DataRobot Case Studies Demystifying Data Science: A Case Study on DemystData and DataRobot

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	Demystifying Data Science: A Case Study on DemystData and DataRobot DataRobot

Demystifying Data Science: A Case Study on DemystData and DataRobot

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
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. Read More
About 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. Read More
Customer Name	DemystData Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

Customer Name

DemystData

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.

Impact #1	DataRobot had a significant impact on DemystData's operations, not only improving the quality of their models and overall data science productivity, but also democratizing data science across the entire organization. The platform's simplicity and ease-of-use allowed even non-technical employees to contribute to machine learning projects, fostering a more inclusive and collaborative work environment. This also changed how DemystData approached hiring, as they could now focus on hiring for specific capabilities rather than generalist data scientists. Furthermore, DataRobot enabled DemystData to focus on their core competency of finding more data and identifying more signals, leading to more efficient use of resources and better business outcomes.

Impact #1

DataRobot had a significant impact on DemystData's operations, not only improving the quality of their models and overall data science productivity, but also democratizing data science across the entire organization. The platform's simplicity and ease-of-use allowed even non-technical employees to contribute to machine learning projects, fostering a more inclusive and collaborative work environment. This also changed how DemystData approached hiring, as they could now focus on hiring for specific capabilities rather than generalist data scientists. Furthermore, DataRobot enabled DemystData to focus on their core competency of finding more data and identifying more signals, leading to more efficient use of resources and better business outcomes.

Benefit #1	DataRobot's platform was able to generate dozens of models in minutes, significantly reducing the time spent on modeling.
Benefit #2	DemystData was able to work at ten times the efficiency with DataRobot compared to their previous operations.
Benefit #3	DataRobot allowed DemystData to do the same amount of work with fewer people, leading to cost savings.

Benefit #1

DataRobot's platform was able to generate dozens of models in minutes, significantly reducing the time spent on modeling.

Benefit #2

DemystData was able to work at ten times the efficiency with DataRobot compared to their previous operations.

Benefit #3

DataRobot allowed DemystData to do the same amount of work with fewer people, leading to cost savings.

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Overview

Demystifying Data Science: A Case Study on DemystData and DataRobot

Operational Impact

Quantitative Benefit