Project Manager, FPS Economy
Business Analyst, FPS Economy
Data Management to Boost your Analytics experience
No matter what kind of business or activity you run, analytics is not only important to achieve your goal, but for each and every organization it will be indispensable for surviving in this competitive and rapidly-evolving world.
Analytics, together with innovating technologies, including big data, will be the foundation of new and better businesses. The analytical process starts with data, and we need to guarantee the value of this data before the analysis and interpretation.
Is your data ready to support business analytics? An often-ignored truth is that before you can do really exciting things with analytics, you need to be able to “do” data first.
What do you know about Data Management for Analytics? What do you need to know? Browse around and learn from experts and best practices.
For data to yield valuable insights on demand, it must be transformed into information that is accurate, complete and readily accessible. To build a solid foundation for analytics success, your data management processes cannot be an afterthought. The key question is how do you manage (big) data for analytics?
In this webcast we discuss the importance of data management for analytics. It is essential that you can rely on analytics results such as reports in order to take the best next step and make the right decisions.
During this 6 minutes webcast you will hear about:
- The role of Data Management within the analytics process.
- Challenges to prepare data for analytics.
- How SAS Data Management will boost your analytical experience.
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. In fact, most data scientists spend 50 to 80 percent of their model development time simply preparing data. SAS adheres to five data management best practices that provide access to all types of raw data and let you cleanse, transform and shape it for any analytic purpose. As a result, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes across your business.
Learn which five themes are essential for successful big data analytics?