IT Voice – How has a unified, data-focused culture come about, and in what ways will AI-driven tools impact decision-making processes?
Kapil Mehta – Data is not just some random numbers. It is more than that. In fact, it’s the lifeblood of a business, the secret language that reveals how a customer thinks and feels. In earlier days, collecting data and unifying it was considered a tedious process which led most of the organizations to leave it aside even after knowing how important it is for their businesses. But today, with the AI-driven tools in place, unified and data-focused culture has become the norm for success. Think of it like this: Data + culture = the beating heart of your organization’s future.
A unified data-driven culture is achieved with increased data management, democratization, and governance to ensure that each and every individual within the organization has uninterrupted access to data that they can trust. Here is where AI tools come into place. AI systems can process data and generate insights in real time and can eliminate the time-consuming tasks of manual data gathering and interpretation. This will automatically grant the governing authorities time to make quicker, more accurate, and unbiased decisions, hence making them agile to drive superior outcomes across business functions.
IT Voice – What is driving the rise of Data-as-a-Service, and how is the trend of data democratization evolving?
Kapil Mehta – The driving factor is nothing but the increasing demand of the organizations for high-quality actionable data in real-time. There are two ways to achieve the so-called high-quality actionable data, either making use of the on-prem infrastructure or utilizing DaaS. And businesses these days are shifting towards DaaS business models primarily due to the significant benefits of cost reduction, scalability, and enhanced data accessibility. Because, Data-as-a-Service eliminates the need for expensive on-prem infrastructure, leading to lower capital and operational expenditures.
With the rise of models like DaaS, there are large volumes of data that are being created at an augmented rate. That is one among the reasons why data democratization is a hot topic now more than ever. Secondly, technological advancements like AI-driven tools, particularly within big data processing and analytics tools, have laid a fertile ground for standardizing data. Also, the cultural shift towards data literacy has made organizations realize the power of democratizing data. By empowering employees at all levels with access to data and analytics tools, companies can actually set up an environment of data-driven decision-making and innovation.
IT Voice – How do advanced data architectures like data fabric and data mesh simplify data management and usage?
Kapil Mehta – Both data fabric and data mesh do help in data management and usage, though in different ways. For those organizations who want to promote autonomy across teams and decentralize the data ownership will have to ideally make use of data mesh. And those who need to centralize the available data and integrate it from multiple systems, data fabric will be the right choice. While the former focuses on organizational structure and data ownership, the latter is more bound to provide the technological foundation for integration and management. Together, these architectures can make use of data more reliable and efficient within organizations.
IT Voice – Can synthetic data help accelerate AI model training while maintaining privacy and security?
Kapil Mehta – Synthetic data is artificially created to impersonate what the real data pattern looks like, without any actual observations. Hence, in today’s scenario, where real data is scarce and expensive, synthetic data is in fact vital for training and testing AI models. This is also because it ensures AI models have large, diverse, and high-quality datasets for accurate training.
Synthetic data is also applicable as and when the small amount of available data appears to be sensitive in nature. Sensitivity is the factor that contributes to the privacy and security infringement of the data. Here, because the data is synthetic, and thus indirectly dependent on real personal or confidential data, it ensures privacy regulations are adhered to.
IT Voice – What role is generative AI playing in transforming business operations and personalizing customer experiences?
Kapil Mehta – With the evolution of AI Agents and models like DeepSeek AI, generative AI is no longer just an assistant but a core part of strategic business operations.
As we all know, generative AI is meant to enhance functioning by automating repetitive tasks. But that alone doesn’t define what the scope of the technology is. Today, for instance, AI-powered chatbots and virtual agents can handle a high volume of inquiries simultaneously, ensuring First Response Time (FRT) for businesses. Acting as autonomous digital employees—handling customer support, conducting research, and even executing financial transactions, these AI agents, powered by generative AI, are redefining how businesses interact with users, creating seamless, personalized experiences at scale.
IT Voice – What strategies can organizations adopt to build a collaborative, flexible, and compliant data-driven culture?
Kapil Mehta – To become data-driven, companies firstly need to be convinced of the benefits of becoming so. Current mindsets need to be changed. The leadership should be aligned to the data-driven goals in creating a culture of accountability and innovation.
To build a collaborative and flexible data-driven culture, the organizations should make sure that data literacy is offered for each employee at all levels. By ensuring this, we are breaking the silos and sharing the data across the group, thereby contributing to data democratization. Modern architectures like Data Mesh and Data Fabric can be adopted for this. To improve this data access and drive actionable insights in real time, it’s significant to deploy AI tools that automate the workflows. And for that, if scaling infrastructure is not really possible, the companies can instead use DaaS models for cutting up their cost. One last thing for the companies to have the data-driven culture is that, it should have a solid policy structure that is in compliance with GDPR, HIPAA and other regulatory frameworks so as to ensure data accuracy and safety.
