NiFi vs. Kafka… Or Is It?
With all of the exciting new tools to analyze and look at data, it's easy to get swept up and forget about a very important part of the process. Getting data to those tools! That…
What Makes Insights-Driven Organizations Different?
If you can’t stand the heat Summer is finally upon us. After an eternal winter and classic spring indecisiveness, temperatures have reached what some would consider “warm” here in the midwest. And that means it’s…
Flustered by Clustering?
Using Clustering for Customer Segmentation Clustering is a useful unsupervised method of machine learning, especially for identifying customer segments. Before, it was necessary to manually examine the qualities of your customers and make assumptions to…
Business Intelligence Tools: Starting with Power BI
With the continued growth of machine learning and artificial intelligence, there is a constant need for reports, dashboards, and other ways of visualizing data. Whether you are downloading CSV files or connecting to a complex…
Marketing and IT Sittin’ in a Tree
Marketing and IT Sittin' in a Tree F-I-N-D-I-N-G [great customer insights] The two worlds of IT and marketing are changing rapidly, both experiencing technological advances at never-before-seen rates. IT is tasked with orchestrating exponentially…
Machine Learning and Flip Phones
Machine Learning and Flip Phones Doing data analytics in Excel is like tweeting from your flip phone. It's time to move on. It was January 2007. I had just started my last semester of high…
TensorFlow: Introduction & Effective Implementation
In this post I will discuss some important points of TensorFlow; namely what is it, how it works, and how easily it can be used in production environments with Keras and eager processing. Enterprises that…
Navigating Machine Learning Obstacles
“Machine learning” has been a buzzword for a while now, and studies continue to affirm the substance behind the hype. Even the most traditional of businesses are now rushing to adopt machine learning into their…
Eating the Elephant: Putting it All Together
We’ve seen how what has traditionally been thought of as “big data” has evolved to encompass techniques and technologies that can benefit any company and prepare them for the future. What could an example project look like?