Evolution of Data Infrastructure: AI and Emerging Trends

Sanjay Agrawal, a visionary Head of Presales & CTO at Hitachi Vantara (India & SAARC), spearheads transformative digital initiatives. Sanjay brings over three decades of leadership expertise in digital transformation and data monetization strategies for enterprises. He champions transformative Hybrid Cloud journeys and team evolution, elevating Hitachi Vantara's prominence in the digital sphere. In an interview with Thiruamuthan, correspondent for Industry Outlook Magazine, Sanjay depicted the evolution of data infrastructure and how AI/ML is driving other industries such as healthcare, finance, and manufacturing.

What pivotal trends do you see shaping data infrastructure across industries, particularly concerning AI/ML advancements?

In 2024, the landscape of data infrastructure across industries is expected to undergo significant transformations and will be majorly driven by influential trends, particularly within AI/ML advancements. One key trend is the convergence of IoT, data, and AI, which is anticipated to lead to a heightened demand for domain expertise.

This surge in knowledge is essential to ensure the accuracy, explainability, and traceability of applications resulting from seamless IoT, data, and AI integration. AI significantly contributes to analyzing large data sets, patterns and signatures, and user behaviour. It also helps identify anomalies and threats, enables automated responses, and streamlines workflows. AI contributions stem from its ever-increasing computational power in supervised, semi-supervised, and unsupervised learning.

The evolution of edge computing is also noteworthy, particularly in emerging cloud computing sectors, as it addresses security concerns and enhances energy efficiency. Additionally, there

is an anticipated transformative shift towards a unified data ecosystem in 2024. This move is expected to seamlessly integrate into existing infrastructure, providing complete visibility and interoperability. Such integration promises quicker insights and agile responses to market needs. 

These collective trends indicate a future where data infrastructure becomes more integrated, efficient, and secure. With AI/ML advancements at the forefront, these trends are influencing innovation and shaping the technological landscape across various industries.

How is the Data Infrastructure and Analytics industry driving innovation in sectors like healthcare, finance, or manufacturing?

The Data Infrastructure and Analytics industry is driving innovation in critical healthcare, finance, and manufacturing sectors. In healthcare, advanced analytics extract personalized medicine insights from extensive patient data, refining clinical decisions and supporting expansive medical research. The industry is actively seeking a Patient360 perspective, which can be achieved by integrating customer profiles with medical records such as CT scans, MRIs, pathology reports, etc. This integrated view is invaluable to doctors, facilitating swift and precise diagnoses that identify the most effective treatment plans. Artificial intelligence plays a pivotal role in discovering, analyzing, and diagnosing health records, contributing significantly to improved patient care.

The finance industry plays a pivotal role in risk management, fraud detection, and real-time data analysis with machine learning, ensuring informed decision-making and fraud prevention. In the sector, particularly within banks, there is a growing inclination to develop an enhanced Customer360 perspective. This goes beyond product and channel considerations, incorporating sentiments, interactions, online behaviour, preferences, and more. This comprehensive approach enables banks to gain a deeper understanding of their customers, subsequently reshaping strategies related to market outreach, customer acquisition, and promotional offerings, all facilitated by artificial intelligence.

In manufacturing, the industry's data infrastructure and analytics propel smart manufacturing and predictive maintenance through real-time insights from IoT devices, optimizing production efficiency and enhancing supply chain management. Manufacturers can harness operational insights that maximize production, regardless of ever-changing factors, by implementing end-to-end solutions that leverage data from machines and sensors, ubiquitous connectivity, sophisticated predictive analytics, and AI. This level of intelligence helps reduce operating overhead and cost through more accurate forecasting and optimal resource usage. Furthermore, AI has been instrumental in the manufacturing industry to achieve a comprehensive 360-degree perspective throughout the entire plant, thereby reducing downtime and enhancing asset utilization and productivity. These examples highlight the industry's transformative impact, fostering growth and efficiency across sectors.

In your view, what are the key challenges hindering the scalability of data infrastructure, and what solutions hold promise for overcoming these hurdles?

The primary contribution of AI lies in the realm of big data, particularly in dealing with predominantly unstructured data, which is experiencing substantial exponential growth compared to structured data. Consequently, the challenges associated with data infrastructure have evolved significantly. Addressing vast data volumes reaching petabytes necessitates an innovative data infrastructure that scales horizontally and incorporates essential native data management features, including data integrity, compliance, governance, and more, at the platform level. This shift enables enterprises to redirect their focus from routine operations and management to fostering innovation.

Other common challenges we grapple with are data security, integration complexities, etc. We aim to have a strategic approach that involves implementing advanced security measures, investing in robust data integration platforms, exploring cloud and edge computing, and considering cost-effective infrastructure options.

Safeguarding the growing volume of data requires stringent security protocols. Managing diverse data sources and formats is streamlined with our investment in data integration platforms. To meet processing demands as data volumes increase, we are exploring the adoption of cloud and edge computing. 

What emerging trends in artificial intelligence and machine learning are poised to significantly influence the landscape of Data Infrastructure and Analytics in the near future?

In 2024, pivotal trends are shaping the data infrastructure landscape across industries, mainly driven by advancements in AI/ML. Let's delve into these influential trends:

Firstly, IoT, data, and AI convergence is rising, demanding domain expertise for accuracy, explainability, and traceability in applications. As per our recent predictions report, quantum computing is gaining prominence, with potential solutions expected to emerge through cloud platforms. The evolution of edge computing is notable in emerging sectors, addressing security concerns and enhancing energy efficiency.

Anticipated in the coming year is a transformative shift towards a unified data ecosystem seamlessly integrating into existing infrastructure. This promises complete visibility and interoperability, facilitating quicker insights and agile responses to market needs.

Collectively, these trends indicate a future where data infrastructure becomes more integrated, efficient, and secure. With AI/ML advancements at the forefront, they influence innovation and shape the technological landscape across various industries.