Data Science and Machine Learning

Medical Devices

Data science and machine learning can be integrated into connected care devices to use biomarkers to recognize and track diseases, improve patient outcomes, optimize device performance, and streamline clinician workflow.

Health & Wellness

Data science and machine learning can be used to aid in predicting patient outcomes based on historical data, enabling improved treatment protocols and patient experience.

In pharmaceutical research, they can be used extract insights from data, leading to drug discovery, the development of new drugs and treatments, and identification of previously unknown side effects.

Digital

Data science and machine learning can tease deep insights from the dense streams of raw data generated by connected devices. These insights can then be used to develop actionable triggers and automated decision making in applications ranging from personal wearables to smart home devices to transportation.

Technology

With every advance of technology, information security becomes even more crucial, regardless of sector. Machine learning can be used to automate cybersecurity solutions and reduce risk. It can efficiently analyze huge amounts of data and identify patterns to help detect threats and malware. It can also be used to audit a company's current data protection techniques and identify vulnerabilities before an attack happens.

Organizational Innovation

Machine learning can be used to drive hyper automation across an organization, resulting in better customer support, improved worker productivity and engagement, and system integration. Automating routine, short-term tasks across an organization gives teams more time, resources, and opportunity to experiment, take risks, and engage in innovative thinking.

Sustainability and Circular Economy

Data science and machine learning can predict the remaining useful life or failure of a system containing motors, pumps, bearings, and other sensors and mechanisms. It can extend the lifetime of a system through predictive maintenance, reducing waste.

Across industries, machine learning can be used to better predict demand, streamline supply, and optimize manufacturing processes. In cities, data science and machine learning can be used to analyze real time and historical data and enhance transportation and traffic management systems, reducing fuel use and greenhouse gas emissions.

Startups

Machine learning and data science can consider thousands of metrics at once, making business forecasting and analysis easier, faster, and more accurate. Some fintech startups use it to forecast demand for different currencies based on real-time market conditions and consumer behavior.

Consumer Products

Machine learning can leverage natural language processing to better understand what people are saying about a company's products on social media, in online reviews, or the company’s customer feedback forms. These consumer insights can guide current improvement and future development of products.

Commercial/Industrial Product Development

Data science and machine learning can combine data from several sensors (sensor fusion) to identify patterns and problems in systems, improving system functionality and efficiency. They can be used in real-time data analytics to optimize operations, logistics, and supply chain.