Machine Learning vs. Traditional Programming

insightsoftware -
February 15, 2023

insightsoftware is a global provider of reporting, analytics, and performance management solutions, empowering organizations to unlock business data and transform the way finance and data teams operate.

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Traditional computer programming has been around for more than a century, with the first known computer program dating back to the mid 1800s. Traditional Programming refers to any manually created program that uses input data and runs on a computer to produce the output.

But for decades now, an advanced type of programming has revolutionized business, particularly in the areas of intelligence and embedded analytics. In Machine Learning programming, also known as augmented analytics, the input data and output are fed to an algorithm to create a program. This yields powerful insights that can be used to predict future outcomes.

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Here’s a closer comparison of traditional programming versus machine learning:

Traditional Programming

Traditional programming is a manual process—meaning a person (programmer) creates the program. But without anyone programming the logic, one has to manually formulate or code rules.

In machine learning, on the other hand, the algorithm automatically formulates the rules from the data.

Machine Learning Programming

Unlike traditional programming, machine learning is an automated process. It can increase the value of your embedded analytics in many areas, including data prep, natural language interfaces, automatic outlier detection, recommendations, and causality and significance detection. All of these features help speed user insights and reduce decision bias.

For example, if you feed in customer demographics and transactions as input data and use historical customer churn rates as your output data, the algorithm will formulate a program that can predict if a customer will churn or not. That program is called a predictive model.

You can use this model to predict business outcomes in any situation where you have input and historical output data:

  1. Identify the business question you would like to ask.
  2. Identify the historical input.
  3. Identify the historically observed output (i.e., data samples for when the condition is true and for when it’s false).

For instance, if you want to predict who will pay the bills late, identify the input (customer demographics, bills) and the output (pay late or not), and let the machine learning use this data to create your model.

As you can see, machine learning can turn your business data into a financial asset. You can point the algorithm at your data so it can learn powerful rules that can be used to predict future outcomes. It’s no wonder predictive analytics is now the number one capability on product roadmaps.

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