Machine learning can drive business outcomes

Source: Pixabay

In our final blog, our data explorer is diving deep under the surface to discover an unfamiliar world for many humans—machine learning. Our previous blogs focused on business analytics (familiar, much like the earth’s surface) and data science (less familiar but making up a much larger portion and providing structure like the earth’s mantle). Machine learning is like the earth’s inner core—sometimes mysterious but teeming with possibility.

What is machine learning

The earth’s core can be a bit of a mystery. Unlike the solid ground we see at the crust, it’s constantly moving, and no one has directly observed it. Instead, scientists use other indirect observation tools to understand what lies beneath the surface. We may not know exactly what is happening at the core, but we see its influence on the earth’s crust and mantle.

In the same way, machine learning can also be a bit mysterious. Machine learning is a subset of data science teaching machines to process information like humans do. These complex algorithms enable machines to speak normally, or “learn” how to make better decisions as they encounter new information. The process machines use to learn isn’t always directly observable, but their insights and outcomes are.

The idea is that given the right data, these algorithms can improve their accuracy over time, becoming more efficient and more integral to business operations. It’s an integral part of the data science field, and companies are quickly putting these complex algorithms to use in business settings. Once data scientists set up machine learning protocols, business users can leverage these pipelines to predict trends and outcomes in the business world without knowing how it all works.

Machine learning automates many business processes

Source: Pixabay

How do companies use machine learning?

Companies use machine learning to automate repetitive tasks like replying to simple customer service questions, checking and scheduling predictive maintenance on machinery, or even flagging legal contracts for possible inconsistencies. It can operate much like another team or community member for the business community, supporting everyone’s tasks through these automations.

In fact, McKinsey predicted back in 2016 that companies can automate much of their operations and save money in the long run, and the potential only grows. When machine learning does the heavy lifting for mundane day-to-day tasks (and gets better over time as it learns), it leaves humans to do what they do best: problem solve, get creative, and act on insights.

Some common machine learning tasks are:

Recommendation engines: Netflix keeps you clicking through its movie catalog by recommending new things to watch based on your previous choices. The more you choose, the better the recommendations get.

Speech recognition: Siri can recognize your requests using natural language processing. NLP teaches machines to understand language, and the more you interact with Siri, the better she understands you.

Computer vision: Machines can also learn to “see” and understand information gleaned from images and video. For example, Facebook algorithms can identify faces in photos to help with automatic tagging or algorithms at YouTube can flag video content that goes against community guidelines.

Predictive maintenance: Machines can use sensor data to scan everything from factory machinery to subway lines. Using this information, machine learning can predict failures before they happen and schedule maintenance during the least intrusive time periods. The more they do this successfully, the better their predictions get.

How does machine learning work?

There are three main components of the machine learning process.

  • Decision process: First, a user asks a question. An algorithm takes data input and finds the pattern to make predictions or “decisions” to answer that question.
  • Error Function: This component assesses the decision or performance of the algorithm, often by comparing the outcome to known examples of the problem.
  • Model optimization: Based on the original outcome and the error function, the model optimizes itself to reduce inaccuracies, essentially “learning” as it goes.

Companies can utilize different types of machine learning to accomplish this task:

  • Supervised learning: Using labeled datasets (i.e., datasets prepared beforehand by humans with organized information) to make decisions. This type of learning requires human intervention.
  • Unsupervised learning: Using unlabeled datasets (i.e., datasets that have not been prepared by humans beforehand) to make decisions by categorizing and linking information and then finding the pattern. This type of learning happens entirely independently of humans.
  • Semi-supervised learning: Using a blend of human and machine, the model trains using a small subset of human-prepared data to categorize the rest of the training sets. From this initial human nudge, the machine finds patterns.
  • Reinforcement learning: Instead of beginning with sample training data, the machine uses trial and error to “learn.” As the machine creates successful outcomes, it can develop the best policy to make decisions with future input.

Machine learning versus deep learning

Both are related to the original process of teaching algorithms to think and make decisions like humans, but their approach is different. Like the earth’s core, we know and understand their operations based on indirect observation.

Machine learning typically requires more significant human intervention—think labeled datasets and error correction. The process is often more transparent because humans set up the original conditions for decision-making. It’s less processing intensive and better for simpler or short-term problems.

Deep learning works through unsupervised learning, taking in large amounts of unprepared data to find patterns and optimize decision-making independent of human intervention. While it can solve much more complex problems, it is processing intensive and often a mystery for how it reaches conclusions.

Using machine learning for business outcomes

While we may never directly observe the earth’s core, we can make logical assumptions based on what we observe on the earth’s surface. Machine learning is a sometimes hidden, constantly moving, and improving system that leverages the power of machine processing to find hidden patterns.

Businesses leveraging machine learning can better predict outcomes to serve customers, reduce risk, and mitigate costs. It cannot replace human team members, but it can help everyone perform their jobs to greater success without extra time or effort. Our data explorer has made it through all three layers of the data environment and is ready to put everything into action.

Subscribe to our newsletter to get more tips in your digital transformation journey!

Follow us on LinkedIn.