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Feature engineering framework: Putting ready-to-use data at your fingertips

Today, it isn’t enough just to collect customer data. To ensure you are unlocking its potential and maximizing its value, you need to structure it in the form of valuable customer insights, analyze it, apply it and reuse it. That’s why raw unstructured data isn’t of much help in the value generation process.

In a world where data is the new gold, data mining is a critical task for companies looking to stay in business.

The building blocks of machine learning models

Machine learning relies on historical data and experiences to gather attributes (or features) about its database, make inferences and create a machine learning model. This ML model can then be reused to craft predictions, like identifying opportunities, most-likely outcomes, and near-future behavior.

Features (also known as attributes, variables or predictors) are the most basic and defining components of the machine learning model. During the model training phase, internal parameters are adjusted based on the training data, namely features. Model precision, quality and durability are largely determined by the quality of the features used. However, when using the model for prediction, inference, scoring or in real-world production, it needs to receive data in the same form and shape as it was trained on. If not, the resulting predictions may be inaccurate or implausible. This represents a major setback for data teams, who would be forced to repeatedly create new ML models for different projects and/or departments — an unsustainable effort with soaring costs and no tangible results.

What can organizations do to counter this? The answer is the creation of a feature store.

Feature stores: The future of ML

A feature store is exactly what its name suggests — a catalogue of attributes (features) available for machine learning. This catalogue of existing features can then be updated with new information and fresh data to fuel the machine-learning algorithms in production. Simply put, the reliable and regularly refreshed insights extracted from raw data form the crux of a feature store — a prerequisite for any ML model.
Let’s now take a look at how a feature store delivers a bang for one’s buck:

1. It enables consistency of features at the training stage and the features at the inference stage, thereby reducing the data team’s efforts to create new ML models. Its reusability factor and continuous upgrade add to its appeal.
2. An organization can use a centralized feature store to develop use cases, drive faster time to market, and deliver value quickly.
3. An organization can leverage a rich variety of data to calculate the churn rate or personalize offerings for a better customer experience and higher conversions.

Developing content for a feature store

Data scientists and machine learning engineers spend between 45-80% of their time preparing, reviewing and analyzing data. This is tedious work that often goes unappreciated. Creating new features cannot be skipped entirely, but here are two types of activities that can be reduced:
1. Not starting from scratch over and over again, the reusability factor of the feature store is a definite advantage here.
2. A clear form on how to write feature logic and automate the repetitive work – Here’s where the Feature engineering framework comes into play.

...Data scientists spend 45% to 80% of their time preparing, reviewing and analyzing data. Feature engineering framework helps data scientists build a reliable registry of attributes ready for AI use cases faster and save time by building on previous work. ...

The DataSentics Feature engineering framework

The Feature engineering framework guides data scientists on how to write the code and offers them the capabilities that would otherwise have to be developed from scratch. It leaves them with just one responsibility – to write the feature logic (transformation). Not only does this save time, but it also reduces the effort needed to deploy new features into production.

The Feature engineering framework is also an integral lever in the Persona 360 product from DataSentics, which enables businesses to make an impact on their business metrics using customer data, as well as being used as a standalone tool.

To learn more about the Feature engineering framework, its use cases and potential business benefits, please feel free to contact me to arrange a meeting to discuss this further.

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About Matěj Matoulek
Product manager
Matěj is an AI enthusiast with 7 years of rich and layered experience in the data analytics, AI and ML space. At Atos’s DataSentics, he plays a pivotal role as a product manager, leading AI CDP Persona 360 — a unique product poised to help businesses understand their customer's preferences and behaviour patterns and use these insights to make data-driven decisioning in activation platforms. Prior to this, he was engaged at MONETA Money Bank, where he was instrumental in evangelizing AI and data-driven decision-making and enhancing advanced data analytics and ML capabilities for risk and customer value management analysts. In his free time, he likes to play badminton.

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