SAS Voices
News and views from the people who make SAS a great place to work
A huge proportion of big data is unstructured text (such as client interactions, product reviews, call center logs, emails, blogs and tweets). Organizations starting to invest in advanced analytics often overlook the value text analytics could add to the process. But when data scientists or analysts get to work exploring
It's the age of big data and the internet of things (IoT), but how will that change things for insurance companies? Do insurers still need to consider classic data warehouse concepts based on a relational data model? Or will all relevant data be stored in big data structures and thus
You could say we've been working toward the internet of things (IoT) since computers were first invented. Look at how airplanes have changed from flying by wire to now, quite literally, flying by IoT (or connected plane). The connected car is another example of how big data analytics is the
"Tap into all your demand signals. Organize. Visualize. Analyze. Predict. Orchestrate. Optimize." The availability and collection of data are compelling companies to invest in demand signal management solutions to take advantage of the vast amount of information to support their planning processes. However, many have not gotten the return on
Data monetization, at its simplest, is the process of turning data into bottom-line value for a company -- often through improving efficiency and/or customer experience, and building customer loyalty as a result. This may sound simple, but in practice, it’s anything but. Good data, advanced analytics and real-time decision making
There has been much discussion about the relationship between data science and artificial intelligence. It can become a complicated dance when applied data science is partnered with emerging artificial intelligence technologies. Who takes the lead? How do we keep the beat? Can we make sure neither party steps on the