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Data Enrichment

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External Data Risk Models
March 25, 2020 Explorium Data Science Team AI Education

COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

The previous decade has been one of impressive growth and massive profits for financial services providers. As the global economy bounced back from the recession, new

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data crisis
March 16, 2020 Explorium Data Science Team AI Education

Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

“Only a crisis —  actual or perceived — produces real change.” – Milton Friedman The start of 2020 has been one of the most turbulent on

SMBs and external data
March 9, 2020 Shelby Blitz Data Enrichment

How SMBs Can Boost Their Bottom Line With External Data

There are some insights that can only come from the data you produce or collect in-house. Historical sales figures, for example. Foot traffic through your store.

feature engineering for machine learning
February 10, 2020 Eilon Baer Data Enrichment

Support and Coverage – Data Integration Metrics You Should Know

Data enrichment is a crucial step in the modeling process that data scientists tend to overlook due to the difficulty in finding and utilizing external sources.

machine learning in banking
January 20, 2020 Explorium Data Science Team Data Enrichment

It’s Official: Online Lenders Can No Longer Afford to Ignore Alternative Data

You don’t know what you don’t know, as the old saying goes. But in the age of Big Data, you simply can’t afford to shrug off

gold mine
January 9, 2020 Revital Rubin Data Enrichment

5 Steps to Mine Hidden Gold Out of An External Data Source

If you’re familiar with Explorium then you know that we believe the core challenge for data science is data. Specifically, we believe data scientists need more

July 9, 2019 Maël Fabien Data Enrichment

Who’s the painter?

Better features, better data

Categories
  • All posts
  • AI Education
  • Automation
  • Business Intelligence
  • Data Enrichment
  • Data Science
  • Explainability
  • Feature Engineering
  • Feature Selection
  • General
  • Interpretability
  • Machine Learning
  • Predictive Models