Machine-learning is getting better at recognition and categorization by leaps and bounds, for sure. But do they understand the purpose?
Stephen H. Yu
The main job of a modern data scientist is to answer business questions for decision-makers. To do that, they have to be translators
Too many marketers are personally annoying their customers in the name of personalization. For that reason alone, I am looking for an
You get to hear “actionable insights” whenever analytics or roles of data scientists are discussed. It may reach the level of a
When faced with a large amount of unrefined, unstructured and uncategorized data, we must indeed fix the data first. Let’s not even
What do you think that ERA (Earned Run Average) stands for? If you can paint the quality of a baseball pitcher with a bunch of
I have been writing about using “model-based” personas stemming from a 360-degree
In the age of constant bombardment with marketing messages, staying relevant to prospects and customer is not just good practice in the
One-dimensional techies will be replaced by machines in the near future. So what if they’re the smartest ones in the room? If decision-
Let’s talk about why buzzwords are bad for the data and analytics business. I don’t entirely deny that there are some benefits of
The thing about predictive analytics is that the quality of a prediction is eventually exposed — clearly cut as right or wrong.
There are data geeks and there are data scientists. Then there are data plumbers, and there are total posers. In this modern world
Like any resource like water, data may be locked in wrong places or in inadequate forms. We hear about all kinds of doomsday scenarios
Recently, I participated in a panel discussion at a major e-commerce conference. The topic was about the “Future of Marketing,” and
The value of data does not depend on size or shape of them. It really depends on how useful data are for decision-making. Some data