Lays the Groundwork for its Data Science Lab with Designer Cloud powered by Trifacta
92 %
Reduction in analytic build time
Core Challenges
Deutsche Börse Group saw an opportunity to transform what they once considered the “exhaust” of their trading business—huge volumes of stock data—to a sizable revenue contributor. Investing in data science would allow them to not only sell raw data, but more advanced content. Despite historical investments in on-premise architecture, Deutsche Börse Group knew it needed to build its new data science center in the cloud in order to take advantage of the cloud’s flexibility and scalability. The problem? Business users needed specific transformations made to data before it could be pushed into the cloud, but didn’t want to inundate an already busy IT team with requests. Even post-data migration, Deutsche Börse Group also wanted to prevent their highly-trained data scientists from spending the majority of their time on data cleansing and preparation tasks.
How Designer Cloud powered by Trifacta Solved this Problem
Designer Cloud powered by Trifacta allows Deutsche Börse Group to securely transform and move data from its on-prem environment to a cloud platform without burdening IT. Business users can see exactly how data will be transformed before moving it to the cloud and, perhaps equally important, can leave a clear audit trail of where the data originated and how transformations have been applied. The ability to save and reuse transformations means business users can accelerate this work with each new batch of data. And as data scientists leverage data for machine learning or predictive models, Designer Cloud allows them to reduce the amount of time spent preparing data; for example, a project that once required nine months time has now been reduced to three weeks with Designer Cloud.
Secure Data Migration
Seamless, visually-driven data transformation and migration from on-prem to cloud platforms. Detailed data lineage provides transparency around data origins and changes.
Increased Data Quality
Allowing business users with the right context of the data to prepare it themselves leads to higher quality data and, down the road, more robust insights.
Increased Efficiency
IT involves themselves in data preparation only when needed; data scientists keep their focus on modeling, not cleansing.
