machine learning
Smart Data Series: An Introduction to Machine Learning
Leo Farias
Posted on: August 25, 2016
Tags: big data data science machine learning smart data
Tags: big data data science machine learning smart data

To conclude our Smart Data Series, we are going to talk about machine learning. If you missed any of the topics in this series, you can visit them below:

Machine learning has turned the business software market upside down. Instead of users telling business software what to do, the software now tells the user what to do. Those who don’t quickly adopt this smarter, more predictive software risk falling behind.

What Is Machine Learning?

Traditional software strictly follows a set sequence. While outcomes might vary based on the input, the trigger for each outcome is preset within the code. Given the exact same inputs, the outcome will always be the same unless a human changes the code. Machine learning, on the other hand, automatically adapts its program to reach the desired outcome.

Consider the example of spam filtering. A traditional filter is programmed to look for specific keywords, email addresses or patterns. If it isn’t achieving the desired results, the programmer would need to manually adjust the filter.

A machine-learning spam filter might start with those initial parameters, but it would continually adapt. When a user flags a spam email that got through the filter or marks a flagged email as “not spam,” the machine-learning filter compares the characteristics of those emails and adjusts its parameters to try to get similar emails in the right place without user intervention.

While both a human and a machine can theoretically reach the same results, the machine can process large quantities of data more quickly and implement immediate changes without the need to wait for a software update. Additionally, while hard-coded software is generally one size fits all, machine learning can adapt to the preferences and habits of individual users.

Machine Learning and Data Science

Machine learning derives from data science, the practice of extracting knowledge from large sets of data. These large data sets can be used to train machine learning algorithms. In turn, machine learning has widened the possible range of applications of data science, as it increases the range of situations in which a machine can be used to draw conclusions from data. Algorithms can often analyze data much more quickly than even the most highly skilled human, speeding up many data analysis tasks.

Business Applications of Machine Learning

While the big data trend goes back decades, machine learning opens data analysis into new areas. Instead of big corporations having to make budget decisions about where to allocate teams of analysts and small companies being left out of the game, machine learning can be integrated into virtually any piece of software. Here are just a few examples of applications.

Inventory and Pricing

Retail stores and manufacturers are faced with constant decisions about how much inventory to order and when to reduce prices to avoid unsold inventory. Infinite factors can influence demand, including what nearby competitors do, the local economy, weather and demand for complementary items.

While pricing and inventory analysts have long built models to analyze these trends, machine learning can use past data to calculate infinite scenarios and build more accurate predictions.

Quality Control

A business’s reputation can be tarnished long before customer complaints reach its executives. Airbnb found this out after reports of discriminatory actions by its hosts surfaced.

Machine learning can look for small patterns and red flags that could indicate potential problems before the customer is even impacted. This allows companies to maintain high levels of quality and proactively deal with any problems that do arise.

Fraud Detection

Machine learning can also turn areas like consumer or employee fraud into issues that can be dealt with proactively rather than reactively. Transactions that fall outside of typical patterns can be flagged for manual review.

If the transaction turns out to be fraudulent, greater emphasis is placed on similar patterns in the future. If there was a non-fraud explanation, the software can adapt to avoid future false alarms without ignoring real frauds.

As machine learning continues to grow, the question will shift even more clearly from “How can you afford machine learning” to “How can you afford not to use machine learning?”

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Leo Farias is the CEO and Co-founder at Concepta. He received his MPS in Business of Art & Design from the Maryland Institute College of Art. With over 18 years of technology-focused experience, he plays a vital role in architecting and leading various mission-critical projects for world-renowned clients like Time Warner Music, Orlando City Soccer, Vasco de Gama and Corinthians Soccer Club.