Before I explain the Canonical Data Model, I will give you a short introduction that might help you understand why organizations are in need of this solution. Due to the digital transformation programs that companies embark on we see that integrations are becoming an increasingly important part of IT budgets. According to Gartner, 50% of all IT budgets in 2020 will be spent on integrating systems and processes. The trend of using best of breed solutions through the emergence of low code application development means that it’s becoming easier to solve specific processes within an organization through the development of single applications.
This development results in an increasingly fragmented IT landscape, which ultimately leads to severe integration issues: what information has to be available where and when? Which data is leading? Do the applications speak the same “development language”?
Point to point connections
With point to point connections, applications are directly linked on a one on one basis. Data is exchanged between these applications. Logically, the amount of point to point connections with multiple applications in your landscape will increase exponentially, as you can see in the graph on the left below.
Whereas with 3 systems the number of point to point connections is still three, with 10 systems this numer has already increased to 45 connections. A recipe for spaghetti!
Not every application needs to be integrated with every single other application, but the graph clearly shows the impact of adding a 12th application. The negative impact on time, money and quality by adding or replacing applications in the point to point scenario is nearly disastrous. Besides that, the risk of mistakes and instable network traffic are high.
Structure your complexity; scale expansion of applications with a Canonical Data Model
So how do you scale up your integrations landscape in a proper way? By using a Canonical Data Model. A Canonical Data Model is often referred to as the common data model: one language that connects all applications. Thus enforcing standardization of data definitions. Therefore, this Canonical Data Model wil hold the single integration model for your company – one truth.
Key advantages of a Canonical Data Model
- Easy overview: All integrations are implemeted in a single model (the Canonical Data Model). There is one location where you can see all involved systems and integrations.
- One single language: All applications are translated back to a single model: the Canonical Data Model. Interpretation of data is managed on a central level.
- Scalable: Adding or replacing applications means translation only to the Canonical Data Model, in stead of to all other applications and systems. The Canonical Data Model will manage this automatically on a central level.
- Speed of development: Connecting applications to the Canonical Data Model is a single operation. Connecting aplications point to point are multiple operations, which costs more effort and time.
Key considerations when setting up a Canonical Data Model
There are a few considerations to take into account when designing your Canonical Data Model:
- Initial investment: The setup of a Canonical Data Model will take more time and effort at start-up time. But after three applications, it is becoming easier and the relative time and effort per integration will drop dramatically.
- Implementing and maintaining a Canonical Data Model is not a rush job – doing the job first time right results in direct benefits.
- Considering future evolutions in your integration landscape so you can already take that into account when designing your future proof Canonical Data Model.
- A Canonical Data Model remains custom for every organisation despite the fact that there are many standard connectors for applications and systems.
Simplify replacing Legacy systems!
When replacing legacy systems or adding new systems or applications, you only have to connect these to the central Canonical Data Model. If you continue to focus on the goal to have a scalable integration landscape, you will find out that the Canonical Data Model provides that overview and scalability.
In my next blog, I will eleborate further on the key elements to translate vision to a working integration model. The focus will be on different integration patterns possible which helps in this ongoing endeavour.
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