SCRS Insights

Dr. Sakshi Shringi: Hello Dr. Ashish. This is Dr. Sakshi Shringi. I am the Editor-in-Chief for SCRS Insights, and it is my pleasure to introduce Dr. Ashish Dibouliya, a data and AI architect and researcher with over 19 years of experience in enterprise data architecture, AI-ready data platforms, and large-scale digital transformation. He holds a PhD in Computer Applications and actively contributes to international research through publications, peer review leadership, and global conference engagements in data science and artificial intelligence. Dr. Dibouliya specializes in building trusted, scalable, and regulation-grade data ecosystems that enable responsible AI and intelligent decision-making. Today, he will share insights on data and AI innovation, industry trends, research challenges, and opportunities for aspiring technologists. We are delighted to have you with us.

Dr. Sakshi Shringi: So, starting with Dr. Ashish, could you briefly introduce your career journey and your background to our audience?

Dr. Ashish Dibouliya: Thank you so much, ma’am, for inviting me to share my experience on this prestigious forum. I am a data and AI architect with close to 19 to 20 years of experience in enterprise-grade data engineering, analytics, and AI enablement. Broadly, my focus has been on designing cloud-native, governed, and AI-ready data platforms. These platforms transform fragmented data into trusted and intelligent data so that organizations can derive the right insights from it. I specialize in metadata-driven data architecture, canonical data modeling, data governance frameworks, and responsible AI foundations. Alongside my industry practice, I also take a keen interest in global research communities, peer review activities, and professional forums in data and AI. Currently, I work as a Managing Director of Data Architecture in a financial institution in the United States.

Dr. Sakshi Shringi: Great. Since you already have deep expertise in this domain, what motivated you to specialize in data and AI architecture?

Dr. Ashish Dibouliya: I have worked in the data domain for almost 19 years. My career progression started as an ETL developer, then a data architect, principal architect, and senior enterprise data architect. Over time, I realized that organizations struggle not because they lack data, but because they lack trust in data. There is a lot of data, but there is no trust in it. That is where reports conflict, analytics become questionable, and AI solutions fail because there is not a reliable foundation. These factors impact an organization’s ability to identify the right insights from the data.

In my journey, I have worked on projects where organizations needed to build platforms from scratch, modern data warehousing platforms, and transformation initiatives. There are also very interesting and complex use cases like mergers and acquisitions, as well as divestitures, where different systems and data landscapes need to be integrated or separated. These situations create a strong opportunity to build architecture that is accurate and useful for decision-making. Accuracy is critical, and now with increased regulatory focus, traceability has become another important segment. How best you can trace data, ensure reliability, and make it ready for intelligent decision-making has consistently motivated my work in data and AI architecture.

Dr. Sakshi Shringi: You are absolutely right. Data is everything today, but extracting correct insights and ensuring traceability is difficult. Given your experience of building systems from scratch, could you highlight some of your key contributions, recognitions, and achievements?

Dr. Ashish Dibouliya: Certainly. First of all, I want to clarify that this interview represents my personal achievements and it has nothing to do with the work I do in my financial institution. Still, I believe it is worth highlighting some of my contributions that could be useful for aspirants in this domain. One of my key achievements is building a metadata-driven data framework, which focuses on setting up metadata so that much of the traditional coding is abstracted out of the process. Developers can focus on small SQL-based logic, while the rest of the pipeline and automation happens behind the scenes through the metadata-driven framework.

Another area of strong interest has been data governance. I have built multiple processes and products pertaining to governance. Many governance products available in the market are expensive and work like a black box. Once an organization adopts them, it becomes dependent on the vendor. On the other hand, if you can create similar data products yourself, and design them in a way that allows customization to fit the organization’s needs, it becomes extremely valuable. That is another area where I have made contributions.

Besides my regular work, my contributions have also been recognized through international research publications, conferences, speaking invitations, and peer review responsibilities for reputed journals, including Scopus-indexed and IEEE journals. I have received some awards and also hold senior-grade professional memberships in recognized bodies.

Dr. Sakshi Shringi: That is very inspiring for budding data engineers and researchers, especially because it reflects how diverse the options are in this field. I would also like to know, what are your core technical expertise areas?

Dr. Ashish Dibouliya: My technical expertise has always been centered around data. I have completed multiple certifications to ensure I stay aligned with evolving technologies. Broadly, my expertise includes enterprise data architecture, cloud data platforms, metadata-driven framework development, automated data quality, and end-to-end lineage, which collectively come under the data governance area. I also work on AI-ready data engineering. I try not to focus too much on tools because these principles are technology-agnostic, but certifications like Snowflake Architect and Databricks Architect complement these skills.

In addition, my focus has expanded lately toward AI governance, explainable AI foundations, and building scalable analytical systems that ensure the data is trustworthy and AI can be deployed at scale. With AI now being a central theme across industries, governance and explainability are becoming equally important.

Dr. Sakshi Shringi: That makes sense. Given your expertise and the kind of work you do, how do you connect data engineering with AI enablement?

Dr. Ashish Dibouliya: AI models largely depend on data quality and consistency. I design architectures where governance, semantics, and lineage are enabled by design. This ensures AI models receive reliable inputs, produce explainable outputs, and can be operated responsibly in real-world environments. In that way, data engineers, data scientists, AI engineers, data architects, and AI architects come together to build a complete ecosystem. That harmony is where the real connection between data engineering and AI enablement becomes strong.

Dr. Sakshi Shringi: Since you are well connected with evolving industry developments, I would like to ask, what current trends in data and AI excite you the most?

Dr. Ashish Dibouliya: One of the most important trends I see today is the strong focus on governance. Governance has always been part of data programs, but the amount of focus it receives today has increased tremendously, especially in financial institutions. Compared to how things were a few years ago, governance has become central to enterprise data programs.

At the same time, this connects with multiple new directions, such as autonomous data platforms, AI-driven governance frameworks, real-time decision-making, and generative AI-based interfaces that allow better interaction with data. Responsible AI frameworks are also becoming essential because organizations need trust and compliance. These trends are shaping the future of data and AI across industries.

Dr. Sakshi Shringi: That is very relevant. Since you are both an industry leader and researcher, what research challenges do you find most significant in data and AI these days?

Dr. Ashish Dibouliya: AI is now everywhere and has moved beyond experimentation into real-world practice. One of the biggest challenges today is trustworthiness of AI. The second is explainability. Bias is another critical issue. We have seen how AI can introduce unintended bias, which creates serious risks.

A major research focus should be how do we make sure models deployed in real-world environments are trustworthy. In my view, AI alone cannot sustain without strong governance and human monitoring. That is why organizations are establishing dedicated AI governing committees that test models through structured evaluation cycles. Only after validation through questionnaires, test cycles, and governance checks should the model be allowed to go into production. This governance-driven approach is essential to handle both AI challenges and remediation.

Dr. Sakshi Shringi: With these challenges, many young researchers are moving toward explainable AI, data insights, and responsible AI. What opportunities do you see for young researchers in this field?

Dr. Ashish Dibouliya: There is a growing demand for experts in data architecture. We often say data is the gold, and truly, the real gold is data. Over the last few years, data volume has grown tremendously. Earlier we talked about MBs and GBs, now it is terabytes and beyond. When AI is combined properly with strong data foundations, it plays a crucial role in driving transformation.

There are strong opportunities in data architecture, AI engineering, cloud data platforms, responsible AI, and AI governance. Researchers who combine technical depth with ethical awareness and domain knowledge will lead the future of AI-driven transformation. This transition has already started. Data engineers are becoming AI engineers, and architects are moving toward AI architecture roles.

Dr. Sakshi Shringi: With your experience and work ethics, what advice would you like to share with researchers and young technologists?

Dr. Ashish Dibouliya: Build strong foundations in data engineering, machine learning, and statistics. Work on real-world problems. Publish your research and participate in peer review, because peer review gives exposure to global thinking and emerging quality research. It helps in understanding what the world is working on.

Most importantly, focus on building AI systems that are not only intelligent but also trustworthy. Today, there are many AI tools and technologies available, and it is easy to build proofs-of-concept, but these systems can fail in real-world scenarios if trust, governance, and ethics are not addressed. When deploying AI in real-world environments, it is important to ensure systems remain within responsible and ethical boundaries.

Dr. Sakshi Shringi: Absolutely. We must ensure reliability and trust in the tools and systems we build. Any final message for our audience, Dr. Ashish?

Dr. Ashish Dibouliya: Data and AI are shaping the future for every industry, but the true impact lies not just in building intelligent systems, but in building systems that people can trust. Adoption of systems depends heavily on trust and transparency. Those who invest in strong data foundations and responsible AI will define the next era of digital transformation.

Dr. Sakshi Shringi: Thank you so much, Dr. Ashish. It was truly insightful listening to you, and I am sure our audience will benefit greatly from this discussion. Thank you for joining SCRS Insights and sharing your valuable perspectives.

Dr. Ashish Dibouliya: Thank you, Dr. Sakshi. It was nice talking to you, and thank you for providing me this opportunity. I am honored to be part of this prestigious platform and to share my experience and work. Thank you so much.

Dr. Sakshi Shringi: Thank you. I will be ending the meeting now, bye.