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Data Processing

Analytics vs Machine Learning

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APIs

Data Analytics vs. Machine Learning

With all the hype around machine learning, many organizations are asking if there should be machine learning applications in their business somehow.

In the vast majority of cases, the answer is a resounding no.

As you learned a few chapters ago, one of the major benefits of the cloud is that it enables you to leverage virtually infinite storage and processing power to gain critical insights from the data your sensors/devices will be collecting. Both data analytics and machine learning can be powerful tools in doing so, but there’s often confusion on what they actually mean and when is best to use one or the other.

Later we’ll explore the value of machine learning in greater depth, but at a high level, machine learning takes large amounts of data and generates useful insights that help the organization. That could mean improving processes, cutting costs, creating a better experience for the customer, or opening up new business models.

However, most organizations can get many of these benefits from traditional data analytics, without the need for more complicated machine learning applications.

Traditional data analysis is great at explaining data. You can generate reports or models of what happened in the past or of what’s happening today, drawing useful insights to apply to the organization.

Data analytics can help quantify and track goals, enable smarter decision making, and then provide the means for measuring success over time.

So When Is Machine Learning Valuable?

The data models that are typical of traditional data analytics are often static and of limited use in addressing fast-changing and unstructured data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.

While traditional data analysis would need a model built on past data and expert opinion to establish a relationship between the variables, machine learning starts with the outcome variables (e.g. saving energy) and then automatically looks for predictor variables and their interactions.

In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision. So you give the machine learning algorithm the goal(s) and then it “learns” from the data which factors are important in achieving that goal.

A great example is Google’s application of machine learning to its data centers last year. Data centers need to remain cool, so they require vast amounts of energy for their cooling systems to function properly. This represents a significant cost to Google, so the goal was to increase efficiency with machine learning.

With 120 variables affecting the cooling system (i.e. fans, pumps, speeds, windows, etc.), building a model with classic approaches would be a huge undertaking. Instead, Google applied machine learning and cut its overall energy consumption by 15%. That represents hundreds of millions of dollars in savings for Google in the coming years.

In addition, machine learning is also valuable for accurately predicting future events. Whereas the data models built using traditional data analytics are static, machine learning algorithms constantly improve over time as more data is captured and assimilated. This means that the machine learning algorithm can make predictions, see what actually happens, compare against its predictions, then adjust to become more accurate.

The predictive analytics made possible by machine learning are hugely valuable for many IoT applications. Let’s take a look at a few concrete examples:

Machine Learning Applications in IoT

Cost Savings in Industrial Applications

Predictive capabilities are extremely useful in an industrial setting. By drawing data from multiple sensors in or on machines, machine learning algorithms can “learn” what’s typical for the machine and then detect when something abnormal begins to occur.

A company called Augury does exactly this with vibration and ultrasonic sensors installed on equipment:

“The collected data is sent to our servers, where it is compared with previous data collected from that machine, as well as data collected from similar machines. Our platform can detect the slightest changes and warn you of developing malfunctions. This analysis is done in real-time and the results are displayed on the technician’s smartphone within seconds.”

Predicting when a machine needs maintenance is incredibly valuable, translating into millions of dollars in saved costs. A great example is Goldcorp, a mining company that uses immense vehicles to haul away materials.

When these hauling vehicles break down, it costs Goldcorp $2 million per day in lost productivity. Goldcorp is now using machine learning to predict with over 90% accuracy when machines will need maintenance, meaning huge cost savings.

Shaping Experiences to Individuals

We’re actually all familiar with machine learning applications in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better experience for the user. That could mean suggesting products that you might like or providing relevant recommendations for movies and TV shows.

Similarly, in IoT machine learning can be extremely valuable in shaping our environment to our personal preferences.

The Nest Thermostat is a great example: it uses machine learning to learn your preferences for heating and cooling, making sure that the house is the right temperature when you get home from work or when you wake up in the morning.

Key Takeaways

The use cases described above are just a few of the virtually infinite possibilities, but they’re important because they’re useful applications of machine learning in IoT that are happening right now.

However, to reiterate, traditional data analytics are usually good enough for most IoT applications. Don’t be fooled by an IoT platform selling you on its machine learning capabilities when you’re just trying to look at trends over time to measure and improve your efficiency.

To make one final, critical point: with both traditional data analytics and machine learning, you need data. Gaining and maintaining large sets of clean, relevant data is an essential prerequisite to unlocking all the value that both data analytics and machine learning have to offer.