Explore how big data is transforming industries, including vital types of big data applications and significant areas of innovation.
Data is one of modern companies’ most valuable assets, with organizations relying on it to gain insights that drive efficiency and competitive advantage. With various big data applications to choose from, big data professionals have more ways to glean those insights and reap the benefits.
With 18 billion Internet of Things (IoT) connections worldwide and nearly 40 billion projected connections by 2033, the amount of data companies and organizations collect is only expected to grow [1]. This growth will likely drive demand for more advanced big data analytics to manage, interpret, and utilize this information across industries. Additionally, the continuous influx of data from sources like social media, IoT devices, and online transactions has created new opportunities for real-time data processing and innovation. It also opens the door to new challenges regarding quality and security. So, how is big data already transforming industries?
You can apply big data in various ways to solve challenging problems, streamline existing operations, and create innovations. While new applications continue to emerge, the following big data applications are already making waves across industries.
When you use predictive analytics, you are trying to answer the question, “What is likely to happen next?” Predictive analytics involves leveraging historical data using data mining and statistics to forecast future trends, actions, and events. Analyzing past data empowers you to make informed decisions about future challenges. Big data makes this type of analysis more accessible, as the increase in information allows you to draw conclusions and spot trends more effectively.
For example, if one of your colleagues went to a particular local restaurant for lunch on a Tuesday, it would be difficult for you to predict whether the restaurant is typically busy on Tuesdays using this information alone. However, suppose you notice several colleagues, your family members, and your friends all frequent the restaurant every Tuesday. The increased data available allows you to predict with a higher degree of certainty that the restaurant will likely be busy for lunch on Tuesdays. Because big data includes so much information, frequent occurrences or patterns are less likely to be due to chance.
Machine learning models use big data to train algorithms that improve automatically through experience. These models can take large volumes of existing information to recognize patterns and relationships and then apply these findings to new and unseen data. As more data becomes available, your models can iteratively improve and provide better insights with the latest data.
You can train your machine learning model to perform whatever type of procedure will provide the maximum benefits at a given time. If you just collected a high volume of data, you might use your machine learning model to label and segment your information. If you have your data formatted and ready for analysis, you might use your machine learning model to perform different types of analytics.
In traditional data analytics, you would pull data from a data warehouse or other data management system that collected it over a set period. Your analysis would represent the data during that time, and to update it, you would need to collect additional data and run another analysis. However, because big data often comes from sources that continually add and update data, such as sensors, social media, and IoT devices, you can now leverage real-time data processing techniques to make decisions, detect errors or anomalies, and support processes based on the most current information.
For example, consider self-driving cars. Many cities already have autonomous vehicles for delivery or transportation services. When making decisions, the autonomous vehicle must process real-time inputs to make safe decisions on the road. Self-driving cars utilize big data and real-time data processing to assess road conditions, traffic, signals, and environmental information throughout the journey.
While big data is popping up in almost every industry, particular areas are emerging as hubs for big data innovation. Consider the following areas to explore the most critical big data applications.
In health care, big data can be used to analyze the data from thousands of patients to find correlations between patient information and outcomes. Understanding these trends allows you to identify patients with early warning signs to improve diagnostic accuracy and treatment efficacy.
Machine learning models are commonly applied in health care to improve access (telemedicine), advance mobile and smart device design, and decrease health care expenditures.
Traffic remains a global issue without a clear solution. However, real-time data collection from remote sensors, mobile phone data, and connected vehicles allows cities to dynamically adjust traffic signals, predict congestion, and optimize traffic flow. This can reduce travel time and improve driver safety.
As cyberattacks become more sophisticated, big data analytics helps you detect and mitigate these threats in real-time. By analyzing online data, algorithms can flag anomalies quickly so that you can respond to cybersecurity threats faster. For example, American Express uses big data from customer and merchant transactions to make real-time decisions at the time of each purchase to determine whether something is fraudulent. This helps to minimize risk overall, which reduces cardholder stress while reducing the financial burden on the company associated with fraudulent transactions.
In education, big data facilitates personalized learning by powering adaptive learning platforms. These platforms analyze your performance data as a learner to create customized instruction plans specific to your needs. This learning experience helps engage students, build on existing interests, and build intrinsic motivation throughout the learning process. One of the ideas here is to mimic the personalized experience offered on streaming services, such as Netflix movie recommendations. By mimicking the style of instruction and assessment material that was effective for learners with similar interests and skill levels, adaptive platforms can leverage big data to design learner profiles and learning maps for each individual.
You have to reach your target audience for your business to succeed. Market segmentation can help with this. With big data, you can use machine learning algorithms to analyze consumer patterns and find trends that help you identify the market area most likely to resonate with your product or service.
Before you implement big data solutions, understanding the advantages and limitations of this type of data can help you decide whether it’s appropriate for your purpose. Consider a breakdown of each.
Improved insights: Big data allows organizations to base their decisions on comprehensive findings from large data sets, leading to more accurate and effective insights.
Identifying areas for innovation: By analyzing data trends, companies can uncover new markets and innovate products to meet current demands.
Personalized experiences: With a high volume of information, you can personalize learning pathways, product recommendations, and streaming services for users.
Security concerns: Big data is a target for hackers, meaning organizations need to prioritize security measures to keep sensitive information safe.
Quality issues: With a constant influx of data from various sources, you may not always be able to ensure the data is of appropriate quality. You need to assess your data for accuracy and relevancy before making conclusions.
As an aspiring professional interested in entering the field of big data, you can take several pathways to build the necessary skills and knowledge, including learning basic skills and knowledge surrounding big data by pursuing a data-related degree, such as data science. Alternatively, big data, machine learning, or data analytics certifications can help you build targeted knowledge.
As with any skill, hands-on practice is vital to the learning process. Consider completing Guided Projects on Coursera or seeking an internship that allows you to work with data in real-world settings. These experiences help you strengthen your skills, identify knowledge gaps, and provide tangible experiences you can showcase when seeking professional opportunities.
Big data continues transforming industries through predictive analytics, machine learning, real-time data processing, and more. Regardless of your industry, learning to leverage big data's insights can help you make informed decisions, accurately predict future events, and identify areas for innovation.
To develop your big data skills, exciting Specializations and Guided Projects on Coursera can help you take steps to meet your educational and professional goals. For a foundational course, consider Modern Big Data Analysis with SQL. Another beginner-friendly option is Big Data Emerging Technologies, a six-module that includes a project for hands-on practice.
Statista. “Number of Internet of Things (IoT) connections worldwide from 2022 to 2023, with forecasts from 2024 to 2033, https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/.” Accessed January 21, 2025.
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