Data-Centric Feature Discovery

Build a scalable feature discovery and engineering process with reusable feature assets

Discover features with dotData

A Paradigm Shift for Enterprise Data

Benefit from a fundamental shift in how enterprise organizations develop curated data and accumulate domain and data “know-hows” as reusable assets.

“Its exceptional data management and feature engineering capabilities make it especially suitable for the most challenging use cases…feature engineering is powerful and scalable, even across tens of tables with billions of rows.”

Key Features

dotData Feature Discovery

Start Feature Discovery from Day One

Getting Started is Hard

Feature discovery requires deep data and domain knowledge. Involving different experts and stakeholders and the complexity and size of enterprise data add to this, making getting started difficult.

Jump-Start Your Process

Feature Factory automatically suggests feature spaces by analyzing your enterprise data. Analyze relational, transactional, temporal, and geolocation data to kick-start feature discovery and engineering and identify signals from day one.

Understand feature quality with dotData

Data-Centric and Programmatic Approach To Explore More Ideas

Manual Process Limits Your Ideas

Feature Engineering has – traditionally – been a highly manual, artisanal process. Your team’s ideas are constrained by a lack of time and resources, constraining the discovery of new and interesting paths.

Programmatic, Data-Centric Feature Engineering

Feature Factory lets you define feature spaces and auto-generates 100X broader feature hypotheses using a data-driven approach that expands your reach and your team’s ability to experiment adding to your existing data and feature knowledge

Gain Feature Transparency

Transform Your “Know-How” into Reusable Assets

Feature Engineering is too Disposable

Feature engineering goes beyond simple SQL queries. Complex data operations and transformations, ETL, data cleansing, and feature transformations take time and require multiple iterations. However, the ad-hoc nature of this process means that when features are identified for specific use cases, the transformation steps taken to get there are usually lost in a sea of unused Jupyter notebooks.

Reusable Assets for Feature Engineering

dotData Feature Factory introduces the concept of reusable feature engineering assets. Stop reinventing the wheel by leveraging a repository of all recorded steps associated with discovered features, allowing your data science team to expand on already available feature discovery assets to accelerate their workflow.

Generate Product Quality ML Code

From Jumbled Notebooks to Production-Ready Feature Pipelines

Hard to Take Features from Lab to Production 

Feature discovery is typically performed inside each data scientist’s Jupyter Notebook. Notebooks quickly become an overwhelming jumble of code and are poorly managed or organized without standardization. Transforming this mess into production code can be challenging at best.

From Experiments to Production-Ready Code, Quickly

Feature Factory makes it simple for data science teams to build transparent, readable, and maintainable feature pipelines that are scaleable and cover edge cases when processing new data. Accelerate the process of moving from experiments to production with dotData Feature Factory.

SMBC Corp - dotData Feature Factory Client

How SMBC Accelerated Their Feature Development Process 48X

When SMBC – one of the World’s largest banks – wanted to accelerate their AI/ML development process, they turned to dotData’s Feature Factory platform. Download the case study to see how they accelerated development times by 4,800%.

Key Features of dotData Feature Factory