- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Product Research & Development
- Data Science Services
DemystData wanted a way to handle the increased complexity and time-intensive work related to datasets getting bigger and data sources becoming more varied. Their data scientists were spending lots of time manually building data science and machine learning pipelines.
DemystData aims to close that gap and help solve the problem by opening up their clients’ access to new and more data. But as datasets get bigger and data sources more varied, that also means increased complexity and more time-consuming work for an already limited pool of data science resources at the New York-based company.
DemystData started in 2010 upon realizing that, while the world is supposedly awash with data, very little of it is being used for customer benefit. 90% of analytics projects failed at the ideation, discovery, or deployment phase, and value is rarely realized. The data exists. The analytics is possible. But the ecosystem is complex. And enterprises have growing barriers to capturing value.
Using DataRobot's Automated Machine Learning platform, DemystData was able to automate and streamline the more laborious and time-consuming parts of data science, like feature engineering, model feature selection, and model deployments, to improve speed to values for various machine learning projects.
By automating many of the previously manual and time-consuming steps of the machine learning lifecycle, DataRobot was able to help DemystData improve not only the quality of their models but also their overall data science productivity
DataRobot didn’t just impact the data scientists at DemystData; because of its simplicity and ease-of-use, even DemystData workers who didn’t have a background in mathematics or data science were now able to contribute to machine learning projects.
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