Adapting or Obsolete: The Rapid Evolution of Tech in Business

In a conversation with Sanjay Agrawal, Head of Presales and CTO at Hitachi Vantara, we delve into how technology has transformed businesses, strategies for data monetization, the role of technology in sustainability, practical applications of AI, and the rise of Edge computing in India and SAARC.

In today’s digital era, businesses are compelled to stay at the forefront of technological innovation to remain competitive. The rapid evolution of the tech industry, exemplified by staggering statistics such as the projected US$623.3 billion global cloud computing market and the expected 463 exabytes of daily data creation by 2025, underscores the urgency for adaptation and thriving. Excerpts from the interview…

With over 30 years of experience in the industry, how have you witnessed the landscape of technology and data management evolve over the years? What emerging trends do you believe will have the most significant impact on businesses in the coming years?

Over the past few decades, the IT industry has undergone a remarkable transformation. Today, IT isn’t merely a business enabler; it’s a business accelerator. We’ve shifted from monolithic applications to microservices, which has brought about increased agility and minimized application and business downtime. The focus has shifted from data management to data modernization and eventual monetization.

Digital transformation is set to accelerate further with the adoption of Artificial Intelligence (AI) and Machine Learning (ML) following the initial advances in technologies like Social, Mobility, Analytics, Cloud, and IoT (SMACi). Unstructured data, generated by both machines and humans, has gained significance, providing businesses with deeper insights into their customers.

Cloud technologies have empowered enterprises with agility and innovation, allowing them to shift their focus from IT operations to business innovation. Hybrid cloud, combining the strengths of private and public clouds, has become a preferred model. Meanwhile, data center technologies have transitioned from traditional infrastructure to more efficient and manageable converged and hyperconverged infrastructure, providing a competitive edge to businesses.

As an expert on data monetization, how can businesses effectively harness the value of their data assets? Are there specific strategies or best practices you recommend for organizations looking to capitalize on their data?

Today’s data strategy goes beyond traditional structured business data, as it now includes vast amounts of unstructured data, often referred to as big data. This shift necessitates new strategies across data platforms, management, discovery, and analysis.

Technologies like object storage are reshaping data platform standards for big data, offering native capabilities for efficient data management. One key approach to data modernization involves associating metadata with objects, which facilitates data discovery for timely decision-making. Data blending, which combines various data sources, enables enterprises to gain a 360-degree view of their customers.

This comprehensive customer view empowers businesses to redefine their Go to Market (GTM) strategies, customer acquisition and retention plans, product positioning, and more, ultimately expediting data monetization.

Sustainability is a pressing concern today. Could you share some insights into how technology can play a pivotal role in helping organisations reduce their environmental footprint and operate more sustainably? What are some innovative approaches in this area?

Sustainability is becoming one of the key areas for enterprises. Data centres are one of the major sources of greenhouse emissions globally. They are conspicuous for energy consumption and lead to a large carbon footprint. The constant need for cooling, powering servers and maintaining redundancy creates a significant demand for electricity. Action must be taken at every stage of the process to reduce the environmental impact of products and services.

The focus is on the procurement of raw materials, production, transportation, the use and maintenance, waste disposal and recycling during the product lifecycle. Various lifecycle stages are captured as follows –

Comprehensive Product Lifecycle Assessment: The major environmental impact is during the use phase, and the immediate focus is on operations and usage to reduce the carbon footprint without ignoring other phases. Key strategies involve the enhancement of energy efficiency, the adoption of renewable energy sources, the optimisation of cooling infrastructure and the implementation of sophisticated data centre management practices. These measures help in mitigating the carbon footprint associated with data centres.

Various initiatives taken at the data centre level include: Smarter DC (Data Centre) Management: Data Operations is the nodal point where IT and building facilities converge for operational excellence. The integration through Data Centre Infrastructure Management (DCIM) solutions fosters efficiency, reliability and sustainability within data centre operations.

Smarter IT Infrastructure Management: It helps reduce GHG (greenhouse gas) emissions by reducing the IT infrastructure footprint and improving the overall system use. Technologies that improve efficiency are -virtualisation and workload consolidation in IT systems. Virtualisation of storage arrays also helps use the free capacity across different systems, which is otherwise not possible.

Smarter Data Management: The objective is to reduce the data footprint, which leads to reduced DC Infrastructure, and hence reduced GHG emissions. Data reduction technologies are adopted while storing the data in the data centre. Also, extending the data storage life by delaying the entire storage array refresh or replacement, and non-disruptively replacing just the storage controller to next-generation systems without the need for data migration, helps reduce the GHG emissions. If there is a full refresh or replacement, old and new systems run in parallel throughout the migration phase. It consumes a lot of power and requires additional cooling and floorspace, also taking months to complete.

Smarter Data Governance: Here, the focus is to reduce the data footprint further by taking some action. We must try and store the data that is a ‘must-have’ and avoid redundant and secondary data that can easily be regenerated from originally sourced data. Faster computing today and in-memory processing enables enterprises to generate reports or aggregated data quickly. These reports may not need to be stored for long periods. Efforts must be made to reduce the data copies. Retention policies must be revisited as per the latest compliance to explore the possibility of data retention periods. Regular dark data and files assessment can help reduce the data footprint by dropping the data that is either redundant or not contributing to the business.

AI is a catalyst for sustainability. It drives innovation in resource management, reduces emissions and helps in environmental monitoring. It optimises energy consumption, enhances waste management and aids in climate modelling. AI-driven solutions empower industries to make data-informed decisions that reduce their ecological footprint.

With your background in AI, what are some of its practical applications in the IT space that you believe are helping transform IT and business in India and SAARC? Are there any unique challenges or opportunities that these regions present in adopting AI technologies?

AI has emerged as a result-oriented initiative to help businesses, particularly in the realm of Information Technology. AI plays a pivotal role in optimizing IT resource provisioning, dynamically allocating resources from underutilized systems to improve efficiency and performance.

In IT operations management (ITOM), AIOPs (Artificial Intelligence for IT Operations) enhances efficiency and optimization. For data-driven enterprises, the challenge lies in quickly making data available for analysis and decision-making. AI aids in rapidly populating data catalogs, enabling organizations to put data to use efficiently.

Edge computing is gaining momentum as a critical technology for real-time data processing. How do you see Edge computing shaping the future of industries in India and SAARC? What are some practical use cases where it’s making a significant impact?

Traditionally, insights and decision-making in enterprises have been centered on data centers. However, to accelerate operational insights and real-time decision-making, industries are adopting edge computing and AI-based decision-making at the Edge. This approach benefits customer-facing enterprises by delivering improved customer experiences and operational efficiency.

In industrial settings, where various machinery across value chains can lead to high operational costs and production delays, Edge processing and analytics are instrumental in minimizing downtime and enhancing customer experiences. Edge computing blends machine-generated Internet of Things (IoT) data with contextualized data to feed predictive models, resulting in improved operational efficiency and customer satisfaction.

With the increasing adoption of cloud technologies, success depends on how enterprises ensure the right mix of different cloud environments for their workloads. What are some of the best practices and strategies that enterprises must consider to ensure their desired outcome when transitioning to the cloud or implementing hybrid cloud solutions?

The emergence of the cloud has introduced various deployment options for enterprises, each offering unique advantages. Enterprises should carefully select the right cloud environments for their workloads to ensure success. The selection should consider workload characteristics, utilization patterns, and data access requirements.

For example, workloads with flat utilization may benefit from an on-premises approach, while those with occasional peaks may be more cost-effective in the public cloud. Workload characteristics, data access patterns, and business goals should drive the decision-making process. Enterprises often opt for hybrid cloud models for their flexibility in switching between on-premises and cloud resources based on business needs.

By making informed choices about cloud deployment, businesses can enhance their agility, efficiency, and innovation, ultimately achieving their desired outcomes.

Sanjay Agrawal

Head, Presales and CTO, Hitachi Vantara

minus@cybermedia.co.in