In addition to providing 1000s of data signals in one platform, Explorium automates many of the tasks involved with external data sourcing and wrangling, including data discovery, cleansing, preparation, matching, and integration. Using Explorium allows data scientists to spend less time on finding and preparing data, and more time analyzing and understanding business-shaping insights.
Try Explorium for free to see how easy it is to find the most relevant external data features to solve the most complex business problems.
All of the external data you need in a single solution.
Explorium is an end-to-end external data platform. Data scientists spend a lot of time sourcing external datasets and preparing them for consumption in predictive models. Explorium helps with every step of the external data process from accessing the most relevant external datasets and signals, to integrating them into data pipelines, and using them to train more accurate models. By automating this process, Explorium enables data scientists to focus more of their energy on driving high value insights.
Automated Feature Discovery & Selection
Find data from multiple sources, in one place.
Explorium automatically discovers the most relevant data sources, signals, and features from our vast data gallery to improve your model performance.
Don’t waste time formatting, matching, and integrating external data sources with internal data.
Explorium seamlessly integrates and enriches the data you have on hand with features from 100s of external data sources.
Build accurate predictive models trained on high-quality data.
Use Explorium’s Prediction Engine, and see the model uplifts instantly. You no longer have to worry about the ROI of data purchases or finding new features to address model drifts.
Learn how Idea Financial cut their data expenses by 50%.
You’ve spent weeks (or months) building a new machine learning model, and you think you’ve cracked it. But, when the rubber meets the road, the answers you get seem disconnected from reality. The problem could be that although your model seems great on paper, the data you used to train it is riddled with errors and inconsistencies.
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