Robust Database Infra is the Bedrock of Successful AI Initiatives

By: Abhijit Banerjee, Managing Director – India and SAARC, SolarWinds

When you are walking through a city like Mumbai or Bengaluru, it is only natural to look up and admire the skyline. Towering skyscrapers seem like impossible feats of engineering. Yet, the true marvel lies beneath the ground, where a robust foundation supports these colossal structures. This analogy mirrors the role of database infrastructure in artificial intelligence (AI) systems, particularly large language models (LLMs) and generative AI (GenAI) that process extensive datasets for tasks like language processing, image recognition, and predictive analysis. Just as skyscrapers require a solid base to remain stable, AI systems depend on a strong database infrastructure for efficiency, reliability, and scalability.

The importance of robust database infrastructure is even more pronounced in India, where digital transformation is rapidly accelerating. According to a report by Intel commissioned from IDC, Al infrastructure spending is expected to reach US$733 million by 2027, placing India at the forefront of Al adoption. With such promising trends in a bustling tech industry and a vast population generating immense data, Indian enterprises must prioritise solid database foundations to support their AI initiatives. Database professionals and IT leaders must maintain performance and scalability while ensuring data security and reliability in a challenging yet opportune landscape.

Scalability: To reach new heights

The design of any of the world’s modern architectural marvels accounts not only for current needs but also for future expansions and modifications. Similarly, database infrastructure for AI must be scalable to handle increasing data loads and complexity. India accounts for a staggering 20 percent of the world’s data, increasing by the minute. It is a country where data from diverse sources—from e-commerce and social media to government services—continually grows, and scalable databases are essential. 

Scalable infrastructure is achieved through cloud-based solutions and techniques like data sharding and partitioning. In cloud computing, scalability is achieved through two strategies: scaling up and scaling out. Scaling up enhances the capacity of existing infrastructure while scaling out adds more servers or nodes to expand capacity. These methods help ensure the even distribution of workloads, preventing bottlenecks and enabling smooth handling of growing data demands.

Data quality: Ensuring integrity and accuracy 

Data is the backbone of every modern enterprise, and its quality and integrity are as essential as the steel frameworks that help skyscrapers withstand any weight or weather. An AI’s performance directly depends on the data quality it is trained on. In our country, this can vary significantly due to diverse sources and collection methods. Therefore, companies must continuously commit to updating and maintaining their databases to ensure they are accurate, consistent, and up-to-date. 

Take the example of the Indian government’s digital initiatives like Aadhaar. The success of such large-scale projects hinges on the integrity and accuracy of data. Similarly, for AI applications, especially in sensitive areas like financial services or public health, maintaining high data quality helps ensure the systems provide accurate, reliable outputs, fostering user trust.

Performance optimisation: To keep the lights on

Imagine the inconvenience that would occur if the elevators in a skyscraper stopped working or if the power supply was erratic. Just as these systems need regular maintenance to ensure smooth operation, databases underpinning AI require constant performance optimisation. Database efficiency is critical in the dynamic tech hubs of India, where startups and enterprises push the boundaries of innovation. Optimisation involves regularly monitoring and tuning databases to address slow queries and resource bottlenecks. Companies can ensure their AI applications remain responsive and effective by optimising performance.

Security measures: The foundation of trust

Indian enterprises, from banking to e-commerce, handle vast amounts of sensitive data. Ensuring the security of this data is foundational to building trust and encouraging the adoption of AI technologies.

Robust cybersecurity measures such as encryption, security by design principles, multi-factor authentication, and regular security audits are essential. These measures help protect data integrity and privacy, allowing AI systems to operate within ethical standards and regulatory frameworks. By prioritising security at every layer of their infrastructure—from monitoring to maintenance and everything in between—organisations can ensure that their AI systems are trusted sanctuaries for valuable data.

Conclusion

When developers and users feel confident in the security of AI systems, they are more likely to experiment and push the boundaries of what these technologies can achieve. In India, where the digital economy is expanding rapidly, fostering such trust is crucial for driving innovation and maintaining global competitiveness. We must continue to build and manage these critical foundations with diligence and foresight. That way, we can ensure our AI systems remain reliable, effective, and capable of reaching their full potential. We can ensure our AI systems soar to new heights and deliver transformative solutions by fortifying our database infrastructures.