EXPERIENCE

Connect each phase for a complete analytics solution.

The ability to address and connect each phase is what we call the SAS Analytics Life Cycle. We have it down to a science. Whether you’re exploring ideas in the Discovery phase or putting analytics into action in the Deployment phase, we'll show you how to ask the right questions, find the right answers and take the next step toward getting the most value from your analytics.

Discovery Phase 
Ask questions, define problem

Whether you're a data scientist developing a new model to reduce churn, or a business executive wanting to improve the customer experience, this phase defines what your business needs to know. The answers will drive different requirements for the next phases in the life cycle.

Discovery Phase
Prepare Data

This phase is both critical to success and frustratingly time-consuming. You have data sitting in databases, on desktops or in Hadoop, plus you want to capture live-streaming data.

SAS provides self-service data management solutions to help you access, integrate, transform and modify data – all in one experience and with one language. We can also clean data in-memory and run analysis in distributed computing environments, leveraging in-memory data access speeds. No other vendor does both.

Discovery Phase
Explore Data

Interactive, self-service visualization tools need to serve a wide range of users – from business analysts to data scientists. In this phase, you'll search for relationships, trends and patterns to gain a deeper understanding of your data. You'll also develop and test hypotheses through rapid prototyping in an iterative process. Our goal in this phase is to enable speed-to-discovery.

Discovery Phase
Model Data

You can build predictive models using a variety of SAS solutions that include a set of algorithms to analyze structured and unstructured data. Code your own models, use R or Python, or augment an interactive predictive modeling environment that makes it easy to create, modify and assess thousands of models quickly.

With a few clicks, you can access, modify and transform your data, choose which machine learning techniques you want to apply and run the models in an automated model tournament environment to identify the best performer. SAS provides the best analytics workbench in the industry.

Deployment Phase
Implement Models

Our decades-long history of optimizing and strengthening the connective tissue between Discovery and Deployment shines in this phase. SAS expedites the model deployment process by automating and accelerating common manual tasks, such as the definition of business rules and automatic generation of vocabularies.

We deliver repeatable, automated and governed processes. Our IT-friendly framework makes it easy to trace modeling activities, make modifications and test continually in a single environment.

Deployment Phase
Act to Move Business Forward

In this phase, we enable two types of decisions: operational decisions that are automated, and strategic decisions where individuals make a long-term impact.

Once a model is in a production environment and being executed to provide answers, the champion model is centrally monitored through a variety of reports based on standardized key performance indicators.

Deployment Phase
Evaluate

Because we build every component with the outcome in mind, we now go straight into a feedback loop that monitors, evaluates and retrains the model to always keep your business running at optimal performance. When the performance starts to degrade below the acceptance level (based on the centrally managed model training assets), the model can be recalibrated or replaced with a new model.

Deployment Phase
Ask Again

The marketplace changes. Your business changes. And that's why your analytics process occasionally needs to change. SAS makes it easy to refresh models and ask new questions to be sure you're staying ahead of the game.

Learn more about our analytical life cycle by reading the full white paper.

Managing the Analytical Life Cycle
for Decisions at Scale