Predictive analytics is the process of using data mining, statistics, and modeling to identify the likelihood of future outcomes based on historical events. Predictive analytics is essentially the science of learning from available data in order to accurately predict future events. The goal of predictive analytics is to minimize risk and uncertainty when faced with making decisions about the future.

Predictive analytics:

  • Organizations are increasingly reliant upon big data for everything from sales forecasting to marketing optimization, but because today’s omnipresent information was not necessarily created with a clear business outcome in mind it can often present more noise than signal. In addition, some data may be relevant only for its historical value, e.g., how many cars Tesla sold between 2013-2014 will not impact their ability or inability to sell cars in 2025. This historical information, while interesting to Tesla fans, does not contribute anything actionable to an organization trying to predict future sales for their manufacturing capabilities which are expected to increase dramatically in the next decade.
  • Predictive analytics is focused specifically on identifying relevant patterns and relationships that can be used for predicting future outcomes. Data mining uses machine learning algorithms like decision trees, neural networks, association rules, clustering methods and regression analysis to identify these relationships.
  • Businesses must be careful when using predictive analytics because without proper identification of biases within the underlying data or accurate inputs for statistical models it becomes very easy to make incorrect predictions (i.e., false-positive) or miss important results (i.e., false-negative). For example, it is very simple to create a model to identify an individual from one of their tweets by using machine learning algorithms. But what happens when you begin making decisions about this individual based on the data in their tweet? In most cases, these predictions will not be accurate because there are many factors not included in the analysis that can significantly impact behavior e.g., somebody who was recently dumped by their significant other may tweet a lot about how much they miss them and yet never actually contact them again.
  • In order for predictive analytics to be effective, input data must be both correct and relevant. If either element is incorrect or missing then predictions will inevitably be wrong. You can’t trust your Netflix recommendations because they know what movies you like but not what you might like and who you are and yet they were only three CDs off in predicting your entire music library.
  • While predictive analytics can be a powerful tool for organizations, it is still just that – a tool. All tools are useful when applied correctly, but one must always remember that the true power lies within individuals being able to use their own experience and knowledge to determine how best to apply this technology to any given situation. If everyone blindly trusts predictive analytics then organizations will eventually lose all of their competitive advantages because everybody else will have access to the same data.
  • The key for effective business decision-making includes using both human intuition and machine prediction by allowing people with creativity and strategic vision to combine an understanding of available information while leveraging highly predictive analytics tools.
  • In today’s hyper-connected world there is more data available to organizations than ever before, but not all of it is relevant and even the most advanced predictive model will only be as effective as the input data used to create it. Businesses integrating predictive analytics solutions need a proper understanding of both their internal strengths and weaknesses along with an ability to identify potential gaps in the information they are using for this analysis while remaining constantly open to new possibilities that may present themselves. This type of integrative thinking can allow companies to quickly move past inaccurate predictions based on irrelevant information before somebody else beats them to market with something better because proactive decision-making requires the willingness to constantly reevaluate what you know while remaining comfortable making educated guesses about what you don’t.


How did you get started in predictive analytics?

I was originally motivated to become involved with predictive analytics because of my curiosity for how things work and the desire to be able to tell what is going on based on what I see. This allows me to understand future implications that can arise from current events such as market trends, product performance and even human behavior.

What types of data do you find most interesting?

When it comes to analyzing truly interesting datasets, I am not sure that anyone type is more interesting than another. Instead, it all depends on understanding what makes each individual dataset unique in order to discover its potential value when used correctly. One thing I have discovered about myself over the years is I tend to enjoy using datasets where the results are easy to understand and measure which allows for a more practical approach to data analysis.

How do you use predictive analytics in your own life?

As far as using predictive analytics in my personal life, I actually don’t since I am not really a “consumer” of media or any other product types that would benefit from this technology. The only thing remotely close is my Netflix account but the success rate on their recommendations is so off base it’s almost humorous – who knew they would recommend so many ’50 Shades of Grey’ type movies when I was looking for something fun and exciting!


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