In a conversation with Dinis Guarda, Prayson Daniel, Director of the Transformation Lab at NTT DATA Business Solutions Nordics, shared his vision for artificial intelligence (AI) and data analytics. Daniel emphasised the importance of integrating traditional machine learning techniques with the latest advancements like large language models. The podcast is powered by Businessabc.net and citiesabc.com.
Prayson Wilfred Daniel serves as the Director of the Transformation Lab at NTT DATA Business Solutions Nordics, a position he has held since December 2023. With over 10 years of expertise in data science, machine learning (ML), and artificial intelligence (AI), Prayson specialises in leveraging these technologies to drive business transformation, enhance customer and employee satisfaction, and optimise operational performance.
He leads a multidisciplinary team of experts in Data Science, MLOps, and DevOps, guiding them in the implementation of cutting-edge technologies like Natural Language Processing (NLP), Computer Vision, and Bayesian decision-making. His work spans multiple sectors, including healthcare, insurance, financial services, and digital commerce.
Prayson Wilfred Daniel shared his approach to algorithm development and A/B testing, highlighting that understanding the client’s goals is crucial before evaluating data. He tells Dinis:
“My primary focus in developing algorithms and conducting A/B testing is to start with people. I first seek to understand the client’s goals before diving into data. Once we know what we aim to achieve, we then determine if we have the right data to support it. Ultimately, it’s not about the algorithms or the data itself; it’s about how we use these tools to enhance our ability to solve problems, whether that’s improving lives or increasing profits. The key is to ensure that what we build is not just for the sake of building but to achieve a meaningful mission. Understanding this mission helps us use data and algorithms effectively to reach our goals.”
Ethical considerations in data Modelling: Understanding data generation
During the interview, Prayson Wilfred Daniel discussed the importance of understanding the data generation process in machine learning and algorithm development. As Prayson explains:
“In machine learning and algorithm development, it’s crucial to first examine the mechanism behind data generation. For instance, in Bayesian modelling, we start with prior knowledge and update it with new data to refine our beliefs. By understanding how data is generated, including the assumptions and biases inherent in different contexts, we can better identify and address potential pitfalls.
Focusing solely on data can lead to overlooked biases, so it’s essential to also understand the people or systems that produce the data. This approach helps prevent common mistakes, such as biassed models or exclusion of certain groups, and ensures more ethical and accurate outcomes.”
Enhancing data model Management with WatsonX.Governance
Prayson Daniel, Director of the Transformation Lab at NTT DATA, emphasises robust data governance through a partnership with IBM, utilising IBM watsonx. His team focuses on ethical AI deployment, performance monitoring, and bias mitigation to maintain AI system integrity in business operations. Prayson explains:
“Our key focus with IBM has been on governance in data model management. Given our experience with diverse clients and various stages of technological advancement, we needed a robust solution to monitor and govern model deployment and performance across different clients. Partnering with IBM allowed us to co-create specialised components, addressing the limitations of off-the-shelf solutions. This collaboration has led to innovative and effective ways to oversee and optimise model performance and governance.”
The future of AI: Integrating classical and modern approaches
Prayson Daniel in a conversation with Dinis Guarda, discusses the current state and future direction of data analytics, emphasising the integration of classical machine learning techniques with emerging technologies like large language models. He tells Dinis:
“While large language models like ChatGPT are currently revolutionising the field, I believe the future lies in blending traditional machine learning with these new advancements. Combining the proven effectiveness of classical statistical learning with the innovative power of emerging language models offers a path forward. Historically, in times of chaos, such as during the COVID-19 pandemic, there was a shift back to classical methods like Bayesian modelling. Moving forward, integrating these time-tested approaches with the latest in generative AI will enable us to harness the strengths of both, potentially transforming industries, improving lives, and shaping the future.”
Concluding the interview, Prayson Daniel provided a compelling perspective for data analytics and artificial intelligence that is based on a blend of modern and conventional approaches. His approach to AI research is influenced by his extensive background in data science, philosophy, and theology. It emphasises the need to fuse advanced technologies such as big language models with traditional statistical learning methods. He believes that this combination is essential to realising AI’s full potential in addressing a variety of industry difficulties and influencing global outcomes.
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