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5 Real Life Big Data Examples and How to Capitalize on Them

By: Kaylie Gyarmathy on May 23, 2019

Organizations have long recognized that information is critical to making good decisions, but in the era of big data, it’s more important than ever before. Advancements in artificial intelligence (AI), cloud computing, and Internet of Things (IoT) devices have fundamentally altered the way companies collect and analyze data. The sheer scale of data being collected makes it impossible for humans to sort through manually, forcing them to turn to sophisticated algorithms to identify patterns, trends, and unusual insights that can inform business decisions.

Implementing the systems needed to collect and analyze big data is one of the great challenges facing companies today. However, given the significant advantages to be gained from a good big data strategy, many industries are going to great lengths to do so. There are a number of big data examples in business today.

5 Real Life Big Data Examples and How to Capitalize on Them


The healthcare industry has never been lacking in terms of data; the problem is that healthcare companies have struggled to make effective use of it. Part of the challenge is the unstructured nature of the data. Incorporating structured metrics like overhead costs and the amount of medication prescribed into an algorithm is simple enough, but accounting for the valuable data contained in sources like medical charts (some of which is still handwritten) is much more difficult. And that’s even before compliance issues relating to patient privacy, one of the core challenges of big data in healthcare, are taken into account.

As big data analysis utilizing AI and machine learning continues to improve, however, healthcare organizations are finding numerous ways to benefit from this trove of data. There are several big data examples in healthcare worth noting. Long-term analysis of trends in diagnosis, treatment, and health outcomes can ultimately reduce the cost of care significantly by eliminating ineffective or redundant practices. Healthcare professionals can also develop more accurate predictive models for a variety of conditions and treatments, which will allow them to make better-informed decisions when it comes to patient care. With the proliferation of wearable IoT medical devices, organizations will be able to collect even more patient data to continuously refine their big data analysis.

Media and Entertainment

The digitization of entertainment content has led to a profound transformation in the way companies develop and market their products and services. Although e-commerce accounts for only 14.3 percent of US retail sales, this figure doesn’t convey the entire picture of how people are functioning as consumers or the total impact of big data examples in retail. According to Nielsen data, young adults (people ages 18-34) spend 43 percent of their time-consuming media on digital platforms, with almost a third of that media coming over their smartphones. Those interactions create a vast array of data points that can help companies position their products and services more effectively.

With big data analytics capable of finding patterns in this data, content providers can predict audience interests and demands far more accurately than ever before. Rather than running their content through a series of focus groups that may not represent their true audience, they can use data trends to target specific content to specific demographics. By understanding how users actually consume media and entertainment, companies can also optimize their distribution platforms to meet their customers where they are. Locating digital services closer to end users through the use of edge data centers, for instance, can significantly reduce the latency associated with streaming content.


The combination of smart RFID tags, GPS tracking, and “smart city” IoT sensors is already transforming the way companies and urban planners conceptualize transportation infrastructure. These devices generate massive amounts of data that provide a clear picture of how people utilize this infrastructure and how traffic patterns are impacted by common variables such as weather, accidents, and maintenance. With autonomous vehicles on the horizon, the potential utility of this data is bound to increase significantly in the coming years.

Urban planners can use accurate data collected from IoT sensors to design better highways and optimize existing infrastructure, making transportation one of the easiest big data visualization examples. Sophisticated cloud computing algorithms can analyze data gathered from sensors and commuters to inform people how to reach their destinations more efficiently, guiding them away from high traffic areas. The predictive power of big data analytics can also identify potential dangers before they actually pose a threat, alerting drivers that their vehicle needs maintenance or informing city engineers that a bridge needs repairing.

Financial Services

It should come as no surprise that the banking and financial services industry were quick to embrace big data analysis. Whether it’s financial data gathered from customers or reports coming from various investment markets, these organizations have a huge amount of data at their disposal. With the rapid miniaturization of processing hardware and the growth of cloud computing, financial services companies no longer need to rely on the mainframe supercomputers of old, instead utilizing the very latest in high-performance computing to sift through the mountain of data they’re collecting on a daily basis.

Several aspects of the financial industry make it a good big data practical example. Much of the world’s equity trading is already being handled by high-frequency trading (HFT) algorithms that take market signals from a variety of sources and make a decision to buy or sell in a matter of milliseconds. Banking and credit card companies can also use big data analytics to monitor purchasing activity to identify fraud, potentially saving customers thousands of dollars. The same techniques can be applied to cybersecurity measures as well, spotting and countering hackers and DDoS attacks long before a human observer could even realize there was a problem.


Although often perceived as a sector in rapid decline, the manufacturing industry has enjoyed quite a comeback in recent decades thanks to automation and other smart technologies that have made factories more efficient and productive. Today’s industrial machinery is equipped with a variety of IoT sensors that provide valuable data companies can use to streamline operations and dramatically reduce costs. Gathering more data at each stage of the production process provides greater visibility into operations and how products are being received and utilized by consumers, which is a key big data example.

Big data analytics can use this data to design better products that are more aligned with customer needs. Rather than investing in capital intensive research and development up front, continuous data gathering enables a more iterative approach to design that responds quickly to market demands. With IoT-equipped manufacturing equipment, companies can use the resulting data to predict when machinery needs to be repaired or replaced, leading to more efficient production schedules. Collecting data throughout the delivery and storage process also helps to optimize complicated supply chains to avoid costly delays and human error.

The era of big data is already here, and the companies that take steps to capitalize on the opportunities it presents will undoubtedly enjoy a competitive advantage in the years to come. Data centers will play a crucial role in these efforts, as any organization engaged in the gathering and analysis of big data will need someplace to store it and access to the resources needed to analyze it for actionable. Colocation data centers can provide both, offering low-latency, high-security cross connections to leading cloud platforms and building the hybrid and multi-cloud environments that allow companies to make the most of big data while still maintaining control and visibility over their data.

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