5 Reasons Why Feature-Oriented Data is the Future of Real Time Analytics


“Our future success is directly proportional to our ability to understand, adopt, and integrate new technology into our work.”

– Sukant Ratnakar, Business Reinvention Author


Operational AI Needs a New Data Paradigm

Human-scale data challenges, however inefficient, have effectively been solved. The goal with adopting a feature-oriented mindset is to focus on solving the machine-scale challenges that we have only just begun to face. Feature-oriented eliminates the data preaggregation step. This is the single most impactful factor in compressing the time it takes to access data from minutes, hours, or days to milliseconds. Why is this important for machine learning?

Traditional Data Access: Collect and store data, manage in legacy human-centric format, preaggregate data, delayed data accessFeature-First Data Access: Collect and store data, manage in machine-native format, instant data access

A feature-oriented approach automatically converts data into a faster, computer-optimized format as the first step in the process, eliminating the need to preaggregate data before putting it to use in complex analytics and ML applications.


First Things First: What’s a Feature?

When developing AI, data scientists build models that look for patterns in data. These patterns are used to make decisions and predict outcomes. This is powerful and has many applications.

Models must be fed data in order to learn. The data that trains models are called features—you can’t just throw raw data into a model.

In the simplest terms, a feature is a data point that is of interest to a data scientist for building a model. But it’s not just any data point—a feature is a type of measurable data upon which a decision can be made. For example, “animal” is not an actionable feature, but the value of a trait called “is_animal,” could be a useful feature. If you know whether or not something is an animal, you can make a decision based on that information.

This is important because machine learning is fundamentally about making a series of decisions. Features allow machines to make decisions. Billions of them. Instantly.

A simplified example would be attempting to predict a type of animal based on its traits. As humans, we’d typically have a list of characteristics we’d look at: wings, snout, number of legs, etc. But a list of generic traits is not decisionable for a computer. If we convert the traits into features “has_wings,” “has_snout,” “has_4_legs,” etc., those features could be fed into a model. The actual feature values would be “yes” or “no,” i.e., “1” or “0.” This format enables the model to process millions of records and identify the type of animal based on the feature values. The presence of a feature (or not) allows the computer to quickly eliminate entire categories at once (for example, if “has_wings” has a value of “0,” the computer knows all birds, bats, bees, etc., are out). Thanks to this decision process, a machine can sort through massive amounts of data to predict animal types faster than any human.

Chart defining feature vs feature value vs feature table

Features are the VIPs of the ML process

Feature selection is one of the most critical steps in a successful ML project. Data scientists must decide what features they need to engineer from the raw data to power models that ultimately achieve the desired business outcomes. This is both an art and science because it’s not always clear in advance what features will be valuable in a model.
Think of a data scientist as a master chef. Now imagine if that chef was developing a new recipe and didn’t have sugar, salt, butter, or spices in their kitchen. Features are the ingredients for models. In today’s standard ML process, data scientists must ask IT for each ingredient in advance and then wait days or weeks to access only those precise ingredients. For example, if the data science chef wants to try adding a little butter to the sauce, they have to go to the farm, request butter, wait for the cows to get milked, and the cream to be churned so on.

FEATURE-ORIENTED means automatically converting your data to a machine-native format so that it is computable and ready for feature selection and model training throughout the entire ML lifecycle. In other words, you effectively turn all of your data into features before doing anything else with it.

the benefits of a feature-oriented approach table

The Benefits of a Feature-Oriented Approach

Feature-oriented is different. Feature-oriented means automatically converting all data to a machine-native format so that it is computable and ready for feature selection and model training at all times. It’s the equivalent of stocking the data scientist’s kitchen with every possible ingredient at arm’s length before, during, and after recipe development.

Furthermore, when all of your data is converted to the machine-native 1’s and 0’s format at the outset, everything you do with the data is faster and more efficient, not just ML modeling. That’s why we believe the feature-oriented paradigm is so revolutionary. If you manage big data, you will benefit from implementing a feature-oriented process. Period. If you are implementing operational AI, manage massive amounts of big data, need real-time access to all of the data for instant queries, and you aren’t a FAANG company, we think you would be crazy to not pilot a feature-oriented project.


Quick Summary

  1. Features are the ingredients for building AI.
  2. The feature engineering and selection process turns raw data into computable features.
  3. That process requires manual provisioning, and is painfully slow and restrictive.
  4. Consider automatically converting all your raw data into features, first.
  5. Feature selection would be self-serve, on-demand, fast, and flexible!
feature-oriented field guide

Feature-First AI Field Guide