Tamara Gruzbarg is a senior analytics and insights executive with 20 years of experience spanning diverse industries such as finance, consulting, Ecommerce, fashion retail and media. She previously led Data Analytics and Consumer Insights within Time Inc. Her team focused on harvesting a deep understanding of the consumer from both – subscriber and advertising standpoints. Prior to Time Inc, Tamara held leadership positions in consumer analytics for Stuart Weitzman and Gilt Groupe. She is passionate about the application of analytics across a wide range of business problems and is excited about the opportunities presented by big data. She is currently the Vice President of Strategic Services at Action IQ, an enterprise customer data platform. There she works with companies to help them make data-driven marketing a reality. Below she teaches us how to maximize our data to its fullest potential.
What is the difference between predictive modeling and machine learning?
Predictive modeling is a method used for predicting future outcomes by using data modeling.
Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
Machine learning as a concept allows you to scale the development of predictive models and form systems that can learn from data with minimal human intervention. That takes the pressure off one statistician building the model, implementing it, gauging the results, looking at the model's performance and adjusting the model based on the results. Instead, it allows machine learning systems to carry out that process automatically.
What is hypothesis testing, and how can it be used in a business context?
Hypothesis testing is a very useful tool once you understand it. It allows you to put a lot of ideas into the market and capitalize on opportunities with great confidence. Hypothesis testing starts with understanding your business's default action in the business context. The default action is what you're currently doing, what you've been doing, and what you're comfortable with it. Hypothesis testing continues by testing an alternative action that you believe could be better for your business.
For example, say you send your daily emails to your customer base at 10 am every day. That is your default action. You already know your average open rates for that send time. An alternative action could be sending your emails out at noon instead. Once you have sent emails at that send time for enough time, you will have the necessary data to compare open rates.
Hypothesis testing calculates statistics based on collected data and then decides if you want to stick to your default action or switch to the alternative. That's where statistical methods come into play. It's not enough to collect the data; you need to interpret it.
Hypothesis testing calculates statistics based on collected data and then decides if you want to stick to your default action or switch to the alternative.
How are the results of statistical methods interpreted?
When interpreting the results of statistical methods, essential terms to know are type one or type two errors. Type one error, sometimes called significance level, is when you stay with the default action. A type two error occurs when you go with the alternative action. By design, type one errors should be more painful than type two because switching your action always assumes some risk. If you decide to switch from your business's usual, you need to be very confident that your alternative action will produce better results.
What kind of support is needed to present data to business stakeholders in order for them to understand it, process it and make informed decisions?
Business intelligence support would be extremely beneficial when data needs to be presented to business stakeholders. In addition to Excel and SQL tools, there are a variety of business intelligence tools that help with this storytelling. The right tools can help analysts evangelize this information and get buy-in from business stakeholders.