Kickstart Your Data Projects with Data Modeling & AI Workflows

You can map source databases (over 160+ connectors) and use genAI capabilities to create data models that reflect business reality. This results in reduced cost and accelerates data product work.

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Reverse Engineering Legacy Systems

Working with Intangible Product Structures

An insurer or a telecom operator has to deal with intangible concepts. Like terms, renewals, coverage, bundles, etc.

While central to your operations, their meaning is not intuitively understood by all. This “intangibility” often results in:

Misaligned Definitions: Teams define & use terms inconsistently.

Disconnected Views: One unit might see a feature as a product, while another treats it as a configuration.

Complex Relationships: Customers often interact with the same product type across multiple channels, in different ways.

Eliminating Gap Between Source Data & Data Projects

There is a gap between how data is structured in legacy systems like SAP or Oracle and how it should be organized in your data warehouse/lakehouse (Snowflake, Databricks, Fabric) for analytics.

Ellie can help bridge this gap. With connectors to over 160 source systems, including SAP and Oracle databases, you can reverse engineer your source databases into Ellie.

Ellie also adds synthetic metadata to explain semantically what each table and column represents.

You can then search for and identify the tables and columns you need to design your dashboard or data product.

AI-Augmented Semantic Layer

Relying on International Standards for Scalability

One solution to these problems is the use of industry frameworks.

For instance TM Forum (telecom) or the International Organization for Standards (e.g. ISO20022 for financial institutions) has published standard business definitions and the connections between these entities to create a business or semantic layer.

However, these often break down when applied to real-world operational data. An enterprise data platform remains as complicated as without a framework if you cannot adapt these standards to your unique needs.

Accelerate Collaboration with Business Experts Using AI

Data projects often struggle because most IT teams cannot get business stakeholders to participate.

Ellie.ai can help. Our AI-assisted modeling workflows make it easier for business stakeholders to create conceptual or semantic data models with the help of a chatbot.

Or data modelers can rely on our AI agent to be familiar with business processes, accelerating the first version of the data model.

Plus, Ellie is built for collaboration. It's incredibly easy to work together once you have the first version of a data model. This means, you need less time from a business expert to gather their input.

Connecting Real World Tables to Data Models

Relying on International Standards for Scalability

Connecting the Dots from Source Data to Business

The average enterprise has dozens of domains that work on the same data from a slightly different perspective.

Same source data, but different use cases. This also means different data models. But why build a new model for every use case?
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Building out your enterprise domain architecture within Ellie enables you to capture a variety of business contexts and reuse data models for different use cases.

That Ellie is connected to your source systems or data warehouse also means you can connect tables and columns to your data models, making reusability that much faster.

AI-Assisted Data Modeling to Speed Up Data Projects

Source Data to Enterprise Data Products Twice as Fast

One

Connect Data Sources & Make Sense of it with AI Assistance

- Reverse engineer source/legacy data warehouse

- Generate synthetic metadata to understand your data and database structures

- Leverage AI agents to support data discovery

- Create reusable entities and models (conceptual, logical, physical)

- Use our customizable metadata to track data sources (data lineage)

Two

Organize Your Enterprise Data Model, Domain Connections

- Multi-level folder structures to isolate and contain what's imported

- Independent glossaries without conflict, which can be shared and updated

- Project-level "hub" with a standard glossary, with subfolders that have its own glossary when necessary

Three

Collaborate on Data Models with Domain Experts

- Collaborate with business to define how data should be structured ('what should be')

- Build your semantic layer one domain at a time, gathering additional context every time

- Leverage AI-assisted data modeling workflows, including chatbots, to accelerate data projects

Four

Design Data Models to Build Your Enterprise Data Product

- Modernize for true transformation, because you have a firm grasp on 'what is' and 'what should be'

- Implement single use cases at a time without fear of creating conflicts, instead of waiting months or years to see results

- Create a foundation for incremental updates with model versioning

- Create reusable physical models/entities that represent reality