If you work with digitalization and AI in customer service, you’ll quickly run into two keywords: data lake and data space. Both sound like modern data management — but they describe very different approaches.
This article explains what sits behind each concept, how they differ, and why you’ll soon want both. It’s not just about technical architecture, but also the practical question: How can service teams work faster, safer, and smarter?
What is a Data Lake?
- Principle: “Store everything, analyze anything.” Data isn’t forced into categories up front, so it stays flexible for future analysis.
- Technology: Typically cloud-based (e.g., AWS, Azure, Hadoop). These systems scale and can handle massive volumes.
- Value: Data from many sources can later be used for BI or AI – even if you don’t yet know the exact use case.
In short: a data lake gives you a complete view of your data landscape. Only then can you spot patterns and run large-scale, automated analyses.
What is a Data Space?
A data space works differently. It’s not a lake; think of it as a conference room where specific folders are put on the table. Not everything is freely accessible – only what’s needed for collaboration.
- Principle: Every participant keeps control over their data. It doesn’t need to be centralized and can remain distributed.
- Technology: Federated infrastructures with connectors, catalogs, and governance rules (e.g., Gaia-X, IDSA). These rules create trust and transparency.
- Value: Secure collaboration across organizational boundaries without giving up data sovereignty.
A data space is ideal when multiple parties need to work with the same information without any single party gaining sole control. That’s especially important in sensitive sectors like healthcare or finance.
Data Lake and Data Space: Not Either/Or
Here’s the key: data lakes and data spaces complement each other. They solve different problems and belong in different parts of your data strategy.
- The data lake is perfect for collecting all raw service data and keeping it available for later analysis.
- The data space adds structure and governance: you define exactly which data can be used by which group or for which analysis.
Together they unlock full potential:
- The data lake gives you a total overview.
- The data space brings control, transparency, and trust.
Combine both and your data becomes not just storable but truly usable – in a way that balances security with flexibility.
How LinkThat ONE Brings Both Together
This is where LinkThat ONE comes in. The platform brings both concepts together in one interface, so you don’t have to choose:
- In the Channel Data Lake, data from every contact channel (phone, chat, email, Teams) is collected and made available at any time for reporting or AI. Additional external sources can be connected via APIs, turning the data lake into a comprehensive foundation for your entire customer service.
- With data spaces in ONE, you can bundle documents, reports, or presentations with intent. Users decide which datasets are active and who gets access. That creates order, clarity, and security — without losing information.
Best of all: the integrated ONE chatbot lets you query both worlds via chat — whether you need quick reporting insights from the data lake or a concrete answer from a defined data space. Working with data becomes as simple as a conversation.
In Short: Real Value for Service Teams
A data lake gives you full transparency across all contact channels and helps you keep the overview, even at scale.
A data space creates secure environments for shared knowledge and controlled exchange, so collaboration runs smoothly and remains trustworthy.
Together, they form the basis for using service data efficiently, confidently, and with a future-proof setup. With LinkThat ONE, you get both in a single platform — ready to use in your contact center without extra tools or complicated integrations.
See Data Lakes and Data Spaces in Action
Explore both concepts in our new LinkThat ONE interface:
- All contact channels united in one data lake
- Interactive data spaces for your questions and analyses
You’ll see channel data and dashboards from 14:30, reporting from 21:40, and the data chat from 26:50.
Send us the details of your project.
We’re happy to help with a quick, no-obligation consultation.