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Democratizing Data Science at DemystData
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Data-as-a-Service
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Predictive Quality Analytics
- Demand Planning & Forecasting
Services
- Data Science Services
The Challenge
DemystData, a New York-based software company, aims to 'demystify' data by providing a platform that helps clients discover, explore, and access the vast world of data. However, as datasets get larger and data sources more varied, the complexity increases, leading to more time-consuming work for the company's limited pool of data science resources. The company's clients, particularly financial institutions, are underutilizing data, leading to business decisions being made based on suboptimal or incomplete information. DemystData aims to close this gap by increasing their clients' access to new and more data.
About The Customer
DemystData is a New York-based software company that provides a platform to help clients discover, explore, and access the vast world of data. The company's clients are primarily financial institutions, including legacy big banks, which are underutilizing data, leading to business decisions being made based on suboptimal or incomplete information. DemystData aims to close this gap by increasing their clients' access to new and more data. However, as datasets get larger and data sources more varied, the complexity increases, leading to more time-consuming work for the company's limited pool of data science resources.
The Solution
DataRobot's automated machine learning platform was introduced to improve DemystData's model quality and overall data science productivity. The platform generates dozens of algorithmic-agnostic models in minutes, allowing DemystData to focus on their core competency of helping clients find more data and identify more signal. DataRobot also democratized data science across the entire organization. By automating many of the previously manual and time-consuming steps of the machine learning lifecycle, DataRobot improved not only the quality of their models but also their overall data science productivity. The platform's simplicity and ease-of-use enabled even non-technical employees to contribute to machine learning projects.
Operational Impact
Quantitative Benefit
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