The data banks handle varies from purchases at point-of-sales, to payments done online, from withdrawals at ATMs to customer profile data, and a lot more. Using this data to gain a competitive advantage and to better understand their clients is of critical importance. Data can be used to generate cross-selling opportunities, to analyze history of client requesting a loan, to improve liquidity and risk management. It can be used in analyzing ROI trends, recommending specific bonds for investors, and automatic purchasing or selling at specific market triggers.

Supply chains rely on real time data, and production risks are evaluated on the spot. It is also used in speeding up production output, running simulations, using robotics in production chains, triggering alarms in case of hazards, and product quality management.

transforming the raw feed from the data pipeline to customer billing, or whether the utilities infrastructure (connectivity, sensors, etc. etc.) all becoming smart, data management has become at the very heart of both the public and private energy and utilities sector. Things have advanced so fast in this field that AI is used in dynamic energy management, outage prevention, and even modelling for failure possibility.

In the earlier years, data was always used to price risk. However in the modern era, their have been rapid developments in this sphere. For example car insurance today uses individual driver behavior data to determine how to price a policy for a specific individual, and health insurance companies are using data generated by a device worn by the patient regarding their health status and developments.

Furthermore, the most cutting edge data scientists are using the newest technologies and most advanced software to develop self-driving cars, robotic manufacturing, traffic control, and even in warning the driver about coming dangers in the road ahead.

Patient electronic health records need to store demographic information about patients, their medical history, and test results in order to trigger a warning or a reminder for the patient. Predictions need to be made about who is most at risk of cancer, diabetes, or Covid. Efficient supply chain management for the pharmaceutical industry is critical for stability and for the health of the general public. Hospital bed occupancy and hospital parking statistics need to be monitored to control overflow of visitors. Telemedicine is constantly pushing diagnostic information to patients using apps. These are just a few examples of how Tripathon Data Science can support the different players in the health care sector.

However in the last few years, more sophisticated techniques are emerging. “Robot reporters” are speeding up production processes, Artificial Intelligence is used by news centers in monitoring of trends and of breaking news from social media, and AI is also connecting with virtual reality in the movie production and television industry, and even artificial voice can be created to cover for actor errors to avoid redoing the shooting once again.

and the data is then analyzed to make all types of decisions about the client experience. Shopping habits are analyzed, smart pricing strategies are created, and strategic decisions like product merchandising, discounts, and product display are all using one form of data management or another. Indeed, multiple studies have shown that profit margins can increase by 60% with the retailers introducing big data analytics.