Machine Learning: Understanding the Difference Between Unstructured/Structured Data
For us non-scientists, machine learning can be a confusing concept. The first step is to identify the difference between unstructured and structured data.
How Enterprise Data Science Has the Potential to Impact Life Sciences
How can life sciences organizations transform large volumes of complex data into hypothesis-generating knowledge representation? Enterprise data science.
What is Value Data and Why Do Information Managers Need It?
Being armed with value data makes justifying R&D content spend significantly easier. Here are some types of value indicators to consider.
Enterprise Data Science: What It Is and Why It Matters
The ocean of digital assets available to the modern enterprise contains an enormous amount of untapped business value. The ability to exploit these digital assets could prove to be the single most important differentiator between leaders and laggards over the next decade.
2 Big Takeaways for Information Managers from the 2017 State of BI and Predictive Analytics Report
How, when, and why are organizations using business intelligence tools? A look inside Dresner Advisory Services’ 2017 Advanced and Predictive Analytics Market Study.
3 Steps to Turn Analytics into Actionable Insights
In order to get the insights you’re looking for, you have to have a clear idea of your goals beforehand.
What Types of Usage Data do Information Managers Actually Want? [Research]
New research from Outsell suggests standalone content usage statistics are not enough. Here’s what information managers actually want from usage data.
How Information Managers Can Explain Usage Data Through Visualizations
Defending your content spend can be difficult. Data visualization helps information managers transform usage data into a clear, engaging story.
Discuss your Research Challenges with CCC at Bio-IT World Conference & Expo ’17 May 23-25 in Boston
Join CCC at Bio-IT World in Boston this May to learn about accelerating research through full text semantic enrichment and data integration.
What is Machine Learning? (And Why Use It?)
Machine learning is making the most of one of pharma’s most valuable assets – data. And it’s transforming the industry in the process.