'DECODE' YOUR DATA WAREHOUSES, MEET KPIs FASTER
Data teams build data warehouses so that new data requirements can be met immediately.
Except this doesn't happen in the real world.
People change jobs, documentation is poor, and it's not easy to track what exists in a database. Data teams often feel It's faster to build again from scratch.
Ellie's data models add a semantic layer that translates what exists so that data engineers can reuse existing assets.
01
Low Effort, Minimum Investment
One Layer to Understand Your Data Warehouse Capabilities
It's not uncommon for large businesses to have multiple data warehouses that try to unify hundreds of complex processes.
Unfortunately, this means data teams are not sure what's already engineered and what needs to be done.
What if you could import your database structure — say from Snowflake or DataBricks — and get a model that represents your database(s)?
Ellie is designed to support you in making sense of millions in data engineering investments.
02
Avoid Re-Engineering Data Products
Collaborate with Key Decision Makers, Manage Expectations
Data warehouses are most profitable when it reflects business expertise, instead of source system structures.
Once you map your existing databases, you can bring in business experts to understand their needs and how close you're to providing it. It's the best way to manage expectations.
Ellie makes cooperation between domain experts and data teams fast and simple, in a way that makes it easy to make use of data warehouses.
03
Built Once, Reuse Any Time
Connect Data Models, Simplify Complex Business Process
Conceptual data models are universally applicable within a business, and that's what makes it a great approach.
Modeling at the semantic level — where the domain expert can support you — lets you define data structures that are usable regardless of the project.
The terms (entities) and relationships are easy to reuse and track across models.
So the data model for your invoicing system can be reused when renewing your ERP software. Or a data model that tracks you logistics can be used to reduce greenhouse gas emissions across your supply chain.
04
Deliver Better Projects, Faster
Speed Up Data Warehouse Implementation with Simplicity
Projects are completed faster when you have one semantic layer that both data teams and business stakeholders can understand.
Business domain experts can lay out their processes logically & visually, while data teams can immediately understand if their existing data warehouse can meet these needs.
Large enterprise teams often re-do things because data teams are not aware of what's already been engineered and what data is useful at any point in time.
This saves millions in engineering costs.
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