According to the International Institute for Analytics, businesses that use data will gain $430 billion in pro

It seems that every week, AI technology has learned to do something humans do, but faster and better.

Driving a strong business starts with the people who work for your business, and those people align with the business through the company culture. How are you shaping yours?

Just as baby boomers on the manufacturing plant floor are getting ready to retire, so are the owners.

Technology is supposed to help us, but sometimes it feels like for every step forward, we take two steps back. Like many people (and despite my resistance), my family has accumulated a few internet of things (IoT) devices in our home.

Imagine that your boss Ethan calls you into his office. He expresses disappointment in your recent performance and lack of commitment. How would you react? Accept the feedback and put in more effort? Would you pout in your office and start looking for a new job?

Technology companies are frequently driven by their engineering processes. Of course product quality is regarded as most important, and that quality can be tested and measured with numbers and data.

What tools should data analysts have in their toolbox? It’s a broad question and one with diverging views. So, I am delighted to welcome back a guest blogger who doesn’t shy away from controversy.

In the past two months we have looked at how three-sigma limits work with skewed data.

If you remember the woodworking, metalworking, and auto shop classes that used to be taught in high school, you already have an idea of what a makerspace is.