Even when organizations have the necessary data to improve service processes, customers may still receive confusing communications that make organizations seem incompetent.
- By Mike Schiff
- November 9, 2016
Lead data scientist Alejandro Correa Bahnsen develops machine learning algorithms for fraud detection. He described for Upside the basic skills and personality traits he believes are necessary to succeed in data science.
- By James E. Powell
- November 9, 2016
Learn about the readability of pie and donut charts, explore how to make an easy grid map, and see how to best use graph analysis on text.
- By Lindsay Stares
- November 9, 2016
How election predictions and polls failed, avoid common mistakes in securing network endpoints, and understand the future of big data and emerging technologies.
- By Quint Turner
- November 9, 2016
According to Gartner, AI and new machine learning techniques will enable a new class of intelligent apps and intelligent things -- along with the emergence of so-called digital twins.
- By Steve Swoyer
- November 8, 2016
Improving customer data security (while fostering a good experience), gaining visibility into infrastructure for IT security, and preventing attacks from inside your network.
- By Quint Turner
- November 8, 2016
Part 1 of this series showed that data needs the context of information to be useful. Here we explain why information alone is still insufficient.
- By Barry Devlin
- November 8, 2016
The RDBMS challenges of the 1980s are being replayed in the world of big data.
- By Luke Liang
- November 7, 2016
Data without context lacks meaning and purpose, but how is data different from information? Defining your terms is the first step to more insightful decisions.
- By Barry Devlin
- November 7, 2016