The Ultimate Survival Guide on AI and Machine Learning in the Fourth Industrial Revolution, Part III

Part 3: Where does data science fit in?

In this five-part series on industrial AI, we will arm you with the knowledge you need to understand what AI, machine learning, data science and edge computing are, and how they are already impacting industry. See part two, where we covered how AI and machine learning work in industry.


What happens with all of that data once it is captured? Unfortunately for many companies, the answer is nothing. As little as 1 percent of industrial data gets used. Not using the other 99 percent is costly in terms of dollars and missed opportunities. But for those who do make the most of their data, the possibilities are endless. Data science is the key to unlocking this potential.

It is All About the Data

Machine learning is only as good as the data it is fed, which means edge computing and machine learning are ineffective without quality data. Say you discover that you have 20 terabytes of machine data. Now what do you do with that mountain of information? There is a lot of it, but you typically cannot get much value out of it directly. You either need to have a large team of data scientists or access to the right software to make sense of the data and use it efficiently and effectively.

Data scientists make and test hypotheses and use software and statistical analysis to extract insights from data. But what is data science in the first place?

Data science is the craft of turning data into action. It converts data into actionable business insights by using computational, statistical, and mathematical techniques and processes. It is an interdisciplinary field of scientific methods, processes, algorithms and systems that extracts knowledge from data.

Data scientists start with the problem they are trying to solve, determine what kind of data they need to provide an answer to the problem, prepare the data, then model the data — translating it into business outcomes.

One of the most important steps in this process is data preparation. Data scientists spend as much as 80 percent of their time cleaning, reformatting and combing through data. In order to reap the benefits of analytics-driven decision-making, machine learning engines must be trained on a large quantity of high-quality data — and industrial data is often messy. Data from heavy equipment can be affected by climate, human error and malfunctions.

In short, machine learning algorithms are a part of data science and skilled data scientists know how to model data to solve the problem at hand.

In part four, we’ll discuss the real-world applications of AI and machine learning in industry.

Don’t want to wait? Download A Survival Guide to AI and Machine Learning in the Fourth Industrial Revolution today.