Big data vs. machine learning: How they differ and relate – TechTarget

Data is at the heart of the modern enterprise, helping organizations to better understand their customers, make better business decisions, improve business processes, track inventory, monitor competitors and take other steps to successfully run their operations. But over the past two decades, many organizations have had to get a better grasp on how to handle the increasing amounts and different forms of data — i.e., big data — that they’re now creating and collecting.
In many cases, big data is so large and complex, with a combination of structured, unstructured and semistructured data, that traditional data management technologies can’t process, store and manage it effectively or efficiently. With regular data warehouses not a good fit, Spark, Hadoop, NoSQL databases and other big data platforms and tools have emerged to help fill the gap and enable businesses to set up data lakes as repositories for all that data.
However, simply doing so isn’t sufficient to get business value from big data. Nor do conventional data analytics applications fully tap its potential benefits. As more companies master the big data management process, forward-thinking ones are applying intelligent and advanced forms of analytics to extract more value from the data in their systems. In particular, machine learning, which can spot patterns and provide cognitive capabilities across large volumes of data, gives organizations the ability to take their big data analytics initiatives to the next level.
Using machine learning algorithms for big data analytics is a logical step for companies looking to maximize their data’s potential value. Machine learning tools use data-driven algorithms and statistical models to analyze data sets and then draw inferences from identified patterns or make predictions based on them. The algorithms learn from the data as they run against it, as opposed to traditional rules-based analytics systems that follow explicit instructions.
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Big data provides ample amounts of raw material from which machine learning systems can derive insights. By combining them, organizations are producing significant analytics findings and results. However, in order to fully harness the combined power of big data and machine learning, it’s important to first understand what each is and can do on its own. Let’s look at big data vs. machine learning.
Big data is, of course, data. The term itself embodies the idea of working with large quantities of data. But data quantity, or volume, is just one of the attributes of big data. Various other “V’s” also must be considered. For example, the following list includes seven V’s:
Big data analytics is the overall process of exploring and analyzing sets of big data. It incorporates disciplines such as data mining, predictive modeling, statistical analysis and machine learning. The cornerstone of modern AI applications, machine learning provides considerable value to organizations by deriving higher-level insights from big data than other types of analytics can deliver.
Machine learning systems are able to learn about data and adapt over time without following specific instructions or programmed code. In the past, companies built complex, rules-based systems for a vast range of analytics and reporting uses, but they often were brittle and unable to handle continually changing business needs. Now, with machine learning, companies are better positioned to improve their decision-making, business operations and predictive analysis capabilities on an ongoing basis.
Big data and machine learning aren’t competing concepts or mutually exclusive. To the contrary, when combined, they provide the opportunity to achieve some incredible results. In fact, successfully dealing with all the V’s of big data helps make machine learning models more accurate and powerful. Effective big data management approaches improve machine learning by giving analytics teams the large quantities of high-quality, relevant data needed to successfully build those models.
Many organizations have already discovered the power of big data analytics enhanced by machine learning. For example, Netflix uses machine learning algorithms to better understand the viewing preferences of individual users and then provide better recommendations, helping to keep people on its streaming platform for longer. Similarly, Google uses machine learning to provide users with a more personalized experience, not only for search but also to build predictive text into emails and give optimized directions to Google Maps users.
The amount of data being generated continues to grow at an astounding rate. Market research firm IDC predicts that 180 zettabytes of data will be created and replicated worldwide in 2025, almost three times more than the 64.2 zettabytes it counted for 2020. As enterprises continue to store and analyze huge volumes of data, the only way they’ll possibly be able to make sense of it all is with the help of machine learning.
Thanks to the work of data scientists, machine learning engineers and other data management and analytics professionals, more companies are using big data, machine learning and data visualization tools together to power predictive and prescriptive analytics applications that help business leaders make better decisions. In the coming years, it will be no surprise if companies that don’t combine big data and machine learning are left behind by competitors that do.
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