Explorium's Data Insights Blog
Where data, marketing, and sales professionals come together
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 regulations, improved technology, and a friendlier landscape for lenders meant that financing and loans were easier than ever to come by. Over ten years later, it took only three […]
Economic Data
What is economic data? Economic data measures the financial health or wellbeing of a country, specific regions, or individual markets. It is often presented in comparison with past measures. This data is used to power economic analysis and enrich other categories of financial data. Where does the data come from? The majority of the economic […]
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 record for the global economy and for businesses everywhere. As fears over the spread of COVID-19 (also known as novel coronavirus) reach a fever pitch, national economies have stalled, […]
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. Sign-ups and downloads of your app. But no matter how complete that data, no matter how intelligent your prediction models, you don’t exist in a vacuum. You can’t collect […]
How Much For The Set? How You Can Monetize Your Datasets
Data science websites overflow with tips for organizing and handling your data, improving visibility and gaining insight into your company’s performance. The question is, though: how do you turn this into cold, hard cash? Buckle up, because you’re about to find out. How Does Data Monetization Work? Before we take you through the process of […]
Understanding and Handling Data and Concept Drift
I know you’ve heard this a million times, but I’ll say it one more time: nothing lasts forever. Youth is not eternal, your phone gets slower, and machine learning models deteriorate over time. Like the second law of thermodynamics says, over time, things tend towards disaster. In the world of machine learning, this translates to […]
NAICS Data
What is NAICS industry data? Industries ranging from agriculture to finance are classified into various categories and then assigned codes. This categorization of industries organized by NAICS (the North American Industry Classification System) codes is industry NAICS data. This data provides key information on a region’s diverse industry sectors, business establishments, retail trade, county business […]
Map Data
What is map data? Map data is a part of geospatial data. It data includes geographic information such as political boundaries, weather patterns, infrastructure information, real-time traffic, and points of interest. Map data is typically in an interactive visual format. Individuals, businesses, and governmental organizations use map data for geocoding and accessing a wide variety […]
Location Data
What is location data? Location data presents geographic information about precise locations expressed in coordinates for devices such as mobile devices, mobile phones, smartphones, tablets, smartwatches, or IoT devices. This data is valuable for many reasons, such as emergencies, including missing persons, individuals in danger or affected by natural disasters. Businesses use this data for […]
Stay Inside The Lines: Coloring With Artificial Intelligence
After a few months with no side projects on my plate, I was eager to create something new. I’ve made it a routine to try and create AI that competes with my nephew in games he’s playing (just like in my previous posts). So, I asked my nephew what game he’s playing at the moment […]
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. Before getting to engineer features, each modeler should ask themselves: “is that all the relevant data? Are there, maybe outside of the organization, additional sources that could generate impactful […]
Clustering — When You Should Use it and Avoid It
No matter what type of research you’re doing, or what your machine learning (ML) algorithms are tasked with, somewhere along the line, you’ll be using clustering techniques quite liberally. Clustering and data preparation go hand in hand, as many times you’ll be working, at least initially, with datasets that are largely unstructured and unclassified. More […]
AI Across the Funnel: The Use Cases You’re Missing Out On
The AI revolution, as a continuation of the BI revolution, caused companies to acquire, ramp up, centralize, and deepen their data analytics, data science, and data handling capabilities. As part of this revolution, companies are building some of their core capabilities around machine learning models as an automated mechanism to make business decisions. In finance, […]
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 what Donald Rumsfeld famously called “unknown unknowns”. In a high-risk, high-reward business like online lending, incomplete information quickly puts you at a disadvantage – and your business in real […]
The Top Six Must-Attend Data Science Conferences Coming in 2020
For all the hype and press it has received in the past few years, data science is still a field very much in flux. Although it’s not a new sector, the sheer amount of innovation and expansion it has seen means that knowledge you had about data science three years ago (and even last year) […]
Cracking The Stability of Feature Selection
One of the best ways to improve your production machine learning models is to improve the data that your model is trained on. Simple, right? Not when trying to do it at scale with the curse of dimensionality — various anomalies that begin to appear that wouldn’t naturally appear in low-dimensional settings — haunting you. […]
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 of the right data to feed their models and make better predictions. This isn’t always the easiest task. The process of acquiring external data might look a little something […]
Extend Your Machine Learning Pipeline With Your Prediction Outcome
Close your eyes and imagine a machine learning production pipeline (come on, you can do it). What do you see? Oh yes, data sources; a data warehouse, a streaming queue, you know them. Now continue. What else do you see? Maybe an ETL service that is regularly cleaning, munging, and merging this conglomerate of bits […]
