If your Agile software development is delivering less functionality or producing more defects in each iteration, then your Agile project team probably believes that data modelling is really dead ...

A data model is not just documentation, because it can be forward-engineered into a physical database. In short, data modelling solves one of the biggest challenges when adopting NoSQL technology: ...

Here is useful information on how to assess alternative data and combine it with so-called traditional data to improve credit risk modelling ...

Aon’s head of catastrophe insight explains the role data science plays in climate modelling and quantifying natural disaster risk.

New technologies are permitting larger scale and more quantitative studies of signalling networks. The large data sets that are obtained from these studies can be analysed by data-driven modelling ...

Quality data is accurate, complete, consistent, and relevant to the risk factors you're assessing. In risk modelling, quality data might include up-to-date credit scores, income information ...

The key to successful data modelling begins with asking the right questions. Many tenders state the need to understand how many inpatient beds will be required in the future. Yet it is essential to ...

Multilevel modelling is sometimes used for data from complex surveys involving multistage sampling, unequal sampling probabilities and stratification. We consider generalized linear mixed models and ...

Measuring the effectiveness of geological exploration and looking for trends that will lead to improved exploration success are key drivers for continued investment in the exploration industry.