Domo Shines a Light on Dark Data with New Augmented Capabilities in the Business Cloud - Seite 2

 

Adrenaline Transforms is a new capability that allows users to transform massive DataSets – even several billions of rows of data – and rapidly transform them into more meaningful summarized DataSets to deliver extremely powerful dashboards for consumption. Adrenaline Transforms allow you to transform 10 times larger DataSets in less than a tenth of the time as compared to traditional DataFlows. Transformations that would have taken hours can now be performed in minutes or even seconds.  

Intelligent data management 

• More intelligent data connectors that refresh the new data coming into Domo. By only refreshing data that has changed since the last update, and not entire DataSets, customers are able to get new insights in a fraction of the time.   
   
• New drag-and-drop write-back connectors that direct data into workflows in systems outside the data pipeline. This makes data work for the organization in bold new ways that before were never possible by taking Domo insights into other external systems. 

Democratized and automated machine learning 

• AutoML, powered by AWS SageMaker Autopilot, makes it easy for anyone to automate their business by democratizing machine learning in the Domo platform. With AutoML, users tell the system the data they would like machine learning to be applied to and then Domo does the rest. This feature makes machine learning accessible to business stakeholders and accelerates data scientist productivity. With this automated, easy-to-use capability, much of the manual work of stitching together workflows, a process that can otherwise take days or weeks, can be eliminated. 

Powerful transformations enhancements  

• New inline connectors that allow users to seamlessly add new data anywhere in their Magic ETL data pipeline. This saves time by eliminating the need to configure the data in advance. 
   
• Auto Data Hygiene and error handling capabilities that use ML to automatically give users suggested steps to make incoming data more usable. This dramatically reduces the time required for manual data cleansing and preparation.