Accuracy and Trust in Machine Learning

A few weeks ago, I wrote about machine learning risks where I described four ‘buckets’ of risk that needed to be understood and mitigated when you have machine learning initiatives.  One major risk that I *should* have mentioned explicitly is the risk of accuracy and trust in machine learning.  While I tend to throw this […]

Machine learning risks are real. Do you know what they are? 

Machine Learning Risks are real and can be very dangerous if not managed / mitigated. Everyone wants to ‘do’ machine learning and lots of people are talking about it, blogging about it and selling services and products to help with it. I get it…machine learning can bring a lot of value to an organization – […]

What is the cost of bad data?

How much is bad data costing you? It could be very little – or it could be a great deal. In this article I give an example of what the cost of bad data really is. A few days ago, I received a nice, well designed sales/marketing piece in the mail yesterday. In it, a […]

Beware the Models

“But….all of our models have accuracies above 90%…our system should be working perfectly!” Those were the words spoken by the CEO of a mid-sized manufacturing company. These comments were made during a conversation about their various forecasting models and the poor performance of those models. This CEO had spent about a million dollars over the […]

Big Data Roadmap – A roadmap for success with big data

I’m regularly asked about how to get started with big data. My response is always the same: I give them my big data roadmap for success.  Most organizations want to jump in a do something ‘cool’ with big data. They want to do a project that brings in new revenue or adds some new / […]

Are your machine learning models good enough?

Imagine you’re the CEO of XYZ Widget company.  Your Chief Marketing Officer (CMO),  Chief Data Officer (CDO) and Chief Operations Officer (COO) just finished their quarterly presentations and were highlighting the success from the various machine learning projects that have been in the works. After the presentations were complete, you begin to wonder – ‘are […]

Deep learning – when should it be used?

“When should I use deep learning?” I get asked that question constantly. The answer to this question is both complicated and simplistic at the same time. The answer I usually give us something along the lines of ‘if you a lot of data and an interesting / challenging problem, then you should try out deep […]

When it comes to big data, think these three words: analyze; contextualize; internalize

If you don’t know, I’m a bit of a data nerd.  I’ve been writing about big data, data science, machine learning and other ‘new’ stuff for years.  I believe in data science and I believe in big data. I’m a fan of machine learning (but think you probably don’t need it) for the majority of […]

Data and Culture go hand in hand

A few weeks ago, I spent an afternoon talking to the CEO of a mid-sized services company.  He’s interested in ‘big data’ and is interviewing consultants / companies to help his organization ‘take advantage of their data’.  In preparation for this meeting, I had spent the previous weeks talking to various managers throughout the company […]

The Data Way

The world has become a world of data. According to Domo, the majority of the data (roughly 90% of it) that exists today has been created within the last two years. That’s a lot of data. Actually…that’s a LOT of data. And it’s your job to use that data to make better decisions and guide […]