Big Data in ActionBig Data in Action

Big data has brought about new business models and transformed traditional administrative systems. Data professionals extract useful, untapped information from an ocean of data source and then integrate that sight into business operations, data warehouses, business proposals and applications, which allows analysts, researchers, and business operators to formulate better business strategies and faster decisions based on existing consumer behaviours and preferences. From using Google map in navigating your way around, to finding the best songs for you on Spotify, Big Data has opened the doors to understanding the world and leaving an impact on every process in our lives even though we may be unaware of it. Here are some of the major sectors where Big Data analytics play a vital role in deriving new insights.


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.

Autonomous Vehicles

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.

How is a background in engineering helpful to the data science industry? Well, data scientists have an in-depth understanding about different data systems and their ability to code helps enhance the productivity and quality of the algorithm code. They also have prior experiences in data cleaning and detecting outliers within the datasets, thus generating more accurate and effective decisions. Possessing multiple skills from data extractions to developing insights and software engineering, data scientists who has a background in engineering are more advantageous career-wise.

Social Commerce market

Ever wonder how websites and platforms seem to understand your taste and recent interests? Through data science, they are able to offer personalised user experiences based on your online activity. For instance, Spotify curate lists of songs based on the music genre you recently listen to. Video streaming platforms like Youtube and Netflix use data aggregator that will recognize your activity collected from other application to recommend the best suited shows from its massive collection.

Working with data is currently a highly wanted profession in many different industries such as finance, consulting, marketing, telecommunications, medical and many more. According to the Salary Explorer in 2022, data scientists in Hong Kong has a monthly salary of HKD27,300 to HKD91,800, with an average monthly salary of HKD58,100. It is predicted that there is a steady and optimistic salary increase for experienced data scientists. Another study by LinkedIn found that statistical analysis and data mining was ranked the second most sought after skills by various employers, highlighting the huge potential for data professionals in today’s work force.

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.