Examples of Big Data-Part II

Banking and Financial Services

The financial industry puts Big Data and analytics to highly productive use, for:
  • Fraud detection - Banks monitor credit cardholders’ purchasing patterns and other activity to flag atypical movements and anomalies that may signal fraudulent transactions.

  • Risk management - 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.

  • Personalized marketing - Banks use Big Data to construct rich profiles of individual customer lifestyles, preferences, and goals, which are then utilized for micro-targeted marketing initiatives.


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
  • Real-time alerting
  • Enhancing patient engagement


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:

  • Customizing curricula - 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.


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

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