Industry

The status quo is changing for most industries as boundaries blur between fields due to innovation, disruption, and digitally-driven change. That’s why keeping abreast of emerging trends in sectors outside your own is vital, not only because your organization’s competitive landscape may be changing, but because there are universal, strategic lessons to learn from the opportunities and threats convergence poses for every marketplace. We examine emerging trends and the impact of evolving tech in key fields such as healthcare, financial services, telco, energy, mobility, and more to help you capitalize on the possibilities of the future while managing the challenges of today.  

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As we explore in this Advisor, machine vision systems employing machine learning and other artificial intelligence techniques are now bringing major benefits to automakers, dealers, and repair shops in the form of camera-based automated vehicle inspection systems.
Saeed Rahman, Natalie Slawinski, and Monika Winn examine how pioneering companies in agriculture, agri-food, and other sectors can build and leverage ecological knowledge (knowledge about the very ecosystems they rely on) to develop innovative practices that help regenerate social and natural systems. In doing so, these companies can reap benefits for their business and help turn our unsustainable agricultural systems into systems that sustain a growing human population without severely degrading or destroying ecological systems necessary for agriculture and other industries.
The theme of this issue of Amplify is quite specific, yet very broad. The contributions cover disparate topics that are all very appropriate under the theme. There are some common threads, though, and recognizing these will help governments, organizations, and individuals understand the many facets of “data” in the large and complex healthcare space and act accordingly.
Curt Hall focuses on the benefits of integrating unstructured data into electronic health records. He describes how biometric data, lifestyle data, and general healthcare information can come together to help clinicians, researchers, and health/wellness companies better understand the effect of patient health behaviors and lifestyles on potential approaches and treatments. More personalized medical treatments, improved health trend identification, and lower healthcare costs are all possible outcomes.
Five Arthur D. Little Partners and Principals predict that big data will move the healthcare industry’s digital transformation forward, providing better admission rate estimation, more effective chronic-care treatments, and a reduction in medication-error rates. Their article includes detailed descriptions of eight drivers of data-driven healthcare: technology trends, data quality and availability, data security, an enabling ecosystem, public-private partnerships, patient participation, the need for better change management, and the development of employees with data analysis skills.
Cutter Expert San Murugesan looks at why health data is so valuable to cybercriminals, why criminals are often successful in their attacks, and the cost of these breaches. He outlines seven technologies/approaches that can help: authentication and access control, encryption, data anonymization, mobile device security, monitoring and auditing, artificial intelligence, and zero trust. Murugesan concludes with a list of processes that should always be in place to secure health data.
Jacek Chmiel examines current challenges in the data processing space. He outlines the issues stemming from multiple health data standards, the need for more developed data quality processes, and the industry’s perhaps unnecessary aversion to data streaming. Chmiel offers hope in the form of federated analytics and federated learning to allow more collaborative data processing between countries and proposes increased use of automation. He also advocates for employing publicly and commercially available data sets and looks at how natural language processing, machine learning, and quantum computing are the future of data-driven pharma.
The authors relate how the data science team at Sanofi’s Toronto, Canada, pharmaceutical manufacturing site moved from a reactive to a proactive operational mode to enhance data analytics and increase efficiency. They describe the prescriptive analytics solution they developed to significantly reduce reaction times when manufacturing issues occur. Their live data analytics engine accommodates various modeling approaches and performs additional data mining.