Benefit from a fundamental shift in how enterprise organizations develop curated data and accumulate domain and data “know-hows” as reusable assets.
Data-Centric Feature Discovery
Build a scalable feature discovery and engineering process
with reusable feature assets.
A Paradigm Shift for Enterprise Data
Key Features of dotData Feature Factory
dotData Feature Factory automates feature engineering, offering tools for temporal, categorical, geo, and text feature discovery. With built-in cleansing and seamless integration, it simplifies creating ML-ready features.
Temporal & Time-Series Features
Analyze temporal relationships, extract recency, seasonality, fluctuation, etc. by optimizing time resolutions (hours, days, weeks, etc.)
Categorical Feature Discovery
Regularized target encoding beyond common one-hot encoding, extract multi-category patterns, numeric featurization like histogram encoding, and more!
Discover & Develop
Geo Features
Analyze numeric and/or categorical attributes based on geo-locational geo-mapping, target distribution as grid-target encoding beyond common longitude and latitude features.
Analyze Text Data & Discover Text Features
Extract high-order topic features that eliminate redundancy with a diagonalization technique. You can apply your domain dictionary to handle domain-specific terminologies.
Supervised Feature Search & Optimization
Apply patented supervised-learning-based feature search, selection, and optimization techniques to discover the most relevant features to your target variables.
Prevent Overfit, Collinearity, Drift, and Leakage
Produce a high-quality feature set by applying multiple techniques to prevent feature over-fitting, collinearity, draft, and leakage.
Fine-Tune Features for
ML Algorithms
You can optionally specify your preferred ML algorithm such as linear regression, gradient boosting, neural network and fine-tune features that are suitable for the selected ML algorithm.
Built-in Data and Feature Cleansing
Apply categorical / string value canonicalization, duplicate record removal, missing value imputation, data outlier elimination, target outlier elimination, etc. as a part of feature generation pipelines.
Explore Millions of Feature Hypotheses
Work on the distributed computation and handle tens of tables, thousands of columns, and billions of rows to explore millions of feature hypotheses.
Integration with Your
Python Workflow
dotData lives as a library in your Python environment to create ML-ready feature tables and is seamlessly integrated with your existing ML workflow.
Feature Store Integration
Produce features, feature metadata, and feature queries that can be registered into your feature store and help you continuously evolve your feature store.
Integrated with Your Preferred Cloud Platform
Work seamlessly on major cloud data platforms such as Databricks, Azure Synapse, Amazon Redshift, EMR, or Snowflake.
Accelerate Feature Discovery and Engineering from Day One
dotData Feature Factory accelerates feature discovery from day one, automating complex processes and enabling teams to scale from experiments to production-ready solutions. By transforming manual workflows into a data-centric, reusable system, dotData empowers data science teams to explore more ideas, streamline feature engineering, and rapidly turn insights into action.
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.
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.
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.
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.
What Our Customers Say
Exeter Finance
The biggest problem is that, when doing it manually, it’s just a repetitive, trial-and-error process that takes time. dotData solves a problem I’ve been trying to solve for 20 years.
sticky.io
I was spending 95% of my time wrangling data…now I can offload most of that work and just focus on delivering viable patterns and insights.
Real-World Use Cases
Discover how dotData is transforming businesses across industries. From demand forecasting to predictive maintenance, our use cases showcase real-world success stories where companies have leveraged automation to drive efficiency and deliver measurable results.
News
Press Release
dotData Announces dotData Ops 1.4 with Advanced Python Ecosystem Integration
Press Release
dotData Announces Updates to Products Enhanced with Generative AI Integration
Press Release
dotData Announces dotData Insight for Salesforce – A Revolution in Sales and Marketing Analytics
Press Release
dotData Announces dotData Feature Factory 1.1 with GenAI-Powered Assistance
Press Release
dotData Announces Enhancements Across Its Entire Product Suite
