iPaaS, Data Lakehouse, and AI Agents: How to Eliminate Data Silos

Data silos , as well as information scattered across systems that don't communicate with each other, are often resolved by combining three layers into a single platform: iPaaS (to connect systems without custom development), a data lakehouse (to organize and centralize the data coming from these connections), and AI agents (to transform this organized data into automations and responses). This combination, present in platforms like Skyone Studio, eliminates silos without requiring the company to replace its ERP, CRM, or any system already in use; the integration happens on top of what already exists.

What is a data silo, in practice?

A data silo occurs when information from one system (e.g., ERP) does not automatically communicate with information from another system (such as CRM or an e-commerce tool). The result is manual rework, decisions made with incomplete information, and difficulty in having a unified view of the business.

The three layers that solve the problem

1. iPaaS: Connecting systems without custom development

The iPaaS layer connects different platforms through pre-built connectors, eliminating the need for manual development for each integration. This includes workflow automation, data transformation and standardization, and support for both cloud systems and on-premise environments (hybrid integration).

2. Data Lakehouse: organizing what was previously scattered

Once connected, the data needs a place to be organized, enriched, and segmented. The lakehouse layer handles data collection, organization, enrichment, and segmentation, with real-time processing, governance, and compliance with privacy regulations, as well as enabling easy export to other platforms when needed.

3. AI Agents: Transforming Organized Data into Action

With connected and organized data, AI agents can interpret intentions, make context-based decisions, and execute actions, from answering a question in natural language to automating an entire operational process, always with the possibility of escalating to a human when the agent cannot resolve a case alone.

Read also: What is the relationship between data and artificial intelligence?

How do these layers work together?

How many systems can be connected in this way?

Platforms like Skyone Studio have already tested and offer integration with over 400 different systems, from ERPs and CRMs (such as TOTVS and Salesforce) to e-commerce tools, spreadsheets, and email marketing platforms, without the company needing to change any of these systems to integrate them.

Frequently Asked Questions

Does eliminating data silos require replacing the company's current ERP or CRM? No. Integration via iPaaS connects existing systems through ready-made connectors, without requiring the replacement of any tools the company already uses.

What is the difference between a data lake and a data lakehouse? A data lake stores raw data, often unstructured. A data lakehouse combines this storage with organization, governance, and optimized query capabilities, similar to a traditional data warehouse, bringing the two models together in a single layer.

Do AI agents replace the team that currently handles data integration? They don't replace them, but they significantly reduce repetitive manual work: simple and recurring cases are resolved by the agents, while exceptions and more complex decisions continue to be escalated to the human team.

Is it safe to concentrate data from multiple systems on a single platform? Yes, provided the platform has native data governance, access control, compliance with privacy regulations, and usage monitoring, which is precisely the role of the lakehouse layer in this type of architecture.

Written by Skyone