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.
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.
A strong data integration strategy connects your business with a number of different benefits:
Explore the nine key steps you need to take as you build your strategy.
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:
Understanding this from the outset will help you define what project success looks like for you.
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.
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 will 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?
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.
You'll need a team to help execute and oversee the data integration process. Consider who will fulfill the following roles on your project:
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:
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. It must be stored in an accessible and searchable yet secure structure.
Before the integration is executed, you'll need to make sure the parameters are in place.
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.
The right toolset is vital for effective integration. This will include:
Often, the most common data integration pitfalls, like below, can be overcome without too much delay to your project.
Q: What are the different types of data integration methods?
A: The main types of 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.
Q: When do companies usually adopt a data integration strategy?
A: 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.
Q: How does data integration improve decision-making?
A: Data integration ensures that the right solutions have the right data, facilitating effective analysis. This enhances the decision-making progress by providing valuable insight.
Panoply is an industry-leading cloud data platform designed to make integration straightforward and comprehensive. The platform combines data connectors, a managed data warehouse, and in-platform dashboards to make integrating, storing, and getting insights from your data faster and more seamless than was ever possible before.
Designed with ease of use in mind, Panoply provides the foundation you need to execute effective data integration.
Spending time defining your objectives for success and putting your integration team together is going to be key here. Solutions like Panoply take much of the stress and hassle out of data integration, but you still need to define what success is like when you achieve it and put the right people in place to measure that success.