This is the first of a series of blogs where Data Scientists Cory Everington and Anna Godwin will discuss five Analytics Best Practices that are key to building a data-driven culture and delivering value from analytics.
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August 11, 2017
In his Top 10 Data Mining Mistakes John Elder shares lessons learned from more than 20 years of data science consulting experience. Avoiding these mistakes are cornerstones to any successful analytics project. In this blog about Mistake #4 you will learn that inducing models from data has the virtue of looking at the data afresh, not constrained by old hypotheses. But, while “letting the data speak”, you must be careful not to tune out received wisdom, because often, nothing inside the data will protect one from significant, but wrong, conclusions.
August 4, 2017
Machine learning has many strengths. Predictive models can synthesize information from millions of disparate cases and identify patterns that would otherwise pass undetected. These patterns lead to inferred insights from the data that can surpass human judgment. The potential value from predicting human behaviors before they happen is exciting to businesses and government agencies. Imagine having the foresight to know which of your customers are likely to churn, which of your providers have a high likelihood of making fraudulent claims, or which of your patients are grateful enough to donate to your hospital foundation?
Renowned author and founder of the Predictive Analytics World conference series Eric Siegel shares six key definitions—and The Five Effects of Prediction—from his book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.
This is the first in a series of short blog posts where we explore common varieties of bias that can beset analytics projects. Bias has serious ramifications for the success of analytics in any organization. Understanding the nature of bias is crucial for understanding the extent of a model’s accuracy. In this first post, we discuss what bias is, why it occurs, and why it matters (a lot).
July 14, 2017
I’ve lived through this phenomenon first hand. The environment was new to me, sitting at my assigned seat at the cherry wood conference table for the weekly executive staff meeting. I was told very clearly that I was to stick to the presentation, answer only when asked a direct question, and never, no matter what happens, ask why! After being ushered out of the meeting when I finished, we quickly huddled for a post-meeting debrief. Everyone started asking “How do you think it went?” “What do you think he meant when he said this?” and “Did you understand what he asked us to do?” I finally asked, “Why didn’t we just ask him?”
July 7, 2017
There is growing literature around interpretable machine learning and explaining black box outputs to humans who will make real decisions based on the results. Predictive model interpretability is a nuanced and complex subject. For example, AlphaGO, the experimental deep learning solution created by Google to play the ancient board game Go, made headlines recently for defeating a Go grandmaster for the first time. This was a significant milestone for a machine learning system since Go is significantly more complex than chess. When Go masters took an interest in AlphaGO’s winning strategy, the program’s creators faced a familiar question: Why did it choose certain moves?
June 30, 2017
Data science and predictive analytics’ explosive popularity promises meteoric value, but a common misapplication readily backfires. The number crunching only delivers if a fundamental – yet often omitted – fail-safe is applied.
June 23, 2017
There are two problems with humans making decisions from data. We are biased— even experts are just as likely to give inconsistent judgments—and we don’t always understand, or trust, the model. Although decision-makers could benefit from using data as a part of their decision making, raw machine learning results may not be meaningful enough. So how can we use data in a way that experts trust without diluting the machine learning process?
June 16, 2017
It’s like an irritating fly buzzing around your head – “Big Data”. Do I have to hear the term one more time? As a Data Scientist who interacts with hard data problems on a daily basis, the term, “Big Data”, has little meaning to me since it lacks any precise definition or structure, the normal comfort food for my mathematical mind. However, I appreciate that the term is “out there” and people who are trying to make sense of the mess of data flowing towards them are searching for a common language to discuss their specific challenges. I hope to help by explaining some of the key language circulating around Big Data concepts – the next level of detail in the Big Data discussion.