Infor Data Lake

The data platform for analytics, integration, and governance in Infor OS

The implementation of Infor LN and Infor CloudSuite Industrial Enterprise is not just about accurately mapping processes in the ERP system. Equally crucial is how reporting, integrations, and data governance will be structured in the future. This is precisely where the Infor Data Lake plays a key role: as a persistent data layer that decouples analytics from the operational system, stabilizes data flows, and creates a controlled, auditable access layer.

What is the Infor Data Lake?

The Infor Data Lake is the central storage and consumption layer within Infor OS/Data Fabric for data from Infor applications and third-party systems. The key point for projects is that data is stored in a way that allows it to be flexibly used later for various purposes—analytics, process monitoring, integrations, and audit trails.

Why Infor Data Lake Acts as a Multiplier in Cloud Migrations

In LN settings, we often see:

Heavily loaded operational databases,
nighttime reporting jobs “at their limit,”
expanded exports, and shadow ETLs.

With data lake-based consumption, reporting is gradually decoupled. This has two effects:

Stability (less load/dependencies on LN)
Release robustness (analytics and exports are less vulnerable to changes in the ERP system).

In discrete manufacturing in particular, LN is often deployed globally. The data lake helps to consolidate data by domain—rather than by plant or table logic—and thus:

Make KPIs comparable,
Ensure the quality of master data is transparent,
Establish comprehensive end-to-end transparency.

Many LN migrations also serve as an “integration overhaul.” The data lake is not a substitute for integration logic, but it does provide a strong foundation for stability:

Events and data flows are standardized
Consumers (BI, data science, external systems) access defined data products in a controlled manner,
and governance and permissions can be managed more centrally.

Infor Data Lake in the Infor OS Stack: Architecture Overview

Data Ingestion: How Data Enters the Infor Data Lake

There are several ways to get data into the data lake:

ION Data Flows/Connection Points as a central integration and data flow mechanism (including for loading data from source systems)
Ingestion API (programmatic loading of data objects via REST)
Queue/upload mechanisms

Practical tip for LN migrations:

Plan your data ingestion using a domain-oriented approach (Order-to-Cash, Plan-to-Produce, Procure-to-Pay, Project/Service) rather than a table-oriented one. This reduces the need for rework later on in analytics, data quality, and KPI definitions.

Metadata & Data Catalog: No Catalog, No Scalability

In reality, data lake initiatives often fail due to a lack of direction:

What is the object?
Who owns it?
Which version is valid?
Which fields are sensitive?

That is why we treat the Data Catalog as an essential component of the design—with clear standards for:

Naming & Domains
Ownership (Data Owner/Steward)
Sensitivity Classification
Approval Processes

Access & Usage: How Teams Use Data Securely and Efficiently

Consumption is the point at which IT strategy becomes “tangible”

BI/Analytics access curated datasets,
the architecture establishes standards for queries, views, and data products
External consumers use controlled APIs instead of unverified exports

Governance level: Security, retention, auditing

Governance isn't just a chapter at the end of the document. In cloud programs, it's an operating model:

Roles & Responsibilities
Access Policies
Retention/Deletion
Traceability

Especially when it comes to personal data (GDPR) or IP-sensitive engineering data, the question isn’t “Can the tool do that?”, but rather: Who is authorized to do what, and how is this monitored on a day-to-day basis?