New questions on ethical implications, data privacy, or public safety are studied seemingly daily.
How businesses manage the knowledge gap between decision makers and data specialists could be the difference between boom and bust
data quality, hyperscale and Python – the foundation of machine learning
A few week’s back I wrote about the data translator – a role on the data science team that bridges the gap between the data scientist and the business. That got me thinking – what are the characteristics (that may be less obvious) that make for a good data analyst? 1. Intellectual curiosity and focus…
Poor data quality is the single biggest contributor to the poor performance of customer risk-rating models. Incorrect know-your-customer (KYC) information, missing information on company suppliers, and erroneous business descriptions impair the effectiveness of screening tools and needlessly raise the workload of investigation teams. In many institutions, over half the cases reviewed have been labeled high risk simply due to poor data quality – McKinsey
Big Data has become a buzzword in recent years as businesses have discovered new ways to collect valuable information about their customers and processes. The advent of mobile tech coupled with the Internet of Things (IoT) has given companies new ways to collect data, while machine learning has given analysts the tools needed to discern…
The data translator bridges the gap between the data scientist and the business