Category: Analytics

The use of analytics in the marketing, development or deployment of social games

What is machine learning and why it is crucially important

A few weeks ago, I posed a question on Quora about the differences between machine learning and predictive analytics. I was surprised at the number of people who started following the question and actually liked it (although it sounded rhetorical, I was hoping to understand machine learning better). A recent article in Fast Company about The New York Times, of all companies, did a great job of explaining the fundamental value of machine learning.

My interest in machine learning was ignited recently as it has become the hot buzzword in the Bay Area; some have argued if you add machine learning to your PowerPoint you can add a zero to the end your company’s valuation. While that claim is obviously an exaggeration, investors are among the savviest businessmen, so their interest in machine learning shows it is a crucial emerging space.

The article discusses Chris Wiggins, a biologist The New York Times just hired as its Chief Data Scientist. Wiggins’ mandate is to build and lead the Times’ machine learning team. In Fast Company’s interview with Wiggins, it became clear exactly what machine learning is, how it is different than predictive analytics and why it is important.

What is machine learning

According to Wiggins, “Machine learning sits at the intersection of data engineering and mathematical modeling. The thing that makes it different from statistics traditionally, is far more focus on building algorithms.”

University of Utah image for machine learning

Also, while statistics is traditionally focused on explaining data, machine learning is geared to building predictive models. When Netflix or Amazon make product recommendations to you, they are using machine learning to predict what you would be interesting in experiencing. Continue reading “What is machine learning and why it is crucially important”

Bayes’ Theorem Part 2: How to use Bayes’ Rule when you have multiple prior data points

Last week I started my series of posts about Bayes’ Rule and why it was the foundation of good business decision making . In the next few weeks I will hit on some fun applications but today wanted to build further the foundation for using effectively Bayes’ Rule by discussing continuous parameter values (or multiple existing data points). Again, I borrow heavily from easily the best work on Bayes’ Rule, James Stone’s book Bayes Rule: A Tutorial Introduction to Bayesian Analsysis. This application will be particularly relevant when using Bayes’ Theorem to make the best decisions in your green light process, corporate development, investing or anywhere there are multiple historical results to examine.

Multiple data points are referred to as “continuous variables.” The values of a continuous variable are like densely packed points on a line, where each point corresponds to the value of a real number. The main advantage of working with continuous values is that we can usually describe a probability distribution with an equation, and because an equation is defined in terms of a few key parameters, it is said to provide a parametric description of a probability distribution.

To make the above relevant to you, think of yourself as a VC. You are looking at a potential investment. You start by looking at how similar investments over the last two years performed; this return on investment represents the points on the line. The parameters could be the space the business occupied, management team and level of investment. Rather than potential investments, to keep the analysis simple I will use a coin flip as an example. Continue reading “Bayes’ Theorem Part 2: How to use Bayes’ Rule when you have multiple prior data points”

Analytics 3.0

As many know, I believe end-of-year predictions have zero value and I prefer to look at important trends that are already unfolding and will impact readers next year. The most important trend right now for people in the social media and gaming spaces, as well as almost anyone in the tech space, is the evolution of analytics. Thomas Davenport, author of the seminal work Competing on Analytics, recently wrote an article in the Harvard Business Review about Analytics 3.0. Just as Analytics 2.0 transformed the gaming space, allowing companies like Zynga, Playfish and Disney to leap over established competitors, Analytics 3.0 can reshape as dramatically the gaming ecosystem. Analytics 3.0 is a new resolve to apply powerful data gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products, services and games that customers buy.

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A brief history of analytics

To understand best the impact of Analytics 3.0, it is helpful to understand 1.0 and 2.0 and their impact. Analytics 1.0 ushered in an objective, deep understanding of important business phenomena and gave managers and leaders the fact-based comprehension to go beyond intuition when making decisions. Data about sales, customer interactions, production processes, etc., were recorded, aggregated and analyzed. For the first time, analytics were used to compete by creating greater efficiency: making better decisions on key issues to improve performance. Continue reading “Analytics 3.0”