What is data integration?
Data integration is about making sure organizations can easily access and use their data. It involves using architectural patterns, methods, and tools to bring together and move data from various sources and types. This ensures that the data meets the needs of the people and applications that use it.
In its most basic form, data integration is how we make data available when it’s needed, where it’s needed, how it’s needed and in the right format.
Multiple integration approaches and patterns exist for different use cases, for example, ETL and Data Virtualization.
How does it work?
The integration component acts as a bridge between your source and target. It’s the method we use to guide the data on its journey. As the data travels, we ensure any inconsistencies are rectified, formats are adjusted, and if there are various sources, we merge these data sets. By the end, the data is directed downstream, arriving at the intended targets.
There are numerous methods to integrate data, but in its simplest form, it’s about shifting data within an organization, ensuring it travels from its origin to its desired location, wherever that might be.
Data integration approaches
Some approaches are old and some are new, but they all fit for a specific purpose. Which data integration approach you use will heavily depend on what scenario you’re in. Don’t get swept up in the hype of new approaches because the old ones absolutely are still valid and have a place.
Extract, Transform and Load (ETL): Data sets from different source systems are extracted, aligned and are loaded into a database or data warehouse.
Extract, Load and Transform (ELT): Data sets are loaded into a data lake or repository and transformed later for specific analytics cases.
Change Data Capture (CDC): CDC identifies changes to data at source and applies them to a data warehouse, where the data is replicated.
Data replication: Data sets are replicated to another database in order to keep as a back up or synchronized for operational uses.
Data Virtualization: Data from disparate systems is combined virtually (and transformed if required) to create unified views for end users.
Streaming Data Integration: Real time data from different streams are integrated on the fly and fed into data stores or systems on a continuous basis.
Why do you need data integration?
Your “why” will be dependent on your use case of what it is you’re trying to achieve or what your business needs to achieve. In its basic form, it’s about making your data available where it’s needed and when it’s needed and how it’s needed.
Think about the last time you tried to solve a jigsaw puzzle. You have all these pieces spread out on your table, each showing just a tiny fragment of the image. On their own, these pieces don’t reveal much. But when you start fitting them together, a clear picture emerges.
Now, let’s say these puzzle pieces are like pieces of information or data from different places:
Corner Pieces: These are like your foundational data, the basics that give structure to everything else, like customer names or product IDs.
Edge Pieces: Imagine these as the data that defines boundaries or limits, like the maximum budget for a project or the start and end dates of a sale.
Middle Pieces: These fill in all the details, like sales figures, feedback scores, or website visits.
When you’re in business (or any project, really), you’re often working with data that’s scattered across different sources, much like those puzzle pieces. If you don’t bring them together, you’re missing out on seeing the full picture.
No Missing Pieces: Just as a puzzle is incomplete if a piece is missing, if some data isn’t integrated, you might miss out on crucial insights.
The Bigger Picture: As with a puzzle, when data is brought together, you can see patterns, get insights, and make informed decisions.
Satisfaction of Completion: Just as it’s fulfilling to see a completed puzzle, integrating data gives you a comprehensive view that’s satisfying and useful.
Avoiding Overlaps: Just like making sure two puzzle pieces that don’t fit aren’t forced together, integrated data ensures there’s consistency and no duplications.
In simpler terms, data integration is like piecing together a jigsaw puzzle. Each piece, or data point, has its place. And when you bring them all together, you get a full, clear, and meaningful picture to guide your decisions. It’s all about connecting the dots (or pieces) to see the whole story!
How to know if you need data integration?
Ultimately integration is the thing that underpins everything else because if you can’t move your data around your ecosystem or around your organization, then you can’t achieve any of the other things that you need to be able to do.
If you’re still not sure, these are the signs you need data integration:
- Multiple or Scattered Data Sources: If you’re fetching data from various places or finding discrepancies among them, data integration might be the answer.
- Efficiency Concerns: Whether it’s time-consuming reports, challenges in team collaboration, or slow operational tasks involving data, integration can streamline these processes.
- Data Quality & Consistency: Concerns about data accuracy, conflicts, or the desire for real-time insights indicate a need for integration.
- Complex IT Landscape: If your IT systems, apps, or platforms aren’t communicating well, data integration can bridge the gap.
- Planning & Growth: Anticipating future data complexity or already noticing a surge in data volume suggests that integration would be beneficial.
Essentially, any business that has data needs some form of integration.
It’s not uncommon for it to be forgotten about, especially if you’re focusing on your data governance or strategy. Ultimately, you’re going to need some sort of integration, whether it’s from one system to another for a specific purpose like MDM, or if you need access to data for a specific purpose and you need to be able to move it through your organization.
Data integration underpins everything. Whether you chose ETL or a newer integration approach, in the end, data integration is like a hidden gem in a charity shop. It’s been around for ages, it’s not new and shiny, but it’s reliable and exactly what you’re looking for.
What does successful data integration look like?
A successful data integration strategy will:
- align with your business goals,
- save time and resources when building integrations,
- identify, prioritize, and assess the feasibility of use cases,
- minimize business disruptions,
- lower risk by centralizing data governance and data management,
- enable a faster response to threats, and
- enable better agility in times of change.
In essence, successful data integration is not just about the immediate transfer of data; it’s a comprehensive system designed with a focus on past learnings, present requirements, and future aspirations, ensuring every step aligns with the broader business objectives.
So, how can Amplifi help your organization?
Whether you’re adopting a modern data management approach like Data Mesh or Data Fabric – or simply trying to improve the availability of data to those that need it – data integration is an essential ingredient. Whilst there are an abundance of data integration techniques and approaches, it’s clear there is no one-size-fits-all approach.
Amplifi can help organizations choose the right approach – or blend of approaches – to suit their business objectives and empower them to make more data-led decisions in a unified, cohesive manner.
Need help defining your Data Integration strategy? Want to know more about our Data Integration Services? Read our guide 3 steps to better Data Integration or get in touch with one of our experts.