A cohesive & data-centric culture is essential for businesses to thrive in the AI-driven world: Kapil Mehta, Visionet Systems

As we move towards 2025, data and AI technologies will change how businesses work, innovate, and grow. It will enable the unified, data-driven culture that enables teams in most industries to make real-time, informed decisions to break down silos and make the business agile. In an interaction with Express Computer, Kapil Mehta, VP of Data & AI, Visionet Systems, explains how data-driven innovations can transform businesses and why organisations must embrace data democratisation and leverage technologies like Gen AI and data fabric to accelerate business innovation.

Q. Could you take us through the emergence of a cohesive, data-centric culture and how AI-powered solutions will influence decisions?

The cohesive and data-centric culture emerged as it was essential for businesses to thrive in this AI-dominating world so as to make smarter, faster decisions. Accurate, accessible, and well-managed data across the organisation often qualifies the organisation to step away from the guesswork and base their decisions on reliable information. Moreover, data-driven culture has always contributed to a more strategic approach to business challenges.

Additionally, AI-powered solutions take this game to a further extent by providing real-time insights, predictive analytics, and automation, which means it allows companies to speedily analyse massive data amounts, reveal hidden patterns, and predict trends, thus acting proactively instead of reactively. For instance, studies have found that AI can improve forecast accuracy in the retail sector by reducing errors up to 50%.

It was also noticed that artificial intelligence could uplift the financial sector by 38% in 10 years time. In the same way, some reports have been released predicting that AI could help the healthcare sector save $150 billion annually by becoming more efficient and making better decisions. These are illustrations of the advanced data culture that AI provides which helps businesses to be proactive and make decisions based on facts.

Q. Describe the developing need for Data-as-a-Service and what’s happening to data democratisation?

Day by day, businesses are becoming more reliant on data for decision-making and innovation, which in turn contributes to the growing need for Data-as-a-Service (DaaS). For instance, a research report published in 2021 estimated that between 2023 and 2028, the global market for DaaS will grow from $9.3 billion to $20.9 billion, illustrating the importance of gaining access to data without the headache of managing it.

This is entirely possible, given that DaaS allows accessibility, analysis, and usage of data without worrying about the complexities of managing infrastructure. Because businesses are looking for ever more efficient, flexible ways of handling their data, it therefore follows that the democratisation of data– the right of people in all organisation levels to access it— has exploded in recent times.

Now, employees in roles with different technical skill levels may access data to derive insights. As a result, there is a significant cultural shift towards a data-driven organisation that enables faster decisions, often accompanied by greater confidence, thereby improving the bottom line. Hence, with this achieved data democratisation, not only data scientists but also business analysts and even front-line employees can access and use data effectively.

Q. How do new, more complex data architectures like data fabric and data mesh make things easier?

According to the findings of some researchers, new data architectures like data fabric and data mesh are gaining more attention these days due to the increasing challenges posed by large volumes of data. According to a report by IDC, over 80% of the top 500 industrial enterprises will have an operational data fabric capability in place by 2026. There is no doubt that this trend largely reflects that organisations are slowly becoming aware of the usefulness of these modern solutions for easier data management and decision-making improvement.

To explain in detail, new data architectures like data fabric and data mesh are ensuring that data is easier and less cumbersome to handle. A data fabric connects a company’s data, whether stored on-premises or in the cloud, to a single layer that businesses will access and manage from one location. In contrast, a mesh will help in decentralising data ownership, giving different teams the ability to manage their own data; therefore, scaling up and flexibility become easier with fewer bottlenecks. The combination of these two data architectures helps break down data silos, makes data more accessible, and speeds up the process of making decisions. In short, they allow companies to manage their data in a more agile approach while still being efficient, helping them prosper while quickly reacting to market changes.

Q. Is synthetic data a promise for speeding up AI training while ensuring privacy?

Synthetic data has great potential for speeding up AI training while ensuring privacy. Instead of spending a lot of time, energy, and resources gathering real-life data, synthetic data is produced artificially to replicate real-life scenarios. As such, AI models can come to be trained with a reduced duration period, and hence the enterprises operate seamlessly with big and diversified databases, bypassing the usual delays.

Besides, data being synthetic means anonymity is guaranteed as no personal or sensitive information is involved, hence making it the safest option from a privacy perspective; thus, companies can develop AI without any privacy law infringements or regulatory constraints. The faster development of AI combined with enhanced privacy provides fertile ground for the growth of innovation for firms in the technological sector.

Q. Explain the growing importance of Generative AI in business operations and customer personalisation.

In business operations, Generative AI helps automate tasks, optimize processes, and predict trends, leading to more efficient workflows and smarter decision-making. By analysing large volumes of data, it can quickly generate content and solutions, helping businesses stay agile and innovative. Reports suggest that AI-driven personalisation can boost customer engagement by up to 25%, enabling companies to offer more tailored products and services.

In terms of customer personalisation, Generative AI excels by creating unique experiences based on individual preferences and behaviors. It can personalise everything from product recommendations to marketing messages, making interactions more relevant and meaningful. For example, Netflix uses sophisticated algorithms to recommend content tailored to the preferences of its 223 million subscribers, resulting in a staggering retention rate of 93%. This seamless integration of AI into customer interactions not only surprises and delights consumers but also fosters a deeper emotional connection, ultimately leading to brand loyalty.

Q. What are the strategies for fostering a collaborative, agile, and compliant data-driven environment?

Laying out a robust data governance strategy is a prerequisite for creating a collaborative, agile, and lawful data-driven environment, with access being permissible where data is protected or encrypted. The working structure of this would depend on cross-functional teams from various departments so that all their perspectives can be included. The standards established to judge the quality of data across the organisation are vital to ensure that data used from everywhere is secure and consistent.

Being agile means that you can change your processes if there is a need for it. You can adapt your own processes to suit your organisational needs and the changing demands of the law or other areas. Furthermore, organisations need to keep an eye on the regular auditing process and the constantly changing DPDP rules for the proper use of compliance and data protection while offering privacy by design in every data initiative.