Dalith Steiger and Massimo Buonaiuto, two renowned experts in the field of artificial intelligence and internationally sought-after speakers, will be in Zurich on 31 October 2024 to shed light on the opportunities, risks and dangers of AI, the current possibilities and challenges, from the following perspectives.
Excited to share my latest conversation on “The Leadership Lab” podcast, now available on Spotify: how generative AI is fundamentally transforming our society.
We are at a pivotal moment, for the first time in human history, we face intelligences that surpass our own: we have created the very intelligent aliens we once imagined would land on our planet. That is Generative AI, and the impact for human society will be immense.
“AI Will Be a Billion Times Smarter 💡 Than Humans before 2037 ” (Mo Gawdat, Former Chief Business Officer of Twitter/X)
I’m thrilled to announce my participation at the Leaders Dialog conference in Kassel, Germany, on the 3rd and 4th of July 2024. This event is a pivotal gathering for DACH companies to explore the evolving landscape of digitalization. My talk will focus on the revolution of the Generative AI, offering insights into cutting-edge technology trends and presenting compelling business cases to inspire other leaders in this digital revolution.
In an insightful presentation at the “World Smart Bioprocessing Summit – Pharma 4.0,”. This summit serves as a crucial platform for sharing advances and fostering innovation within the Pharma & Biopharmaceutical Manufacturing community. The role of data management in the context of AI.
Scaling in machine learning ensures each feature contributes equally to model outcomes. This article explores key methodologies like Min-Max, Standardisation, and Robust Scaling, and how choosing the right method impacts model performance.
Highlighting the transformative role of Health Level Seven International (HL7) and Fast Healthcare Interoperability Resources (FHIR) in the pharmaceutical industry, emphasizing their importance in enhancing drug development, patient outcomes, and regulatory compliance. HL7 and FHIR facilitate seamless data exchange across healthcare systems, enabling pharmaceutical companies to leverage comprehensive patient data for accelerated drug development and improved therapeutic strategies. The integration of FHIR in a global R&D company is showcased as a case study, illustrating significant improvements in clinical trial efficiency, data security, and regulatory compliance. The implementation process involved strategic planning, infrastructure upgrades, software development, and rigorous data validation to ensure compliance with FHIR standards. This case study underscores the critical role of data standardization and advanced analytics in pharmaceutical innovation, highlighting the benefits of real-time data access, efficient patient recruitment, remote monitoring, and streamlined regulatory processes.
Addressing challenges in data management involves designing robust strategies, implementing quality frameworks, and ensuring compliance with regulations. From data migration to security protocols, each step plays a crucial role in maintaining accurate, accessible, and secure data for organizational success.
Data management stands as a pivotal element in modern enterprises, encapsulating both value and inherent risks. Recognizing the significance of high-quality data is essential for enhancing decision-making, boosting revenue, and minimizing costs. This discourse delves into the multifaceted nature of data management, highlighting the paramount importance of maintaining impeccable data quality to avert the adverse impacts of inaccuracies and ensure compliance with regulatory standards. Addressing data quality risks—including incompleteness, inaccuracies, and inconsistencies—is crucial for operational efficacy. Emphasizing a strategic approach to data governance and the adoption of best practices, such as regular assessments and the implementation of comprehensive management plans, can significantly mitigate these risks. This exploration underscores the necessity of a holistic and proactive stance towards data management to harness its full potential while safeguarding against potential pitfalls.
In a manufacturing plant endeavor to optimize processes through Artificial Intelligence, the challenge transcended technical hurdles, evolving into a quest for cohesive teamwork among specialists. The introduction of weekly knowledge sharing sessions, fostering a culture of open dialogue and mutual respect, paved the way for a unified language and understanding. Agile methodologies, including Scrum and Kanban, streamlined project management, enhancing collaboration and transparency. A pivotal strategy involved pairing data scientists with engineers, facilitating a symbiotic exchange of expertise that culminated in a predictive model reducing machinery downtime. This journey underscored the transformative power of technology, harmonized through teamwork and shared vision, culminating in heightened efficiency and innovation.
In the realm of life sciences research, the FAIRplus Cookbook emerges as a pivotal guide for integrating the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—into data management practices. This invaluable resource offers a series of practical recipes for the FAIRification of data, catering to a diverse audience, including researchers and data stewards. It underscores the significance of data standardization, as demonstrated through the application of CDISC-SDTM models for clinical trial data, to enhance the quality and utility of scientific data. By leveraging the FAIRplus Cookbook, the scientific community can navigate the complexities of data management, ensuring that research data are not only standardized but also aligned with ethical considerations and regulatory requirements. This guide thus stands as a testament to the collaborative effort to foster a culture of open, standardized, and ethically responsible data sharing in life sciences.
Total Data Quality Management (TDQM) embodies a holistic approach to enhancing data integrity across all facets of an organization’s data lifecycle. This methodology prioritizes the accuracy, completeness, consistency, and relevance of data, ensuring its strategic alignment with business objectives. TDQM integrates practices such as data profiling, cleansing, governance, and quality monitoring to mitigate risks and elevate decision-making capabilities. Central to TDQM are the principles of data governance and management, which establish the framework for data quality standards, stakeholder roles, and the implementation of data strategies. Additionally, TDQM stresses the importance of data security and privacy, safeguarding the organization’s and stakeholders’ trust. Through comprehensive components including data analysis, integration, and continuous quality monitoring, TDQM ensures data serves as a robust, strategic asset, facilitating competitive advantage in a data-centric business landscape.
At the upcoming “AI, Data Analytics & Insights Summit – DACH” on 11th – 12th November 2021, a session will be dedicated to exploring Artificial Intelligence applications within Research and Development. This interactive, senior-level online meeting will convene 250 experts from the DACH region, offering a unique platform for sharing insights and advancements in the field.
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