Pairing Big Data With Machine Learning
Big data and machine learning are two technological developments that are changing our lives and transforming businesses around the world. If you want to harness them, you first need to know what they represent individually, in order to understand what they can achieve together.
Defining Big data
Big data is a term that describes a large volume of structured, semi-structured and unstructured data that has the potential to be mined for information and used in machine learning projects and other advanced analytics applications.
Bigger Data Than Ever Before
More data is being created now than at any point in history. It seems like every part of our increasingly connected world is undergoing a digital transformation. We’re creating new words beyond megabytes and gigabytes to describe all this data: Facebook users create 4 petabytes of data per day by one measure, view or uploading 350 million photos and 100 million hours of video. There are an estimated 4.3 billion internet users worldwide, each of those browsing, shopping, swiping, chatting, and posting.

It’s not only about our activity online. Connected devices account for an ever-growing share of digital traffic, especially smart devices in (IoT) Internet of Things. And every projection has data creation growing exponentially in the coming years, as it has over the past decade. By one measure, more than 90% of the world’s existing data has been created in the last 2 years, and this has been true for the last 30 years.
Big data has the potential to provide companies with valuable insights into their customers which can be used to refine marketing campaigns and techniques and increase customer engagement and conversion rates. Brands and businesses who utilize big data hold a competitive advantage over those who ignore the data since they have the ability to make faster and more informed business decisions.
Furthermore, utilizing big data enables companies to become increasingly customer-centric. Past and real-time data can be used to assess the evolving preferences of consumers, consequently allowing businesses to update and improve their marketing strategies and become more responsive to customer desires and needs.
These huge datasets being created today offer immense opportunities for insights and new business models, but they also pose challenges. How can a business handle such unprecedented volumes of complex data effectively? That’s the question that animated early and ongoing discussions of big data, harnessing these large datasets for business use.
Big data was first coined as a term in the 1990s, but it wasn’t until the 2000s that technology caught up with the ideas. The advent of superfast networking, cloud-based storage and processing, and digital services all combined to make big data a reality — and give people the opportunity to do something with it.
The Smartest Machines Yet
In the early years of big data, applications taking advantage of it were known as data mining. Now, data mining has been superseded by Ai and machine learning.
Machine learning is ideal for handling big data. It’s a fast, precise, and sophisticated data analyst/processor that can work with massive volumes of complex information, sorting data accurately, spotting interesting patterns, and even making predictions of the future.
Machine learning is particularly skilled at categorizing data and performing analytics to find patterns. It can process vast amounts of data, quickly and accurately, using its ever-accumulating knowledge of the subject matter to make judgments that continually improve. And as machine learning becomes more advanced, it can handle more and more complex data, with greater and greater sophistication.
In fact, big data is integral to the advancement of machine learning. The more data you provide it to learn from, the smarter it gets. As machine learning’s knowledge increases, so does its ability to make informed analyses and judgments. You could even look at data as fuel for Ai— the more a model is fed, the better it progresses.
Big data and machine learning have a perfectly symbiotic relationship. Big data’s usefulness depends on machine learning’s ability to interpret it, and machine learning’s increasing analytical skill depends on the existence of big data. It’s a relationship that continues to create ground-breaking results.
Data In A Day
Examples of Big data in use are everywhere. In the online retail sector, powerhouse Amazon is leveraging a truly gigantic data store to train its world-class recommendation algorithm, so that it can provide nuanced and appropriate product recommendations to its estimated 103 million Amazon Prime users and other customers.
The treasure trove of purchase records, browsing information and customer details and other data that Amazon collects every millisecond of every day provides an incredibly deep level of customer insight. This could never have been imagined in the era before Big data — and accessing it would not be possible without the power of machine learning.
Information Found In Big Data
- Comparative analysis — This includes the cross-examination of user behavior metrics and the observation of real-time customer engagement in order to compare one company’s products, services and brand authority with those of its competition.
- Social media listening — This is information about what people are saying on social media about a specific business or product that goes beyond what can be delivered in a poll or survey. This data can be used to help identify target audiences for marketing campaigns by observing the activity surrounding specific topics across various sources.
- Marketing analysis — This includes information that can be used to make the promotion of new products, services, and initiatives more informed and innovative.
- Customer satisfaction — All of the information gathered can reveal how customers are feeling about the brand, if any potential issues may arise, how brand loyalty might be preserved and how customer service efforts might be improved.
Big Data Collection Practices, Praise, And Criticism
Companies use Big data accumulated in their systems to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer preferences and, ultimately, increase profitability. Big data is also used by medical researchers to identify disease risk factors. Data derived from electronic health records, social media, the web, and other sources provide up-to-the-minute information on infectious disease threats or outbreaks.
For many years, companies have had few restrictions on the type of data they collect from their customers. However, as data collection and use has increased, so has data misuse. Concerned citizens who have experienced the mishandling of their data or have been victims of a data breach are calling for laws around data collection transparency and consumer data privacy.
The outcry about personal privacy violations led the European Union to pass the General Data Protection Regulation (GDPR), which took effect in May 2018; it limits the types of data that organizations can collect and requires opt-in consent from individuals. While there aren’t similar laws in the U.S., government officials are investigating data handling practices, specifically among companies that collect consumer data and sell it to other companies for unknown use.
The Human Side Of Big Data
Ultimately, the value and effectiveness of Big data depend on the practitioners tasked with understanding the data and formulating the proper queries to direct Big data analytics projects. Some Big data tools meet specialized niches and allow less technical users to use everyday business data in predictive analytics applications. Other technologies — such as Hadoop-based Big data appliances — help businesses implement a suitable compute infrastructure to tackle Big data projects while minimizing the need for hardware and distributed software know-how.
Big data can be contrasted with small data, another evolving term that’s often used to describe data whose volume and format can be easily used for self-service analytics. A commonly quoted axiom is that “big data is for machines; small data is for people.”
Conclusion
IDG’s Data Age 2025 report forecasts that global data will grow from the current 33 zettabytes to 175 zettabytes (175 trillion gigabytes) by 2025. When we’re talking in terms of figures like that, big data seems like quite an understatement.
Imagine the insights an Ai system can learn from such quantities of high-quality data, and the levels of sophistication and precision it can reach from such an abundance of training material. For all that’s been accomplished so far, we have only seen the tip of the iceberg for Big data and machine learning.
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