By 2025, it’s estimated that the data volume to be created, copied, and consumed globally will hit a mind-blogging 463 exabytes. A significant change in the future is inevitable with such a massive number on the table. The more data you have, the better you can fulfill organizational goals. Here’s how.
The colossal amount of data that’s being generated each day enables organizations to derive valuable business insights. How? By revealing the patterns of potential customers. It fuels processes like predicting user behavior, building recommendation engines, and others. This data collectively, when used under a wide spectrum of domains, is what is referred to as data science. And the fuel that keeps the show running from the background is machine learning (ML) and artificial intelligence (AI).
This article will aim to eliminate the blur between both these closely related terms to help you understand how AI and ML are tied to data science.
Before delving deep into the topic, here’s a quick overview of the big picture.
Like many people, you probably shop online. You visit your favorite online store or several and scroll through the site until you find a pair of jeans you like. You also come across a slew of other items like accessories, footwear, etc., along with the top choices that coordinate best with the jeans.
Who’s making these recommendations? How do ‘they’ seem to know what you like? It’s all thanks to user-generated data or data science.
Data science is a broad and constantly evolving scientific discipline that deals with both raw and structured data to derive meaning out of them. It leverages big data - data that is incredibly large and complex and that can’t be processed through traditional methods - and utilizes all the necessary tools and technologies to help build such systems as artificial intelligence and machine learning. With data analysis, programming, statistics, data visualization, interpretation, etc., data scientists can make informed decisions that add value to the business.
Experts say that the more data you have, the more insights you can gain for your business. This is because enormous amounts of data allow you to apply various algorithms to it. In turn, you can achieve more accurate results to rely on for your business. You may even discover patterns that you didn’t know existed.
The question still remains, though: how is artificial intelligence or machine learning making a contribution?
Artificial intelligence - another term for making machines smart - is a broad concept of advancing machines to match human intelligence, thinking, behavior, and reasoning. These machines are fed huge amounts of data to make them proficient in identifying patterns and interferences in a matter of seconds.
Artificial intelligence is not a subset of data science. Nor is data science a class of artificial intelligence. However, some things overlap in both concepts. These include text mining, time series forecasting, and recommendation engines. Here’s a look at each one of them.
All these techniques find mutual use in AI and data science that add value to a bigger goal. It’s no surprise, then, that when the estimated data volume is estimated to be so high, AI will become less artificial and more intelligent.
Machine learning is a sub-category in the field of artificial intelligence. It includes a data-driven approach in which computers learn, adapt, grow, and develop by themselves with the data fed to them. Instead of relying on explicit programming, machines learn from the data. They observe the dataset, recognize the patterns in it, learn from the behavior automatically, and make predictions.
An example is the recommendations that you receive on YouTube, Facebook, and other platforms, which leverage machine learning technology. An even better example of machine learning is Google’s self-driving cars. It utilizes machine learning algorithms and executes the operation after automatically grasping the relevant information.
Although machine learning is a part of artificial intelligence, both have made and continue to make significant contributions to the field of data science. Artificial intelligence makes machines imitate human intelligence. It’s linked to a building system that focuses on solving complex problems. It also works on all kinds of data, such as raw, semi-structured, and structured for learning, reasoning, and self-correction. In essence, it elevates the chances of success for any business with its conclusions.
On the contrary, machine learning learns from past data to give future predictions. It only deals with semi-structured and structured data to understand the patterns and provide accurate results. It focuses on making machines learn with all the available data to put forward precise results.
Data science leverages the collected data from all of this at the end of the funnel to extract insights for business. The data goes through pre-processing, analyzing, visualizing, and predictions via statistical techniques and tools to find hidden patterns. To summarize, it mines massive datasets that are concerned with precisely resolving and estimating the unknown.
There’s really no debate left on whether artificial intelligence and/or machine learning are among the best routes to data science. Both these fields empower data scientists with abundant data that they can use to make insightful conclusions and decisions. It has always been a priority for every business to have their key needs fulfilled, and with continuing advancements in technology, the future is indeed bright. By imitating the desired actions of human intelligence, artificial intelligence and machine learning are proving to be the right way forward for data science.
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