As a Strategic Planning and Performance Manager, Aubrey Phillips engages both people and data to optimize departmental efficiency. She has demonstrated leadership by spearheading interagency teams responsible for the development of Pinellas County’s COVID-19 dashboard and relief programs. Aubrey holds a bachelor’s degree in political science and environmental studies from New College of Florida, along with an advanced Geographic Information Systems certificate.
Jino Noel is a data science and technology leader with extensive experience in building data teams and practices across different organizations. His experience ranges from working in startups to large conglomerates across both Australia and the Philippines. At the time of this interview, he was the Chief Data Officer at Data Analytics Ventures, Inc. (DAVI). Currently, he is the Chief Data Officer at Angkas.
What are the key skills that a Chief Data Officer should possess nowadays?
A Chief Data Officer should have both data-related technical expertise as well as people leadership skills. Leading will always be part of the job, particularly for highly specialized technical people such as data engineers and data scientists. To be able to lead them properly, I believe it is better to be a technical person myself, so I can discuss technical matters fluently, which helps me gain their trust.
What data-related challenges have you faced as the Chief Data Officer of DAVI? How did you overcome these challenges?
Our data-related challenges are the same as any company. Being able to trust our data, cleaning up data from our sources, data latencies, and other related issues. DAVI overcame these by investing in people—hiring high-quality experts in our data engineering, data governance, and analytics teams to help us make sense of the data coming in—and building robust data pipelines that have increased the standard of quality of the data in our data lake.
How does DAVI make use of advancements in artificial intelligence (AI) and machine learning to help its clients understand their customers’ needs and buying patterns?
DAVI has recently started using machine learning to model our users’ propensity to buy certain products. This helps us create more accurate target audiences for our precision marketing campaigns. We are also moving forward with a recommendation engine project, with the goal of improving user engagement with our retail partners and with our promos and campaigns. On top of this, we are improving our machine learning operations expertise to make our model deployments repeatable and robust.
In the digital marketplace, data analytics acts as a guiding compass for app developers, enabling the creation of personalized, high-performing applications that align with user preferences. By leveraging data, developers can understand nuanced user behaviors and preferences, allowing them to tailor apps to meet specific user needs and aspirations.
Dive deeper into these discussions by reading Jino Noel’s full interview with The KPI Institute. Download the free digital copy of PERFORMANCE Magazine Issue No. 26, 2023 – Data Analytics on the TKI Marketplace. You can also purchase a physical copy via Amazon.
Alfonso Medela is the Chief Artificial Intelligence (AI) Officer at Legit.Health, where he oversees the use of advanced computer vision algorithms. A renowned expert in few-shot learning and medical imaging, his contributions include developing an algorithm capable of diagnosing over 232 skin conditions.
What are the key skills that a Chief AI Officer should possess in the context of your role at Legit.Health?
A Chief AI Officer at a medical organization like Legit.Health needs strong AI expertise, including extensive knowledge of machine and deep learning, and a profound understanding of medical data and healthcare to ensure precise algorithm development. Besides technical skills, strategic thinking and leadership are vital for guiding the AI team and aligning with company goals. Great communication and collaboration skills are also crucial for working effectively with different teams.
Can you describe your experience in developing and implementing AI strategies for computer vision applications, specifically in the context of diagnosing and treating skin pathologies? How have you leveraged AI to improve diagnosis accuracy and enable life-saving therapies?
Heading a team of specialists, we’ve developed advanced algorithms that accurately identify over 232 skin conditions and automate follow-ups for chronic skin conditions. Using deep learning techniques, our platform provides real-time diagnostic support to healthcare professionals, improving their accuracy and enabling early intervention. By collaborating with medical experts and continuously refining our algorithms, we are able to offer a powerful tool that empowers clinicians, transforming healthcare and improving patient outcomes.
What approaches or methodologies do you use to ensure the accuracy and reliability of computer vision algorithms in the context of skin pathology diagnosis? Can you share examples of how you have validated the performance of AI models and ensured their safety and effectiveness in real-world clinical settings?
To guarantee accuracy and reliability, our computer vision algorithms undergo a multi-stage validation process that encompasses retrospective and prospective clinical validations. Rigorous testing is performed on diverse, representative datasets, employing cross-validation to assess model performance. We collaborate closely with medical professionals, reviewing AI model outputs and gathering feedback to iteratively refine our algorithms. Furthermore, we conduct clinical trials and pilot studies to evaluate safety and efficacy. This ensures that our models adhere to real-world requirements and actively contribute to enhancing patient outcomes.
AI stands as one of the most transformative technologies of the modern era, revolutionizing the way people approach complex problems across various fields. From enhancing healthcare diagnostics to driving advancements in autonomous vehicles, AI’s potential is vast and continually expanding.
To explore the full spectrum of Alfonso Medela’s pioneering work in AI and to stay updated with the latest industry insights, read his full interview exclusively featured in the PERFORMANCE Magazine Issue No 26, 2023 – Data Analytics edition. Download your free copy now through the TKI Marketplace or purchase a printed copy from Amazon.
Dietmar Böhmer joined Tyme seven years ago and he has been leading the data and analytics function across the group, which includes TymeBank in South Africa and the newly launched GoTyme in the Philippines. He has led lending, data science, machine learning, and big data teams in banking for close to two decades and has lectured in the actuarial field prior to that.
Nowadays, with mounting pressure on businesses to be accountable for their environmental and social impact, it is no longer optional but expected for them to develop and implement sustainable business strategies that play out across three key areas: Environment, Social, and Governance (ESG). This pressure comes from rising public awareness, tightening regulations, and increased expectations from customers, employees, and investors.
Stakeholder engagement plays a significant role in the successful implementation of ESG strategies. In this article, let’s explore its functions and effects on ESG strategies.
The power of stakeholder engagement
Stakeholders are individuals, groups, or organizations that can influence or are affected by a company’s strategy from within and outside the organization. They can either drive change or resist it. Therefore, it is critical to identify stakeholders and understand their needs and expectations to ensure the ESG agenda reflects the priorities of those who matter and support the strategy’s long-term success.
Pay Governance LLC, a firm that provides independent advice on executive compensation matters, has developed the Stakeholder Value Creation Chain model (See Figure 1) to better understand the effects of stakeholder engagement on the economic success of a business. It demonstrates how ESG strategy, the stakeholder model, and the generation of corporate value all intersect to provide various advantages for corporations.
Engaging with stakeholders during the strategy execution phase allows companies to foster collaboration, build trust and confidence, encourage support for ESG actions, evaluate how the actions are perceived, mitigate potential risks, and improve decision-making.
To know more about ESG strategy and how it exactly boosts stakeholder engagement based on a report, read the full article in the PERFORMANCE Magazine Issue No. 25 – Sustainability Edition. You can download a free digital copy through the TKI Marketplace. Printed copies are also available on Amazon. But the price may vary depending on location.