By Grace Hall, Product Leader | Product Manager of Data Strategy at Resonate
This article is part of a five-part series exploring the most pressing pain points data scientists face when trying to increase model lift—and how Resonate Embeddings offer a practical, innovative solution.
Why quality beats quantity when it comes to boosting model lift.
In the era of big data, the temptation is to think, “If we just have more data, we’ll get better results.” But for many data scientists, more data doesn’t always mean better outcomes—it often means more noise, more complexity, and more headaches.
The Big Data Paradox
Every day, businesses collect terabytes of data—clickstreams, transactions, social signals, surveys, you name it. But here’s the catch:
- Noisy Data: More data means more irrelevant information that can drown out useful signals.
- Redundancy: Duplicated or overly similar data points can inflate datasets without adding value.
- Gaps in Behavior: Many datasets lack the behavioral and attitudinal nuances that truly make predictions actionable.
The result? Models trained on these bloated datasets often underperform, despite having “more data.”
Finding the Right Signals in the Noise
For predictive modeling, the goal isn’t just to collect data—it’s to extract meaningful patterns that directly impact outcomes. That’s why data enrichment is critical. By incorporating behavioral insights (how people act) and attitudinal signals (why they act), you create a richer context for predictions, making your models smarter.
Example: Imagine you’re building a model to predict online purchases. Traditional data might show visits, clicks, and cart abandonments. Enriched data might reveal why those carts were abandoned—price sensitivity, loyalty concerns, or simply browsing behavior.
How Behavioral Data Can Help
With Resonate Embeddings, you can:
- Filter out noise by focusing on relevant behaviors.
- Reduce data overload while increasing predictive power.
- Bring clarity to chaotic datasets without adding unnecessary complexity.
Next Up: Streamlining Feature Engineering
Even with high-quality data, there’s a challenge every data scientist knows all too well: feature engineering. In the next article, we’ll discuss how Resonate Embeddings can simplify this resource-intensive process, allowing you to focus on driving results.
Have you ever struggled with noisy or redundant datasets dragging down your model’s performance? Contact a Resonate data expert to learn how you can unleash the full potential of your data, streamline your workflows, and drive actionable insights for smarter decision-making.
