From declining a credit card transaction for risk or fraud, to loan approval, product quality control and anything where data plays a role, automated analytical models are what drives digital transformation.
The steps from data to discovery to deployment are a continuous loop in the analytics life cycle, driven by teams of business analysts, data scientists, and development operations experts.
With time-to-value always in mind, one goal is to intelligently automate and speed up processing wherever possible. One key aspect of solving the processing problem is called massively parallel processing (MPP), which increases the speed of running analytical models through large complex data, allowing your modelers to be up to 100x more efficient.
SAS helps make organizations exponentially more efficient, and productive. How?
- We have automated repetitive steps across the analytics life cycle and the entire analytics process.
- We have packaged best practices into intelligent templates that data scientists can use and customize for rapid prototyping.
- We can increase the speed and productivity of data science teams by enabling analytical workloads to run in massively parallel processing (MPP) environments – allowing modelers to be 100x more efficient.
- By automatically generating natural language to explain findings in the data, we ensure that analytics automation helps everyone in the organization become more productive.
- Model Deployment: With SAS, you can register and deploy analytical models with one click for instant value, avoiding months of recoding for operational environments.
- Model Health: SAS ensures your models are in optimum health at all times. With efficient model processing and governance, you can easily compare and test analytical models, generate performance benchmarking reports and alerts, and send workflow notifications.
