What is “Machine Learning” and Why Should I Care? A Gentle Introduction for Non-Technical Professionals (Part 1)
Let’s be honest. Many people are tired of hearing about big data, analytics, and all of the other stuff coming along with our massive (but massively exciting) explosion of data. But if your excitement for the expanding reach of data science is tempered by hyperventilating media coverage, consider that roughly 90% of all data in the world was produced in the last two years alone. You can be sure someone is trying to something with all of those 0s and 1s….and that brings us to machine learning.
What IS Machine Learning?
Machine learning just means getting a computer to learn from data, identify relationships, and make predictions. In essence, only two things are predicted: a number or a category. Applied to HR, one might wish to predict the number of work days missed (number) or whether someone will quite his/her job in the next year (category). In marketing, one might wish to predict the number of targeted viewers that visit an advertised website (number) or whether someone will make a purchase (categorical prediction).
This is a rich topic that will be discussed in greater depth in later posts, but just being aware of the number-category distinction is enough for now.
What is the Power of Machine Learning?
The true power of machine learning is that the learned relationships in the data that provide for accurate predictions are never explicitly programmed. Data scientists provide the algorithms (read: recipes) that determine HOW these relationships are learned but NOT WHAT is learned.
This creates massive value because many of the core algorithms that work in one domain also work for someone else in a completely unrelated area. For example, the basic principles underlying a machine learning algorithm in facial recognition software can also be used in human capital analytics to predict who is likely to leave within the next year.
The upshot is that HR and numerous other previously non-technical business domains directly benefit from machine learning methods and developments from every corner of business and research. Like our native human intelligence, the deep power of these machine learning rests on generalizability across contexts, not narrowly defined skills.
Machine Learning: For the People, By the People
Whither the people? For all of the broad potential of machine learning, the ultimate quality and accuracy of specific machine learning solutions still often depends on both a data scientist’s individual knowledge and the incorporation of subject matter expertise. Machine learning applications to real world human capital issues such as recruitment, retention, and development are most effective when they combine raw computing power, technical acumen, expert knowledge, and creative intuition. This integration can yield insights that beat the power of any one of those elements in isolation.
Coming Up Next…
In our next installment of this series, we dive a little deeper into some specific use cases. The spoiler (well, not quite) is that sometimes we are willing sacrifice the accuracy of our predictions to increase the ease of interpretation and our ability to take directed, data-informed action.
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