stored – is it distributed across multiple locations or stored in one place? In addition to security or compliance issues related to the current data architecture, format is also an essential factor.
Step 3: Define Expected Data and Constraints
Here, we return to step 1: objectives. You can exclude specific datasets from consolidation because not all source data is suitable for these goals. This will make the integration more efficient.
On the other hand, make sure you have all the information you need. Does your goal require the integration of data from another source? Are there different challenges or obstacles?
Step 4: Consider Compliance and Security Issues
Organizations that use personal data are subject to specific laws and regulations. These legal frameworks may vary by industry and location.
Make sure your data transfer process complies with all applicable laws. This also applies to end-of-solution storage. At the end of the retention period, some data may need to be deleted, which should be done in a timely manner.
Step 5: Assigning Roles to The Integration Team
A team is needed to implement and manage the data integration process. Consider who should fulfil the following roles in the project:
1. Project Manager and Project Administrator
2. Compliance auditors
3. Assessor of regulatory compliance
4. Data analysis and usability testing
Step 6: Select Data Integration Tools
The gap between the data source and the target solution is created by using data integration tools and reports. There are many tools available to you and your organization. When choosing a tool for your project, consider the following points:
1. Data Volume
2. number of data elements
3. Number of data formats; frequency of data updates
4. Compatibility between the data source and the target solution
5. Project budget
Step 7: Select The Data Structure and Format
Create a well-organized plan for data transfer between business systems. This requires defining data models, schemas and integration rules. Implement data cleansing and normalization processes to ensure data consistency and quality between interconnected systems.
The quality and reliability of business data can be improved by eliminating data errors through data cleansing and defining guidelines to standardize formats and values. The goal is to create a solid foundation that will ensure seamless integration of data to achieve business goals.
Step 8: Define Parameters for Implementation
Before integration, make sure parameters are set.
1. Ensure that the target and source databases are configured correctly.
2. Ensure that the archiving mechanism of the target database is compliant with current legislation.
3. Ensure that the data is in the correct format. Otherwise, the integration tool will change it.
4. Ensure that all integration tools are working and configured correctly.
5. Set dates for testing and final implementation.
Step 9: Evaluate and Monitor the Integration
The effectiveness of data integration processes should be carefully evaluated. Establish standards and measures to assess the reliability, accuracy, and quality of the merged data.
Conduct data validation and functional and performance testing to confirm that the integrated data meets the specified standards and objectives established in the initial stages of strategy development. To represent the real-world conditions and configurations in which the integrated data systems will operate, these tests should simulate real-world situations.
Wrap Up
At this stage, it is critical to take the time to define success goals and assemble the integration team. Data integration has become much less stressful and complex, but you still need to determine what success looks like when it is achieved and assign the right people to measure it.
