Many companies approach the topic of automation by asking a simple question:

Can I connect System A to System B?

With Synesty, the answer to this is often, at first: yes. If systems have an API or if data can be provided and processed in a standardised way, they can usually be connected to one another.

However, the more important question actually comes afterwards:

Which data should flow from where to where – and in what format?

It is crucial that it is clear from the outset which data is to be transferred, how this data is structured, what information is actually required, how it needs to be processed, and in what format the target system expects it.

Anyone wishing to automate processes should therefore think not only in terms of systems, but above all in terms of data flows.

Video: Why data clarity is crucial before automation

In our video, Katrin and Rocco discuss why process automation often stalls even before it is actually implemented. The focus is on the question of what data looks like, where it comes from, and how it can be transferred cleanly from one system to another.

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Technically feasible does not automatically mean that data flows cleanly between systems

The fact that two systems can, in principle, be connected does not mean that they communicate with each other in a consistent manner.

In e-commerce in particular, different data structures such as XML, CSV or JSON frequently come into play. Product data, stock levels, orders or shipping information must be transferred consistently between the shop, ERP, PIM, marketplaces and other systems. Field names, formats and content of the source data often differ from the requirements of the target system.

Three questions that should be clarified before any automation

Anyone wishing to automate processes should be able to answer at least these three questions before implementation:

1. Where does the data come from?

It is important to clearly define the sources of the data. Does it come from an ERP system, a shop, a marketplace, a file, a database or via an API?

It is not just the source itself that matters, but also the way in which the data is provided.

2. What should happen to the data?

Should the data simply be transferred, or does it need to be processed, transformed, supplemented or filtered beforehand? Do fields need to be renamed, content enriched or values recalculated?

It is often this step in particular that determines how complex and how stable the automation will be later on.

3. Where should the data go?

The target system is equally important. What data is expected there? In what format? Which fields are mandatory? Which information is optional?

Only once it is clear what the target data must look like can the path to achieving this be planned in a structured manner.

How to clarify your data flows

Before a process is implemented, it is worth carrying out structured preparatory work. This primarily involves the following steps:

• Identify processes
• Record systems and interfaces
• Check data formats
• Compare source and target formats
• Identify manual steps
• Set priorities

Practical tip: Don’t start with the most complex process. It makes most sense to begin with small, manageable and recurring workflows that are clearly structured and relevant to the business.

5 Practical Tips

#1 Think in terms of data flows, not just systems

The question “Can I connect this system to that one?” is a good starting point. However, it is not enough for successful automation.

What matters is how data flows through the process.

#2 Work with real sample data

Theoretical assumptions are of limited help. By working with real test data, you can identify early on where fields are missing, formats differ or content needs to be adjusted.

#3 Model processes as a clear flow

It is helpful to view the process as a sequence of clear steps:

• Retrieve data
• Process data
• Forward data

This makes even more complex automations easier to grasp.

#4 Start small

Not all processes need to be automated at the same time. It often makes more sense to start with a specific use case and learn from it.

#5 Allow for trial and error

No-code and low-code approaches are particularly well-suited to testing ideas quickly. By experimenting in a controlled manner, you can learn more quickly which approaches work in practice.

Conclusion

In many cases, Synesty can be used to connect systems where data is available via APIs or other standardised channels. However, the key to success lies not only in the connection itself, but in a clear understanding of the underlying data flows. Those who know where data comes from, what it looks like, what should happen to it and where it needs to be transferred lay the foundation for stable and scalable automation. This is precisely why successful process automation does not begin with the interface alone, but with an understanding of the data.

What to do next

If you’d like to further structure your data flows and processes, a joint review of your specific use case could be a useful next step. In a no-obligation onboarding call, we can work together to identify which systems are involved, where data flows are still unclear, and what the next steps might be. If you’d like to delve deeper into the analysis, our feasibility analysis workshop can help you systematically evaluate processes, data flows and interfaces.

• Arrange an onboarding call
• Find out more about the data flow analysis workshop