The data science ecosystem is vast. It encompasses many technologies – from those focused on cleaning and curating data sources in the most initial steps, to those performing machine learning and predictive analytics in the final step of disseminating results. In between, you’ll find technologies that perform data collection and assimilation, like databases, ETL (extract, transform and load) tools, business intelligence solutions and many others.

Data science is not one of these things; it’s all of them.

While data scientists rely on myriad tools to derive insights and build predictive models, data science remains a human-driven, iterative process – particularly in the critical feature engineering stage.

What is feature engineering?

Before data scientists can apply machine learning to understand and operationalize a solution to a business need, they need to define the prediction problem. Then, they must brainstorm and calculate explanatory variables. This is feature engineering, and it has been one of the most persistent bottlenecks in the data science process.

A data scientist tackling this task for, say, an e-commerce company that wants to predict future sales behavior, might start by asking questions such as:

  • How often does the customer purchase products?
  • How long has it been since the customer purchased last?
  • Does the customer typically buy low-end or high-end products?
  • How often does the customer abandon products in the cart?

These questions are then translated into features by following relationships, aggregating values and calculating new features. Doing that manually, however, can take a team of data scientists weeks or months. Not only does that hold up the machine learning work to come and slow the process of solving the initially identified business need, it also increases the potential for errors that sometimes aren’t uncovered until projects are near completion.

That’s why feature engineering is the No. 1 part of the data science process ripe for automation. According to Gartner, even if your “organization already has a data science team…it may need to be enhanced with even more specialized data science skills specific to machine learning, such as feature engineering and feature extraction.”

Removing the bottlenecks in the data science process

Feature Labs’ Deep Feature Synthesis algorithm automates the onerous work of feature engineering, so data scientists can solve more business problems, based on more predictive questions, more easily and much more quickly. It’s about accelerating data science so companies can adopt Machine Learning 2.0, which streamlines the process of creating and deploying new machine learning products and services.

Once enterprises automate feature engineering, they reap greater value from data science. When they solve their most pressing problem, they can start on the next one. And the next. And the next. They move from solving one prediction problem to solving 10 or 100 in the same period of time. It completely changes the way data science teams apply machine learning.

By providing a reusable framework to automatically formulate prediction problems, automated feature engineering enables the building of more unique predictive problems. This shift is changing what teams can do with their data, across every part of the data science ecosystem.

Learn more in the peer-reviewed paper, “Deep Feature Synthesis: Towards Automating Data Science Endeavors.”