Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
The rise of Big Data, data science and predictive analytics to help solve real world problems is just an extension of science marching on. Science is humanity’s tool for better understanding the world. The tools that we use to build models, test hypotheses, look for trends to build value with our brand all derive directly from scientific principles. With these principles comes a myriad of obstacles. The obstacles are known to philosophers as “logical fallacies”, which I outlined in my previous post "The 7 Logical Fallacies to avoid in Data Analysis." In this blog post, we focus on the Texas Sharpshooter Fallacy and how to avoid it in your data analysis.
“Lies, damned lies and statistics” is the frequently quoted adage attributed to former British Prime Minister Benjamin Disraeli. The manipulation of data to fit a narrative is a very common occurrence from politics, economics to business and beyond.
Here's a great visual overview of what you need to get started with a data-driven customer loyalty programme: the questions to ask before getting started and an overview of all the possible data sources to consider.
If you’re involved in credit risk and existing customer marketing you’ll know that random numbers are frequently used when deploying different strategies. As strategies grow more complex and numerous, so the role of the random number grows more important. In this blog post, I’ll cover what randomisation is, why you should do it, when you should do it and what else to consider.