Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
Credit lenders use data analytics to assess potential clients and determine affordability. However, many credit lenders and debt collection companies fail to apply the same practice when dealing with defaulting clients. In my first blog post, I'll cover the important role that data analytics can play in collections operations and solutions.
We've covered a few fundamentals and pitfalls of data analytics in our past blog posts. In this blog post, we focus on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive. Note: This blog post was published on the KDNuggets blog - Data Analytics and Machine Learning blog - in July 2017 and received the most reads and shares by their readers that month.
If I was to sum up our purpose at Principa, it would be “to help clients make informed decisions using data, analytics and software”. As information grows, so the opportunity to make better decisions increases. Data helps you understand your customer better. That’s our mantra. That’s our ethos. That’s why we are.
We take pride in our ability to predict - from the results of the 2015 Rugby World Cup and the 2016 Oscars to predicting profitable customers and customer churn. However, there is no denying that 2016 was a year full of shocking, unexpected events - from Brexit and the US election results to the acrimonious break-up of "Brangelina" (shocking!) and the sad loss of some very talented artists.