More and more businesses are switching from reacting to situations to anticipating them. How do they do this?
In order to anticipate or predict a situation, an organization must have a business question in mind. Questions such as:
Is this a fraudulent transaction or not?
Will the stock price in the next week go up or not?
Is this particular person a credit risk or not?
Will this customer buy item ‘A’ or not?
How many units will be sold next week?
What will the stock price be tomorrow? etc
The best way to make business decisions is to analyse the relevant historical data and to identify the patterns in the data. To identify the relevant data, it is important that the analyst speaks with the domain experts to help them identify the relevant data. Further, it is not only the analysis of the historical data (being reactive always) that can help answer the above business questions. It is also important that businesses use predictive data (being proactive and sometimes preventing negative activity) to help answer the above questions.
Machine Learning is a method for data analysis, in particular, Machine Learning Algorithms focus on using the relevant data inputs to make Predictions for the business or Classify the business products, services or customers.
Machine Learning is an algorithm that explores the data for patterns and identifies the key data (factors) that will help the business to predict/classify its target outcome of interest (like the above business questions). Machine Learning uses statistical techniques (such as the logistic regression, neural networks, decision tress, etc) and computer programming languages (such as Python, R, SAS, Matlab, etc) to automate the predictive/classification models using algorithms that iteratively learn from the data and find the hidden insights that humans would not usually have known about. These hidden insights are like ‘gold for the business’ as they usually improve the business predictions/classifications without being explicitly programmed where to look. The Machine Learning Algorithms further improve when they are regularly updated with new data and knowledge.
For Machine Learning Algorithms to be effective, it is important for businesses to be able to capture the relevant, good quality data in real time or almost real time and to allow the right people in the business to apply the predictions/classifications timely and thereby gain advantage over their competitors.
Are you interested in learning more about machine learning algorithms and how your business can improve its sales/predictions and become ore efficient? Contact me at firstname.lastname@example.org