Data Infrastructure Modernization in the Digital Era
Sanjay Agrawal brings over 30+ years of industry ex- perience. In this role, Sanjay is responsible for the tech- nical direction at Hitachi Vantara and leads a team of solution consultants and technical experts in India to help customers focus on solutions and initiatives around Digital Transformation. His team is responsible for developing solutions in Analytics, IOT, Content Platform, Public Safety Solutions, Enterprise Storage and Server solutions. Furthermore, Sanjay leads a team that is also responsible for Business Development and Product Spe- cialization.
In conversation with Charulatha,Correspondent,Siliconindia Magazine. Sanjay shares his views about the driving strategic initiatives and primary business objectives for the modernization of technology infrastructure and data infrastructure upgrade.
The driving strategic initiatives and primary business objectives for the modernization of technology infrastructure and data infrastructure upgrade
Enterprises actively pursue digital transformation, emphasizing outstanding customer exp- eriences, streamlined automation, and innovative business models. Data is pivotal, providing a comprehensive understanding of customers for tailored services. In sectors like banking, data encompasses sentiments, interactions, online behavior, and preferences. Data analysis optimizes operations, reducing latencies. Enterprises tap into external sources, particularly social media, for insights, driving new business models, and committing to value throughout the life cycle. Business agility, reliant on IT infrastructure, is crucial for com- petitiveness.
CIOs prioritize modernizing IT infrastructure, emphasizing automation for agility and innovation. Data's centrality to digital transformation makes modernizing data infrastructure paramount. IT modernization includes consolidation, automation, and AI/ML-based management. Integration of converged and hyper-converged systems enhances data center efficiencies, with storage guaranteeing sub-50 microsecond data access.
Modernizing data infrastructure necessitates a unique approach, given the scale of data, requires a specialized framework known as Big Data, led by object storage technology. Strategic data modernization aligns stakeholders, fostering a DataOps culture, widespread machine learning adoption, and organized data for insights. A tailored, strategic approach is crucial, aligning diverse stakeholders on business drivers and objectives for data ecosystem modernization.
Data infrastructure with industry best practices and standards, and the challenges are typically faced during the implementation of modern data infrastructure solutions
Efficient data infrastructure deployment is crucial for enterprises to prevent silos that should ensure seamless access from diverse hosts and interoperability with various setups, avoiding isolated storage. Storage virtualization enables unified management of multi-vendor setups for business applications. Sustainability efforts, including deduplication and compression, aim to reduce data and infrastructure footprints. The adoption of NVMe standards with SSDs significantly improves data access.
AI-driven IT Operations Management (ITOMs) is gaining prominence. However, vendor lock-in poses a notable challenge, leading to isolated data infrastructure which introduces com- plexities and underutilization. Data infrastructure virtualization and consolidation emerge as strategies to eliminate isolated pockets that must support distributed cloud strategies, evolve workloads, and optimize operations for cost efficiency. Acknowledging potential disruptions from increasing digitization, data growth, and transitioning to distributed cloud is crucial. Implementing data modernization aids in cloud migration challenges.
"Businesses are increasingly utilizing AI analytics, including predictive analytics and machine learning, to boost customer satisfaction, streamline operations, and strengthen their competitive edge"
Specific technologies or tools have been put into use for the enhancement of data management and analytics capabilities
Big data is a cornerstone in expediting digital transformation. Traditional technologies fall short in managing their complexities, driving a surge in adopting cloud storage, especially Object storage. For structured business data, NVMe with SSDs is now standard. The contemporary approach heavily leverages AI/ML for optimal results. Implementing an AIOPS-based strategy streamlines processes like anomaly detection, root cause analysis, automation, and predictive analytics.
A diverse range of technologies and tools enhance proficiency in data management and analytics. IT automation tools are invaluable for streamlining workflows. Simultaneously, data management tools aid enterprises in organizing and overseeing their data effectively. Intelligent metadata plays a crucial role in autonomously enhancing incoming data, simplifying analysis, and insight derivation. Moreover, businesses are increasingly utilizing AI analytics, including predictive analytics and machine learning, to boost customer satisfaction, streamline operations, and strengthen their competitive edge. It's essential to highlight that the choice of technologies and tools hinges on each organization's unique requirements and objectives.
Security and compliance factors play a role in your data infrastructure upgrade projects
In data management, safeguarding against threats remains a primary concern for CIOs. Their commitment to robust tools and best practices forms the core of their approach. The rise of ransomware adds complexity, leading to operational disruptions, reputational damage, and financial concerns. Global regulatory bodies now emphasize stringent Recovery Time Obj- ectives. To counteract these threats, organizations turn to specialized technologies like data volume snapshots, coupled with effective locking, versioning, and Digital Twin-based approaches.
Enterprises prioritize native capabilities for compliance management, including Write Once Read Many (WORM), Legal Hold, Data Retention, and Lifecycle Management. These are pivotal factors in procuring or upgrading data infrastructure, especially with the challenges of big data.
Given the surge in ransomware and tighter regulations, coupled with heightened market competition, safeguarding and extracting maximum value from assets is crucial. Object storage gains traction, especially in heavily regulated environments. Compliance requires effective storage and management of vast data volumes, often beyond the capacities of outdated systems.
Strategies do the organization employ to stay at the forefront of emerging trends and innovations in data infrastructure management
Enterprises are constructing versatile data platforms to manage diverse data types from various sources. The aim is streamlined storage, security, and efficient management. An AIOPs-based approach is crucial, particularly for rapidly expanding unstructured data. A scalable platform with native management and compliance features is essential for modernizing data infrastructure. Post-modernization, efforts concentrate on refining data quality, utilizing a metadata-centric approach for informed decisions. Integrating structured and unstructured data offers a comprehensive customer view, enabling prioritization of customer experiences, streamlined operations, and optimized data models for an accelerated digital transformation.
In dynamic data management, an organization's path is guided by its goals and resources. Established practices, like R&D allocation and industry engagement, prove effective. Cultivating a culture of creativity drives adaptability and agility in data infrastructure management. This strategic approach empowers enterprises to excel in the evolving landscape of data mana- gement.
In conversation with Charulatha,Correspondent,Siliconindia Magazine. Sanjay shares his views about the driving strategic initiatives and primary business objectives for the modernization of technology infrastructure and data infrastructure upgrade.
Modernizing data infrastructure necessitates a unique approach, given the scale of data, requires a specialized framework known as Big Data, led by object storage technology
The driving strategic initiatives and primary business objectives for the modernization of technology infrastructure and data infrastructure upgrade
Enterprises actively pursue digital transformation, emphasizing outstanding customer exp- eriences, streamlined automation, and innovative business models. Data is pivotal, providing a comprehensive understanding of customers for tailored services. In sectors like banking, data encompasses sentiments, interactions, online behavior, and preferences. Data analysis optimizes operations, reducing latencies. Enterprises tap into external sources, particularly social media, for insights, driving new business models, and committing to value throughout the life cycle. Business agility, reliant on IT infrastructure, is crucial for com- petitiveness.
CIOs prioritize modernizing IT infrastructure, emphasizing automation for agility and innovation. Data's centrality to digital transformation makes modernizing data infrastructure paramount. IT modernization includes consolidation, automation, and AI/ML-based management. Integration of converged and hyper-converged systems enhances data center efficiencies, with storage guaranteeing sub-50 microsecond data access.
Modernizing data infrastructure necessitates a unique approach, given the scale of data, requires a specialized framework known as Big Data, led by object storage technology. Strategic data modernization aligns stakeholders, fostering a DataOps culture, widespread machine learning adoption, and organized data for insights. A tailored, strategic approach is crucial, aligning diverse stakeholders on business drivers and objectives for data ecosystem modernization.
Data infrastructure with industry best practices and standards, and the challenges are typically faced during the implementation of modern data infrastructure solutions
Efficient data infrastructure deployment is crucial for enterprises to prevent silos that should ensure seamless access from diverse hosts and interoperability with various setups, avoiding isolated storage. Storage virtualization enables unified management of multi-vendor setups for business applications. Sustainability efforts, including deduplication and compression, aim to reduce data and infrastructure footprints. The adoption of NVMe standards with SSDs significantly improves data access.
AI-driven IT Operations Management (ITOMs) is gaining prominence. However, vendor lock-in poses a notable challenge, leading to isolated data infrastructure which introduces com- plexities and underutilization. Data infrastructure virtualization and consolidation emerge as strategies to eliminate isolated pockets that must support distributed cloud strategies, evolve workloads, and optimize operations for cost efficiency. Acknowledging potential disruptions from increasing digitization, data growth, and transitioning to distributed cloud is crucial. Implementing data modernization aids in cloud migration challenges.
"Businesses are increasingly utilizing AI analytics, including predictive analytics and machine learning, to boost customer satisfaction, streamline operations, and strengthen their competitive edge"
Specific technologies or tools have been put into use for the enhancement of data management and analytics capabilities
Big data is a cornerstone in expediting digital transformation. Traditional technologies fall short in managing their complexities, driving a surge in adopting cloud storage, especially Object storage. For structured business data, NVMe with SSDs is now standard. The contemporary approach heavily leverages AI/ML for optimal results. Implementing an AIOPS-based strategy streamlines processes like anomaly detection, root cause analysis, automation, and predictive analytics.
A diverse range of technologies and tools enhance proficiency in data management and analytics. IT automation tools are invaluable for streamlining workflows. Simultaneously, data management tools aid enterprises in organizing and overseeing their data effectively. Intelligent metadata plays a crucial role in autonomously enhancing incoming data, simplifying analysis, and insight derivation. Moreover, businesses are increasingly utilizing AI analytics, including predictive analytics and machine learning, to boost customer satisfaction, streamline operations, and strengthen their competitive edge. It's essential to highlight that the choice of technologies and tools hinges on each organization's unique requirements and objectives.
Security and compliance factors play a role in your data infrastructure upgrade projects
In data management, safeguarding against threats remains a primary concern for CIOs. Their commitment to robust tools and best practices forms the core of their approach. The rise of ransomware adds complexity, leading to operational disruptions, reputational damage, and financial concerns. Global regulatory bodies now emphasize stringent Recovery Time Obj- ectives. To counteract these threats, organizations turn to specialized technologies like data volume snapshots, coupled with effective locking, versioning, and Digital Twin-based approaches.
Enterprises prioritize native capabilities for compliance management, including Write Once Read Many (WORM), Legal Hold, Data Retention, and Lifecycle Management. These are pivotal factors in procuring or upgrading data infrastructure, especially with the challenges of big data.
Given the surge in ransomware and tighter regulations, coupled with heightened market competition, safeguarding and extracting maximum value from assets is crucial. Object storage gains traction, especially in heavily regulated environments. Compliance requires effective storage and management of vast data volumes, often beyond the capacities of outdated systems.
Strategies do the organization employ to stay at the forefront of emerging trends and innovations in data infrastructure management
Enterprises are constructing versatile data platforms to manage diverse data types from various sources. The aim is streamlined storage, security, and efficient management. An AIOPs-based approach is crucial, particularly for rapidly expanding unstructured data. A scalable platform with native management and compliance features is essential for modernizing data infrastructure. Post-modernization, efforts concentrate on refining data quality, utilizing a metadata-centric approach for informed decisions. Integrating structured and unstructured data offers a comprehensive customer view, enabling prioritization of customer experiences, streamlined operations, and optimized data models for an accelerated digital transformation.
In dynamic data management, an organization's path is guided by its goals and resources. Established practices, like R&D allocation and industry engagement, prove effective. Cultivating a culture of creativity drives adaptability and agility in data infrastructure management. This strategic approach empowers enterprises to excel in the evolving landscape of data mana- gement.
