An AI Q and A

with Sasan Moaveni, Global Business Lead – AI & High Performance Data Platforms, Hitachi Vantara.

  • 1 month ago Posted in

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

The data infrastructure landscape across various industries is set to experience pivotal changes in 2024, primarily driven by groundbreaking advancements in AI and machine learning. A central trend will be the integration of the Internet of Things (IoT), data analytics, and artificial intelligence and its significant impacts on existing infrastructure environments. This fusion is also expected to trigger a substantial increase in demand for specialised knowledge to address the added strain being placed on data infrastructure.

This expertise will be crucial for maintaining the precision, explicability, and traceability of applications that emerge from the seamless integration of IoT, data, and AI. Artificial intelligence plays a critical role in the analysis of extensive datasets, including recognising patterns, signatures, and user behaviours. Furthermore, AI aids in detecting anomalies and threats, facilitates automated reactions, and enhances workflow efficiency. The capabilities of AI are also expanding due to its increasing computational power, applicable across supervised, semi-supervised, and unsupervised learning domains.

The development of edge computing also marks a significant advancement by addressing security issues and boosting energy efficiency. With edge computing, data is processed locally or closer to its source, minimizing potential vulnerabilities that may come as a result of long-distance data transfers. This localised data processing model can also enable a reduction in bandwidth usage, since less data is sent to the cloud, which in turn can result in energy savings and data being processed more quickly.

This year, we are also expected to witness a pivotal shift towards a unified data ecosystem. This evolution aims to integrate smoothly with existing infrastructures, offering comprehensive visibility and interoperability. Such enhancements are set to enable faster insights and more agile responses to market demands.

These developments collectively suggest a future where data infrastructure is increasingly integrated, secure, and efficient. With AI and machine learning leading the way, these trends are sculpting the technological landscape across a range of diverse industries.

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

The Data Infrastructure and Analytics industry is spearheading innovation in essential sectors such as manufacturing, healthcare, and finance. In the manufacturing realm, data infrastructure and analytics enable smart manufacturing and predictive maintenance through real-time insights gathered from IoT devices. This intelligence streamlines production processes and improves supply chain management by offering operational insights that adapt to dynamic conditions, achieved through comprehensive end-to-end solutions that incorporate data from machinery and sensors, along with advanced connectivity, predictive analytics, and AI. Such capabilities not only reduce operating costs but also minimise downtime, enhance asset utilisation, and boost overall productivity, showcasing the industry's role in driving growth and efficiency across various sectors.

In healthcare, sophisticated analytics are being utilised to draw insights for personalized medical insights from extensive patient data, thereby enhancing clinical decisions and advancing medical research. The sector is moving towards a comprehensive Patient360 view, which merges individual customer profiles with detailed medical records like MRIs, CT scans, and pathology reports. This holistic approach provides doctors with crucial information, enabling quick, accurate diagnoses and the formulation of effective treatment plans. AI is crucial in the analysis of health records, playing a significant role in refining patient care.

Finally in the finance sector, the emphasis is on leveraging machine learning for risk management, fraud detection, and real-time data analysis, which supports sound decision-making and helps prevent fraud. Banks especially are adopting a more refined Customer360 approach that extends beyond basic product and channel data to include customer sentiments, interactions, online behaviour, and preferences. This enriched understanding allows banks to tailor their strategies for market outreach, customer acquisition, and promotions, all driven by artificial intelligence.

What are the key issues preventing the scalability of data infrastructure, and what solutions do you think will play a part in overcoming these hurdles?

AI's primary contribution is centred around big data, particularly in managing the surge in predominantly unstructured data, which is growing at an exponential rate when compared to structured data. This development has notably shifted the challenges related to data infrastructure. To manage the immense data volumes that can reach into 10s or 100s of petabytes, there is a critical need for an innovative data infrastructure that can scale horizontally. Such infrastructures should integrate essential features like data integrity, compliance, and governance directly at the platform level. This transformation allows organisations to shift their focus from routine operations and management tasks towards driving innovation.

Organisations are facing common challenges such as data security and the complexities of integration. A strategic approach includes implementing advanced security protocols, investing in robust data integration platforms to simplify the management of diverse data sources and formats, and exploring cloud and edge computing solutions as well as cost-effective infrastructure.

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