A good analytical model should satisfy several requirements, depending on the application area. A first critical success factor is business relevance. The analytical model should actually solve the ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

Inaccurate or overlooked alerts on manufacturing data can be reduced with proper data handling when developing and deploying predictive models. Data analytics, and specifically predictive analytics, ...

Founder and Managing Principal of DBP Institute. I consult companies on how to transform technology and data into a valuable business asset. There are many reasons for this poor success rate, one of ...

The Heisenberg uncertainty principle, which has origins in physics, "states that there is a limit to the precision with which certain pairs of physical properties of a particle, such as position and ...

In a study published in Analytical Chemistry, researchers from the University of Amsterdam's Van 't Hoff Institute for Molecular Sciences (HIMS) reveal a sobering reality regarding nontargeted ...

"Analytics as a discipline has changed dramatically in the last five to 10 years – and for sure in the past five," says Anne Snowdon, chief scientific research officer at HIMSS. "With the explosion of ...

HIMSS Analytics has launched a new maturity model to help healthcare organizations measure how their technology deployments compare with their peers. The new Infrastructure Adoption Model, or INFRAM, ...