Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Machine Learning
Applicable Industries
- Cement
- Construction & Infrastructure
Applicable Functions
- Quality Assurance
Use Cases
- Time Sensitive Networking
- Visual Quality Detection
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Deutsche Börse Group is an international exchange organization and innovative market infrastructure provider. The company offers its customers a wide range of products, services, and technologies that cover the entire value chain of financial markets. Operating globally, Deutsche Börse Group is a key player in the financial services industry, dealing with huge volumes of stock data. The company sought to transform this data into a significant revenue contributor by investing in data science and migrating to a cloud-based data science center.
The Challenge
Deutsche Börse Group, a global financial services company, saw an opportunity to transform the large volumes of stock data, previously considered as 'exhaust' of their trading business, into a significant revenue contributor. The company decided to invest in data science to sell not only raw data but also more advanced content. Despite having invested in on-premise architecture in the past, Deutsche Börse Group realized the need to build its new data science center in the cloud to leverage the cloud's flexibility and scalability. However, the company faced a challenge. Business users required specific transformations to be made to the data before it could be migrated to the cloud, but they did not want to overload the already busy IT team with requests. Furthermore, Deutsche Börse Group wanted to prevent their highly-trained data scientists from spending most of their time on data cleansing and preparation tasks, even after the data migration.
The Solution
Deutsche Börse Group adopted Designer Cloud to securely transform and move data from its on-premise environment to a cloud platform without burdening the IT team. Designer Cloud enabled business users to see exactly how data would be transformed before moving it to the cloud. It also provided a clear audit trail of where the data originated and how transformations had been applied. The ability to save and reuse transformations allowed business users to accelerate their work with each new batch of data. Moreover, as data scientists leveraged data for machine learning or predictive models, Designer Cloud enabled them to reduce the amount of time spent preparing data. For instance, a project that once required nine months has now been reduced to three weeks with Designer Cloud.
Operational Impact
Quantitative Benefit
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