Big Data in ActionBig Data in Action
Effective integration and efficient analysis of Big Data analytics can help healthcare providers uncover hidden illness and improve patient care. These data include electronic health records, treatment plans, prescription information and medical images, which provides a more in-depth understanding of patient’s illness efficiently. It contributes to more advanced disease research by providing access to bioinformatic data, where researchers can transform these data into useful information that enables a deeper understanding of diseases such as cancer and congenital disorders of unknown illness. Anaylsing these big data is a great tool for healthcare professionals in devising disease treatment and prevention plans.
Being in a competitive field, car manufacturers are turning to deploying artificial intelligence in self-driving technologies. These cars use thousands of tiny cameras and sensors to relay real-time information, enabling predictive analytics so that these vehicles can perform decisions automatically, such as adjusting to speed limits, avoiding dangerous lane changes, and driving via the quickest route.
Bid data that is collected from the Internet of Things (IoT) devices is useful in improving services and product manufacturing in many industries. Integrating these data with advanced algorithms helps companies such as UPS find the best routes for delivery drivers and offer alternative routes in case of accidents or bad weather conditions. Not only does this optimizes companies’ revenue, but it also brings good to the environment by reducing fuel consumption and exhaust emissions.
Banking and Finance
The bank industry is turning towards big data and analytics that effectively gathers customer behaviour-related insights as a way to improve their transactions and revenue too. They collect data through online actions and transactions, which helps with implementing new schemes, strengthen security, prevents fraud and maintaining regulatory compliances. Thanks to big data technology, it also saves time processing millions of valuable customer information too. JP Morgan’s Contract Intelligence (COiN) platform utilizes Natural Language Processing (NLP) to process and extract data from approximately 12,000 commercial credit agreements annually. Without machine learning, this could take a much longer time processing manually. Other fintech companies such as Stripe and Paypal also invests in data science and machine learning tools that detect and prevent fraudulent activities.
Data science is essential to making sure that our personal information is safeguarded from cybercrime wherein unusual activities can be detected instantaneously. Kaspersky, an international cybersecurity firm uses data science and machine learning to detect of malware every day. Through data science, its technology is able to detect and learn new methods of cybercrime instantly.