Explorium Blog

When it comes to external data for machine learning, data catalogs provide a handful of time-saving benefits over databases. Learn more.

Leaders must find a way to prove, tangibly, that embracing data and investing in better ways to use it has true value and real ROI.

Walter White and Marty Byrde are known for their money laundering schemes. But could they really fool the best risk models banks have today? Let's look.

Data marketplaces make the lives of data scientists looking for machine learning datasets much easier. Read how.

Top tips to engage stakeholders at every stage of the data science project life cycle.

In this article, we explain where external data comes from, the key challenges, and how to connect and utilize it with data science solutions.

With ML models rendered useless, we built an entirely new set of COVID-19 signals in our platform that let organizations understand their risk derived from the current pandemic.

Let’s take a look at some of the most prevalent types of bias, the data mistakes that cause them – and how to prevent this from happening in your own models.

During crises, risk takes on a whole new meaning, and assessing it becomes more important, but also much more complex.

Data acquisition is a long and time-consuming process that many data and analytics leaders don't have time for. How can you streamline it for a major impact?

Marketers can't afford to waste time piecing together data for insights. Data science-as-a-service offloads this entire problem for better decisions.

For lenders, pre-COVID-19 data is no longer useful in a post-COVID-19 world. Here's how we're giving our customers external data to ensure their risk models work in our new reality.

It’s time to look outside of our own silos to external data and make sure that despite the crises we face, we can give our organizations the power to make it through. The post Tomorrow Comes Today –

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. The post How SMBs Can Boost Their...

Data science websites overflow with tips for organizing and handling your data. The question is, how can you take this clean data and monetize it?

Over time, ML models start to lose predictive power due to a concept known as model drift. How can you spot data and concept drift and avoid it? Read more.

Can you teach AI to color better than a human? Using a generative adversarial network we can certainly try.

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.

Cluster analysis is an essential tool for data scientists but it shouldn’t be your only one. Discover when you should, and shouldn’t, use clustering.