It's all about the data: Explorium's bet pays off in $75M Series C funding

Data science and machine learning are being commoditized, so it's the datasets that make the difference in the end. That's the thesis startup Explorium is capitalizing on.
Written by George Anadiotis, Contributor

Data driven analytics and machine learning predictions are key to business success, but they are being commoditized. How well they work is all about the data. This is the position startup Explorium took a bet on in 2019, and it seems to be paying off.

After a $19M Round A in 2019 and a $31M Series B in 2020, today Explorium announced it has closed a $75M Series C funding round, led by global venture capital and private equity firm Insight Partners, with existing investors Zeev Ventures, Emerge, F2 Venture Capital, 01 Advisors and Dynamic Loop Capital also participating.

We covered Explorium when it first set out to conquer the world in 2019. We caught up again with co-founder and CEO Maor Shlomo to discuss progress made, and the state of external data acquisition in 2021.

The state of external data acquisition in 2021

Explorium is growing alongside the demand for external data in organizations. As business conditions evolved and regulations restricted access to crucial sources of information, teams went hunting outside their organizations for data to support machine learning and other mission-critical analytics.

recent survey found enterprises are hungry for external data, but they don't necessarily have a clear idea how to get it. Here are some of the key findings.

Organizations value data acquisition, but that doesn't mean they have a clear strategy. Survey respondents overwhelmingly indicated that the acquisition and onboarding of external data was important to their business.

Interestingly, less than a third of respondents actually have a strategy in place, with 26% relying on ad-hoc practices or an informal process for data acquisition. Seven percent of respondents find data acquisition so challenging, they don't do it at all.

Most organizations are increasing their data acquisition investment for 2021, and allocating significant budgets for this. In 2020, 81% of companies spent more than $100k each month on external data acquisition, and 31% spent more than $500k.


Demand for external data acquisition is growing in organizations, and it's a complicated issue. Image: Explorium

Almost half of respondents said they spend over 50 hours per month on external data acquisition. In 2021, 78% of respondents are planning to increase their budgets for external data acquisition, and only 1% do not have a budget in place at all.

Multiple vendor relationships are slowing down external data acquisition. Nearly all respondents engage with at least two data providers in their external data acquisition strategy.

Sixty-nine percent of companies engage with three or more vendors, and 7% have five or more vendor relationships. Expectedly, the more data vendors organizations engage with, the more time and money they spend on data acquisition.

Most organizations need to discover and onboard external data at scale. When asked about the types of external data the organization was purchasing, over 50% of respondents said they are purchasing three or four types of external data.

This means the efforts and costs of purchasing a single data source must be multiplied in order to fulfill their business needs. As the number of use cases, data sources, and providers evaluated for each data source grows, this increases efforts exponentially, and so organizations must find a way to make the process more scalable.

Removing friction is the road to success

Concluding, the survey found that 77% of respondents simply don't know what to look for in data acquisition, despite considering it valuable. Nearly all respondents said that finding relevant external data, as well as deriving insights from data, is medium to high effort for their organization.

Granted, that survey was sponsored by Explorium, and it points towards a single end-to-end platform, which Explorium would very much like to be. Regardless, the need for data external to organizations seems to have legs. As Shlomo emphasized, COVID-19 has exacerbated this by showing how diversified datasets can lead to better predictions.

Explorium touts itself as the external data platform that automatically discovers thousands of relevant data signals and uses them to improve analytics and machine learning. Explorium's platform works in three stages: Data enrichment, feature engineering, and predictive modeling.

The platform acts as a data marketplace and a predictive model hub at the same time. It aggregates and provides access to an array of data sources external to organizations. When the most relevant datasets have been chosen, Explorium uses them to generate candidate features for machine learning models.

Explorium then evaluates hundreds of models on different subsets of features and sources to introduce automated feedback on the data sources and features. Eventually, Explorium converges into the best subset of features given a specific model.


Explorium's platform works in a 3-step process: Data enrichment - Feature engineering - Predictive modeling. Image: Explorium

The basic recipe has not changed, Shlomo said, because it works well. Explorium is seeing exponential growth, he went on to add. Both its team and is customer base are growing. In the last year, Explorium has doubled its customer base and more than quadrupled revenue.

Companies like BlueVine, GlassesUSA.com, Melio and PepsiCo use Explorium to enhance AI models for use cases including lead scoring, identifying default risk and fraud and upleveling analytics such as demand forecasting and customer lifetime value.

On the technical side, Explorium has progressed by expanding the breadth and depth of its offering. Recently it introduced Signal Studio, which Shlomo described as a streamlined user interface to help users who are not necessarily data scientists build their own custom data feeds.

Explorium has also built integrations with a number of 3rd party data vendors, such as AWS, Azure, Google Cloud, SAP and Snowflake. Shlomo referred to those as deep integrations, mentioning for example how Explorium enables Snowflake users to integrate external data without having to code.

It's all about removing friction, as per Shlomo. Explorium did not invent data marketplaces, or AutoML. But it came up with a way to bring them together and make them easy to access, and that's the road to success. 

Explorium will use the funding to grow its ecosystem and offering further, and there will be more announcements soon, Shlomo concluded. The round brings Explorium's total investment to more than $127M, and it entails George Mathew, Managing Director at Insight Partners and former President & COO of Alteryx, joining Explorium's board of directors.

Editorial standards