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Demystifying Data Science: A Case Study on DemystData and DataRobot
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Machine Learning
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Quality Assurance
Use Cases
- Predictive Maintenance
- Time Sensitive Networking
Services
- Data Science Services
- Testing & Certification
The Challenge
DemystData, a New York-based software company, is dedicated to demystifying data for its clients, particularly financial institutions. Despite the increasing use of data in the financial sector, it is still heavily underutilized, leading to business decisions being made based on suboptimal or incomplete data. DemystData aims to close this gap by providing clients with access to new and more data. However, as datasets grow larger and data sources become more varied, the complexity increases, leading to more time-consuming work for the limited pool of data science resources at the company. The challenge was to manage this increasing complexity and workload without compromising the quality of data analysis and insights.
The Customer
DemystData
About The Customer
DemystData is a software company based in New York that aims to 'demystify' data for its clients. The company's primary clientele includes financial institutions, particularly legacy big banks, which are increasingly using more data in their operations. Despite this, data is still heavily underutilized in this sector, leading to suboptimal business decisions. DemystData's goal is to close this gap by providing clients with access to new and more data, thereby enabling them to make more informed business decisions. However, the company faced challenges in managing the increasing complexity and workload associated with larger datasets and more varied data sources.
The Solution
To address this challenge, DemystData turned to DataRobot, an automated machine learning platform. DataRobot allowed DemystData to take an inductive approach to problem solving, starting with the data and looking for answers within it. This approach enabled them to find correlations and predictive factors without having to start with a specific hypothesis. DataRobot's platform was able to generate dozens of algorithm-agnostic models in minutes, improving DemystData’s model quality. Furthermore, DataRobot allowed DemystData to focus on their core competency of finding more data and identifying more signals, as the platform took care of the modeling process. This resulted in a more efficient use of the company's data science resources, as they could spend less time on cleaning variables and testing modeling combinations, and more time on building models.
Operational Impact
Quantitative Benefit
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