Managed data
What is Data as a Service (DaaS) and why it matters
A clear definition of Data as a Service (DaaS): what it includes, how it differs from building your own infrastructure and why more companies adopt it.
Read articleKey takeaways
- Integrating data combines information from different sources into a coherent view.
- There are several methods: ETL/ELT, APIs, virtualisation and CDC.
- The key is to start from the use case, not from moving everything.
- A governed intermediate layer makes integration maintainable.
- The classic failure is integrating everything at once.
A company’s data lives spread across dozens of systems that do not talk to each other. Data integration is the discipline that joins them so they can be exploited together — one of the biggest challenges and costs of any data strategy.
What it is
Data integration is the set of techniques and processes to combine data from different sources into a unified, coherent view, ready for analytics, reporting or AI.
The main methods
- ETL / ELT: extract, transform and load (in different order).
- APIs: programmatic exchange in real time.
- Virtualisation: query without moving data.
- CDC: capture and replicate only changes.
Best practices
- Start from the use case: integrate only what answers a concrete question.
- Use an intermediate layer: do not connect systems directly.
- Govern from the start: quality, access and traceability.
- Automate and monitor: pipelines break; they must be watched.
Sources
ERP, CRMfiles, APIs
Intermediate layer
NormaliseGovern
Coherent view
AnalyticsReporting, AI
Why integrations fail
The classic mistake is to try to integrate "everything with everything" at once, without an intermediate layer or governance. The result is an endless, fragile project. The approach that works is incremental: a governed data layer that grows case by case.
Do not integrate everything with everything. Grow a governed layer case by case.
In summary
Data integration combines scattered sources into a coherent view using methods like ETL/ELT, APIs, virtualisation and CDC. The key is to start from the use case and connect through a governed intermediate layer, growing incrementally — not integrating everything at once, which is how projects fail.
Sources & further reading
Frequently asked questions
Which integration method is best?
It depends: ETL/ELT for analytical loads, APIs for real time, virtualisation to avoid moving data and CDC for efficient sync. Often combined.
Why do so many integrations fail?
By trying to integrate everything at once without an intermediate layer or governance. The incremental, use-case approach is far more reliable.
Do I have to change my systems?
No. Good integration adapts to your current systems via an intermediate layer that connects and governs them.
What is the intermediate layer for?
To normalise and govern data and decouple systems, so a change in one source does not break the whole integration.
Where should I start?
From a concrete business question, integrating only the data that answers it, then growing case by case.
Why automate and monitor?
Because pipelines break and sources change. Automation and monitoring keep the integration reliable over time.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.