Today’s businesses depend on data, but more than that, they need to get the right data to the right solutions in the right configuration and format. It’s not simply a question of transferring information where it needs to be. It’s about creating a secure system in which these information values can be shared, collaborated on, and wielded with great effect.
For this, you need a data integration strategy. We’ll be looking at how to do this in our guide.
What Is a Data Integration Strategy?
Your data integration strategy comprises the plan, framework, and guidelines that will guide the integration. Beginning with your objectives for the integration and finishing up with testing and monitoring, this must be a comprehensive process that meets the needs of all stakeholders. This is why it pays to spend time perfecting and honing your strategy ahead of the integration itself.
The Benefits of a Data Integration Strategy
A strong data integration strategy connects your business with a number of different benefits:
- Source data becomes easier to store, manage, and analyze.
- Data becomes an asset rather than a drain on resources.
- A documented integration strategy helps teams apply consistent security, access and retention controls when transferring and sharing data.
- Integration platforms create reliable data ecosystems, streamlining future processes.
- Data is cleaned, transformed, and streamlined, ready for consumption at the destination.
9 Steps for Creating an Effective Data Integration Strategy
Explore the nine key steps you need to take as you build your strategy.
Step 1: Decide on Project Objectives
The first step is to decide on your aims and objectives for the project. There can be many different reasons for a data integration strategy. These may include:
- Gaining insight from analyses and business intelligence
- Providing data as a service
- Complying with regulatory guidelines
- De-siloing data
Understanding this from the outset will help you define what project success looks like for you.
Make success measurable. For example, require yesterday’s sales in the dashboard by 8 a.m., totals that match the source system, and operating costs within an agreed monthly budget.
Step 2: Review Data Sources
Data integrations typically connect source solutions with destination solutions. So, it’s important to review those data source solutions.
Consider where the information is stored at the source. Is it stored in a single location or across multiple sites? The format is important too, as well as any security or compliance concerns associated with the current data infrastructure.
Record each source’s owner, available API or database access, access permissions and rate limits. Check how it records updates and deletions, not just new records.
Step 3: Set Data Limits and Expectations
Refer back to Step 1 here: your objectives. It’s likely that not all of the data at the source will be relevant to these objectives, so you may want to exclude some datasets from the integration. This in turn can improve integration performance.
Conversely, you need to make sure that you do have all the data you need. Do you need to integrate data from another source to effectively complete your objective? Does this pose any further challenges or obstacles?
Specify how much historical data to load and how source updates and deletions should appear at the destination. These decisions affect both accuracy and the initial workload.
Step 4: Consider Security and Compliance
Any businesses that use personal data will be bound by certain rules and regulations. The exact nature of these regulatory frameworks will vary, depending on the industry you operate in and on the geographical location of your operations.
Make sure the data transfer process is fully compliant with all relevant regulations. This also applies to the storage structures at the destination solution. Some data may need to be deleted after a retention period expires, so this must be carried out on schedule.
Include access permissions, credential storage and rotation, encryption in transit and at rest, and retention controls in your checklist. Document how required deletions reach downstream copies.
Step 5: Assign Integration Team Roles
You’ll need a team to help execute and oversee the data integration process. Consider who will fulfill the following roles on your project:
- Project leader and director
- Technical and IT professional
- Regulatory compliance assessor
- Data analyst
- Usability tester
Assign an ongoing pipeline owner for failures and maintenance, plus a business owner who confirms that the resulting data is accurate and useful.
Step 6: Choose Data Integration Tools
Data integration tools and connectors bridge the gap between the data source and the data destination solution. There are a number of different tools available to you and your business. To select the right one for your project, keep the following in mind:
- The volume of data
- The format of the data
- Data refresh frequency
- The compatibility of the data source and destination solutions
- Your project’s budget
Choose the integration approach before comparing tools:
- ETL or ELT: extract, transform, load (ETL) transforms data before loading it. Extract, load, transform (ELT) loads it first and transforms it at the destination.
- Batch or streaming: process scheduled groups of records or a continuous flow of events, according to how quickly updates are needed.
- Change data capture (CDC): capture source changes rather than repeatedly copying the full dataset.
- Federation or virtualization: query data across sources without first consolidating all the underlying records in one store.
These choices can work together: CDC can feed an ELT pipeline. Compare storage, processing, data transfer and maintenance costs alongside subscription fees.
Step 7: Decide on Data Formatting and Structure
Again, you’ll need to refer back to the early steps in the process here. What do you want to achieve from the data you are integrating?
Make sure the data arrives at the destination in the correct format for this purpose. If you store a destination copy, it must have an accessible and searchable yet secure structure.
Agree on matching identifiers, date formats, time zones and currencies. Document how renamed columns or changing data types will be handled before they break downstream reports.
Step 8: Set Implementation Parameters
Before the integration is executed, you’ll need to make sure the parameters are in place.
- Ensure that the source and destination are properly defined.
- Check the destination storage method is compliant with regulations.
- Assess whether the data is in the proper format or if it will be transformed by the integration tool.
- Make sure all the integration tools are operational and properly configured.
- Set deadlines for testing and eventual execution.
Start with a pilot using one source. Test the historical load, ongoing updates and recovery from a failed run. Confirm that retries do not create duplicate records.
Step 9: Test and Monitor Integration
Finally, you will need to test and monitor the integration so everything takes place as it should. Your usability testers, technical and IT professionals, regulatory compliance assessors, and data analysts should examine the results of the test and decide whether or not the integration is successful.
Team members then report back to the project leader or director, who can officially sign off on the project.
Monitoring should continue after the initial sign-off. Track whether data arrives on time, whether important fields are missing, and whether duplicate records or unexpected schema changes appear. Assign someone to investigate alerts and document how failed runs will be retried without duplicating data. Review operating costs against the budget.
Popular Tools Used to Bolster a Data Integration Strategy
The right toolset is vital for effective integration. This will include:
- Data integrator tools and connectors: Data integrator tools and connectors bring together non-compatible solutions, facilitating data integration between the two.
- Data integration platforms: A data integration platform performs integration connection on a broader scale. Multiple solutions are brought together on the platform for enhanced data insight.
- Analytics and visualization tools: Analytics and visualization tools turn integrated data into reports and dashboards. Depending on the tool and connection mode, they may import data or query it directly at the source.
- Data transformers: Data transformers manage the format of the data between the source and the destination, ensuring consistency, accuracy, and usability.
Common Data Integration Pitfalls to Avoid
Plan for these common data integration pitfalls before they delay your project.
- Data is in the wrong format or structure. Data transformers can help prevent this pitfall, cleaning and organizing the data so it is ready for use.
- Data integration methods do not comply with regulations. Your team member responsible for compliance can check that all solutions meet your specific requirements. You may need to reconfigure or find another integration tool.
- Solutions are incompatible. Check that your chosen connector supports the specific source, destination and data types you need. Some combinations require custom work.
- Data integration is slow and cumbersome. Consider reviewing the data source and removing unnecessary values or updating your integration tools.
Data Integration Strategy: FAQ
What are the different types of data integration methods?
Two common approaches to data integration are ETL (extract, transform, load) and ELT (extract, load, transform). The primary difference is where the data transformation takes place. With ELT, it takes place at the destination, while ETL transforms the data before it is received. Batch processing, streaming, change data capture and virtualization address other integration choices. See Step 6 for how these approaches fit together.
When do companies usually adopt a data integration strategy?
Companies may choose to adopt a data integration strategy when they are dealing with siloed data, when disconnected solutions are working out of sync, or when they wish to implement digital transformation.
How does data integration improve decision-making?
Data integration ensures that the right solutions have the right data, facilitating effective analysis. This enhances the decision-making process by providing valuable insight.
Summary
Spending time defining your objectives for success and putting your integration team together is going to be key here. You still need to define what success looks like when you achieve it and put the right people in place to measure that success.
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