In advance of the Data Science Salon taking place in Miami from September 11 to 13, we asked our speakers to shed some light on how Artificial Intelligence and Machine Learning are impacting three of America’s largest industries. Whether these data science practitioners have been working in the fields of finance, hospitality or healthcare from 3 years to over a decade, the wide span of knowledge that their experiences bring is instrumental to understanding AI and ML’s global impact at large.
Within the healthcare industry, AI is making significant waves. According to Michael Zelenetz, Analytics Project Leader at New York-Presbyterian Hospital, AI is affecting both front-of-house and back-of-house processes. “We’ve implemented robotic process automation and centralized time keeping,” he explained. “Additionally, we’ve used AI to improve our clinical documentation.”
“In the pharma industry specifically, AI has brought in visible process efficiencies in the three major areas of the industry - drug discovery, development, and commercialization,” said Sangeeta Krishnan, Data Solutions Architect at Daugherty Business Solutions. “The adoption of AI applications in the pharmaceutical process is bound to bring in personalized therapy options for the patient.”
AI’s impact on the hospitality industry is no less instrumental. Dalela Bharati, Product Owner of Data and Metrics at Booking.com, identified “one of the several uses of AI at Booking as facilitating communication between customers and properties. A chatbot can be used to modify or cancel a reservation, thereby eliminating the need to call customer support and consequently saving time and transaction costs,” she said.In addition to hotels, airlines are witnessing welcome changes due to AI’s introduction. “Within the airline industry AI allows for advanced customer analysis leading to efficiencies in personalization of customer experiences through targeted marketing and product recommendations,” said Alise Otilia Ramirez, Data Scientist at Spirit Airlines. “Data Science has allowed for automation of traditional revenue management in terms of identifying optimal price points, and customer segmentation as a method of maximizing revenue.”
Get insights like this in person at our next Data Science Salon: Applying AI and Machine Learning to Finance, Healthcare and Hospitality, in Miami.
However beyond improving operational efficiency and customer service, AI’s most novel contribution to every industry is its predictive ability. As Catalina Arango, Associate Data Scientist in Customer Care Strategy & Analytics at Florida Light and Power explained, “Data science helps us be more precise in our planning and execution. We’re able to better forecast our call volumes, predict when people will call and why they are calling.” Bharati has witnessed similar impacts on customer service at Booking.com: “Data science has enabled us to better predict our customers demands and purchase behaviour and match it with supply from our partners, thereby increasing customer satisfaction and overall revenues.”
On top of staying current in terms of innovation, data science teams must remain ethical as they navigate the difficult balance between respect for privacy and growth. As Bharati explained, “There is a fine line between personalization and privacy which must be held sacrosanct by data scientists. The most common challenges for my data science team include governance, maintaining and validating foundational datasets and adhering to utmost standards of data quality.”
Healthcare in particular highly prioritizes issues of privacy, given the large amount of personal data involved in all aspects of the industry. Krishnan spoke to the controversial ethical debate taking place in her line of work, saying, “In pharma and healthcare, patient data security is an area of big concern and different stakeholders in an organization have differing views on this aspect. However, reliable evidence is a key aspect to decision making based on existing models.”
Predictive analytics are essential to teams mediating issues of ethics and privacy. As Ramirez put aptly, “Common challenges for teams include identifying possible consequences of data science solutions prior to implementation, and the response from customers with such dynamic adjustments.”
Though the speakers agree on many aspects of AI’s current impact on their fields, experts have differing opinions on how the new industry will develop in the coming years.
Nathan Black, Co-founder and Chief Data Scientist at QuantHub, pointed to the growth of what he refers to as “citizen data scientists”; in other words, “employees and business managers who apply data science methodologies in non-data science roles.” As this distinct group of people continues to grow, AI tools will become more largely accessible across company departments and the use of data science in everyday tasks will span beyond analytics teams.
Bharati believes that the transformation Black mentioned will be an arduous process, but remains excited for such changes to take place. “The data science community will continue to grow, however its growth will also be fraught with pain since most organizations do not yet understand how best to use these resources and ask the right questions,” she expressed. “While it is difficult to predict whether techniques and solutions will converge, I am highly optimistic about the collaboration opportunities it could present fuelled by knowledge exchange events such as Data Science Salon.”
When discussing issues of community, it is important to recognize that humans are not the only ones relevant in data science. Machines are part of the mix as well, and this may be more of a stepping stone than a threat. In Krishan’s eyes, AI’s adoption will lead to increased partnership between humans and machines. “A combination of digital tools such as AI, ML and RPA would lead to process optimization. This would build up a collaborative workforce creating a natural balance between machines and people to maximize productivity,” she predicted.
Forging a successful relationship between AI tools and the teams that build them requires a deep understanding of one’s goals and necessary strategies. As Matt Denesuk, Senior Vice President of Data Analytics and AI at Royal Caribbean Cruises explained, “Data generation and retention at most companies was designed for reporting and compliance – not for supporting AI. AI Transformation requires treating data as a strategic imperative, and re-instrumenting the entire company to generate, retain, and make available the data needed to build and run AI-based systems.”
Curious for more?
Don’t miss the next Data Science Salon in Miami, September 10-11, 2019.