Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
A year ago, I published an article about motivated reasoning and how that can damage the data analytics process. It is part of a blog series on cognitive biases and logical fallacies that data analysts should avoid. Today I’d like to extend this conversation into a topical matter: p-hacking, also known as data fishing.
Business Rules Management Systems (BRMS's) are the Swiss-army knives of business software. Despite this, very few companies we work with are getting the most out of their decision engines. In this blog, we explore how BRMSs are used across the customer lifecycle.
Data science continues to be a hot topic in many large firms globally. 2017 saw data science subjects such as R vs. Python, deep learning, natural language, gamification, AI and machine learning being arguably the most topical.
As 2017 draws to a close, we reflect back on the year of credit. Some of the key themes that featured this year for us included:
As a company passionate about innovation we are regularly evaluating and re-evaluating knowledge – whether it’s our collective own, an employee’s or a client’s. Knowledge is a funny thing. Common sense might suggest that the more one learns about a subject the more confident one becomes. However, this is not entirely true, at least not in the beginning. The Dunning-Kruger Effect The Dunning-Kruger effect (DKE) is a cognitive bias that has been known for some time, but was only formalised in 1999 by two Cornell psychologists. It involves the seemingly contradictory idea that often those with little knowledge on a subject may come across as exceedingly confident about the subject. Conversely those with more knowledge may be less confident.