The technology known as Big Data is one of the most impactful innovations of the digital age. Patterns and correlations hidden in massive collections of data, revealed by powerful analytics, are informing planning and decision making across nearly every industry. In fact, within just the last decade, Big Data usage has grown to the point where it touches nearly every aspect of our lifestyles, shopping habits, and routine consumer choices.
Here are some examples of Big Data applications that affect people every day.
Big Data powers the GPS smartphone applications most of us depend on to get from place to place in the least amount of time. GPS data sources include satellite images and government agencies.
Airplanes generate enormous volumes of data, on the order of 1,000 gigabytes for transatlantic flights. Aviation analytics systems ingest all of this to analyze fuel efficiency, passenger and cargo weights, and weather conditions, with a view toward optimizing safety and energy consumption.
Big Data simplifies and streamlines transportation through:
Congestion management and traffic control
Thanks to Big Data analytics, Google Maps can now tell you the least traffic-prone route to any destination.
Real-time processing and predictive analytics are used to pinpoint accident-prone areas.
Ads have always been targeted towards specific consumer segments. In the past, marketers have employed TV and radio preferences, survey responses, and focus groups to try to ascertain people’s likely responses to campaigns. At best, these methods amounted to educated guesswork.
Today, advertisers buy or gather huge quantities of data to identify what consumers actually click on, search for, and “like.” Marketing campaigns are also monitored for effectiveness using click-through rates, views, and other precise metrics.
For example, Amazon accumulates massive data stories on the purchases, delivery methods, and payment preferences of its millions of customers. The company then sells ad placements that can be highly targeted to very specific segments and subgroups.
The financial industry puts Big Data and analytics to highly productive use, for:
Banks monitor credit cardholders’ purchasing patterns and other activity to flag atypical movements and anomalies that may signal fraudulent transactions.
Big Data analytics enable banks to monitor and report on operational processes, KPIs, and employee activities.
Customer relationship optimization
Financial institutions analyze data from website usage and transactions to better understand how to convert prospects to customers and incentivize greater use of various financial products.
Banks use Big Data to construct rich profiles of individual customer lifestyles, preferences, and goals, which are then utilized for micro-targeted marketing initiatives.
Government agencies collect voluminous quantities of data, but many, especially at the local level, don’t employ modern data mining and analytics techniques to extract real value from it.
Examples of agencies that do include the IRS and the Social Security Administration, which use data analysis to identify tax fraud and fraudulent disability claims. The FBI and SEC apply Big Data strategies to monitor markets in their quest to detect criminal business activities. For years now, the Federal Housing Authority has been using Big Data analytics to forecast mortgage default and repayment rates.
The Centers for Disease Control tracks the spread of infectious illnesses using data from social media, and the FDA deploys Big Data techniques across testing labs to investigate patterns of foodborne illness. The U.S. Department of Agriculture supports agribusiness and ranching by developing Big Data-driven technologies.
Military agencies, with expert assistance from a sizable ecosystem of defense contractors, make sophisticated and extensive use of data-driven insights for domestic intelligence, foreign surveillance, and cybersecurity.
The entertainment industry harnesses Big Data to glean insights from customer reviews, predict audience interests and preferences, optimize programming schedules, and target marketing campaigns.
Two conspicuous examples are Amazon Prime, which uses Big Data analytics to recommend programming for individual users, and Spotify, which does the same to offer personalized music suggestions.
Weather satellites and sensors all over the world collect large amounts of data for tracking environmental conditions. Meteorologists use Big Data to:
Study natural disaster patterns
Prepare weather forecasts
Understand the impact of global warming
Predict the availability of drinking water in various world regions
Provide early warning of impending crises such as hurricanes and tsunamis
Big Data is slowly but surely making a major impact on the huge healthcare industry. Wearable devices and sensors collect patient data which is then fed in real-time to individuals’ electronic health records. Providers and practice organizations are now using Big Data for a number of purposes, including these:
Prediction of epidemic outbreaks
Early symptom detection to avoid preventable diseases
Electronic health records
Enhancing patient engagement
Prediction and prevention of serious medical conditions
Enhanced analysis of medical images
While Big Data can expose businesses to a greater risk of cyberattacks, the same datastores can be used to prevent and counteract online crime through the power of machine learning and analytics. Historical data analysis can yield intelligence to create more effective threat controls. And machine learning can warn businesses when deviations from normal patterns and sequences occur, so that effective countermeasures can be taken against threats such as ransomware attacks, malicious insider programs, and attempts at unauthorized access.
After a company has suffered an intrusion or data theft, post-attack analysis can uncover the methods used, and machine learning can then be deployed to devise safeguards that will foil similar attempts in the future.
Administrators, faculty, and stakeholders are embracing Big Data to help improve their curricula, attract the best talent, and optimize the student experience. Examples include:
Big Data enables academic programs to be tailored to the needs of individual students, often drawing on a combination of online learning, traditional on-site classes, and independent study.
Reducing dropout rates
Predictive analytics give educational institutions insights on student results, responses to proposed programs of study, and input on how students fare in the job market after graduation.
Improving student outcomes
Analyzing students’ personal “data trails” can provide a better understanding of their learning styles and behaviors, and be used to create an optimal learning environment.
Targeted international recruiting
Big Data analysis helps institutions more accurately predict applicants’ likely success. Conversely, it aids international students in pinpointing the schools best matched to their academic goals and most likely to admit them.