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
To remain competitive, traditional banks must be able to make intelligent decisions on how best to serve their customers – and the crux of intelligent decision-making is quality data.
For someone that’s been preaching data governance and data quality for more than fifteen years, its been fascinating to see how these two topics have been gaining traction in the last few years. A few week’s back I touched on the difference between data governance and data quality – governance is about “what” and “who”,…
It may sound counter-intuitive but the “what” of metadata is probably the least important thing. Why do I say that? We, let’s start by looking at what metadata is. Metadata is information about other data that gives it context. Two common examples are the data dictionary and the business glossary. The “what” of the data…
But to business, having customer data consolidated into a master data management application often leaves a bad taste.
What makes the difference, for any AI project, is the quality and quantity of data that is available to feed the model.
