We came to learn from a master. Instead we learned that Hadley is just like us. He makes mistakes. A lot of them. And it made all of us laugh. Not because Hadley was the fool, but rather because we all saw him fight through the same typos, errors, conflicts, and sometimes inexplicable bugs that we all face, every day.Read More
Machine Learning. Neural Networks. Hierarchical Clustering.
Does anyone know what these terms really mean? Sure, a handful of nerds know. Like the roughly 50,000 ranked Kaggle members.
But these esoteric terms only scratch the surface of what the world’s data scientists have come up with to describe, promote, and ultimately obfuscate their day to day jobs.Read More
Sherlock Holmes, Donald Trump, and the Data Science Paradox
Yes, “deducive” is a real word. And it’s the name we chose for our company. We chose it for its rational correlation to logic and the scientific method, as well as its emotional connection to a great fictional sleuth. But, in data science terminology, it may have been a bad choice.Read More
Combining Empathy and Data Mining for Better UX
Empathy has rightfully become predicate to good user experience (UX) design. But the means designers use to achieve empathy — such as Personas — fall short of their intended purpose.Read More
Marketing momentum builds over time and brings many benefits —but it also leads to bloated process and wasted budget built on old assumptions. Basic data science techniques offer a solution.Read More
Successful SME marketers thoroughly understand what it takes to compete: a strong website, content strategy, CRM, and multichannel engagement plan. But as all the tactics stack up, so does the data. It’s harder and harder to know what works and which steps in the customer journey are most important.
This is not a new problem, but it is one that large enterprises have largely solved by investing in big data strategy and analytics platforms.