I recently spent some time with Robin Dement, Vice President, R&D-IT Medicines Development Capabilities at GlaxoSmithKline. Robin has a unique perspective on IT (Information Technology) since she started as a chemist in Research and Development at GSK. She is now responsible for managing relationships and prioritization across multiple R&D Executive members
Analytics Infrastructure: Vision & Strategy Consideration #1 (Part 1 of 15 considerations for Analytics Infrastructure) In a perfect world, the entire organization would be aligned, and the analytics vision would be driven by top down, executive leadership. Since we aren’t living in a perfect world, it often takes work to
This may be controversial coming from someone that has worked with data his entire career, someone that has been involved with software vendors focused on data integration and access for the last 6 years, and someone that is responsible for product marketing for SAS data management capability, but I’ll say
Big hype about big data has played a significant role in driving awareness about the value of analytics. SAS welcomes the interest in big data, since it highlights our ability to work with huge volumes of complex and diverse data. Since this is such a critical topic, we have formulated
The basic big data problem is simple to understand: we create too much data to store and analyze it all. The problem gets bigger, however, when you consider the related factors: our problems themselves are getting bigger, the analytics needed to solve them are more complex and the data is
“You don't talk about data quality.” No, wait—that's The First Rule of Poor Quality Data. The First Law of Data Quality: “Data is either being used or waiting to be used—or wasting storage and support.” Although understanding your data is essential to using it effectively and improving its quality, as
