Navigating Data Scarcity and Compliance with AI

Suneet Taparia's S.L.I.M AI framework transforms data engineering by synthesizing data, locating intelligence, and measuring impacts.

Suneet Taparia, the Associate Director of Data Engineering and Data Science at MSD, shared his extensive knowledge and innovative ideas at the Data Engineering Summit 2024 held in Bengaluru. With over 18 years of experience in data science and analytics, Suneet has established himself as a leader in leveraging data for both business growth and societal benefit. Suneet’s talk focused on a transformative framework for integrating AI into existing data systems, aiming to enhance business processes and create significant social impact.

The Context of the Talk

At the heart of Suneet’s presentation was the S.L.I.M AI framework, a strategic approach to integrating AI into existing products and systems. Given the rapid advancements in AI and the proliferation of startups in Bengaluru, this talk was particularly relevant. Suneet’s framework promises to streamline AI adoption, addressing challenges like regulatory compliance, data scarcity, and the need for scalable solutions.

Synthesis: Overcoming Data Scarcity

Suneet began by addressing a common challenge in data engineering: data scarcity. Regulations like GDPR and limitations on data sharing can significantly hinder data-driven projects. To overcome this, Suneet introduced the concept of synthetic data generation using Generative Adversarial Networks (GANs). He highlighted how GANs, commonly associated with generating realistic images and videos, can also be used to create synthetic tabular data. This synthetic data mirrors real-world data in statistical and aesthetic properties, allowing engineers to test and scale their models without accessing sensitive production data.

Suneet emphasized that synthetic data is not just a workaround but a crucial component for compliance by design. By generating data that retains the essential characteristics of real data without the associated privacy risks, businesses can innovate while staying within regulatory boundaries. This approach is especially vital for sectors like banking and finance, where data restrictions are stringent.

Locate: Enhancing Self-Service Analytics

The next pillar of the S.L.I.M framework, Locate, focuses on improving data accessibility through self-service analytics. Suneet’s co-presenter, Guru, elaborated on this by discussing the use of Natural Language Processing (NLP) and Large Language Models (LLMs) for translating human language into SQL queries. This innovation simplifies data access, enabling non-technical users to interact with data systems more efficiently.

Guru explained how metadata, table schemas, and descriptions can be leveraged to build a robust self-service analytics platform. He also highlighted the limitations of current NLP systems, such as the tendency of LLMs to hallucinate and generate overly complex queries. To mitigate these issues, he suggested incorporating knowledge graphs, which provide a more structured and contextual understanding of data relationships. This integration ensures more accurate and relevant query results, enhancing the overall user experience.

Intelligence: Building Knowledge Graphs

The third pillar, Intelligence, revolves around the creation and utilization of knowledge graphs. Knowledge graphs enable the understanding of complex, multi-dimensional relationships within data, making them invaluable for various applications. For instance, in the auto insurance industry, knowledge graphs can link customer information, claims data, location data, and weather patterns to provide deeper insights and improve fraud detection.

Suneet illustrated how knowledge graphs could revolutionize customer segmentation and service personalization. By mapping out intricate connections within data, businesses can uncover hidden patterns and opportunities. This capability is crucial for developing more effective and responsive strategies, whether in customer service, marketing, or operational efficiency.

Measure: Beyond Traditional KPIs

The final pillar, Measure, challenges the conventional reliance on Key Performance Indicators (KPIs). Suneet argued that while KPIs are useful for business assessments, they often fall short of capturing the nuances of human interactions and social impact. He provided a compelling example from the education sector: an eighth-grade student struggling to understand a concept. Traditional KPIs like click-through rates do not capture the emotional and psychological struggles of such a student.

Suneet proposed a more holistic approach, using AI to provide hyper-personalized educational assistance. Large Language Models (LLMs) can act as study buddies, offering tailored explanations and support without the frustration that a human teacher might experience after repeated questions. This personalized interaction not only improves learning outcomes but also addresses the mental well-being of students, demonstrating AI’s potential for profound social impact.

Integrating AI into Existing Systems: The S.L.I.M Framework

Suneet’s S.L.I.M framework—Synthesis, Locate, Intelligence, Measure—offers a comprehensive strategy for integrating AI into existing products and systems. This approach allows businesses to innovate without discarding their current investments. By identifying and enhancing specific areas within existing products, companies can achieve AI-driven improvements incrementally and sustainably.

The framework’s adaptability is one of its strongest features. It can be applied across various domains, from banking and finance to healthcare and education. For instance, in healthcare, the S.L.I.M framework can enhance patient care through personalized treatment plans and predictive analytics. In agriculture, it can optimize resource allocation and crop management, contributing to food security and sustainability.


In his concluding remarks, Suneet emphasized the importance of adapting to change and leveraging AI for both business and social benefits. He encouraged the audience to view AI not as a disruptive force but as an enabler of positive transformation. By applying the S.L.I.M framework, businesses can navigate the complexities of AI integration, ensuring they remain competitive while also making a meaningful impact on society.

Suneet and Guru’s insights at the Data Engineering Summit 2024 provided a clear roadmap for harnessing the power of AI in data engineering. His vision of a future where AI enhances both business processes and social outcomes resonated with the audience, inspiring them to embrace innovative solutions and drive continuous improvement in their respective fields.

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