July 25, 2024, Bangalore, India – Lenovo today unveiled the 2nd edition of its ‘Smarter Data Management Playbook’ report, capturing insights from 550+ CIOs and IT decision-makers across Asia Pacific, including 100+ in India. Commissioned by Lenovo, the IDC study delves into critical aspects of AI data management for 2024, encompassing data security, AI model integrity, data architecture and management, and data-driven enterprise IT strategy.
Sumir Bhatia, President, Lenovo ISG, AP, says “Data innovation is crucial for today’s CIOs to unlock new business opportunities and drive digital success. The playbook unveils data management complexity as the prime challenge for 29% of Asia Pacific enterprises. This represents a significant shift towards hybrid cloud environments tailored for AI. Lenovo’s open, reliable, and secure infrastructure empowers organizations to modernize their data strategies, ensuring agility and competitive advantage.”
The IDC survey highlights hybrid cloud as the preferred infrastructure for enterprise AI needs, shaped by each enterprise’s unique data architecture and strategy. Notably, 33% of Indian organizations are repurposing existing hybrid architectures for AI, while 22% are leveraging cloud vendors to host the AI infrastructure demands.
Amit Luthra, MD – India, Lenovo ISG, stated, “As highlighted in the Data Management Playbook, 2024 will be pivotal for organizations navigating the evolving landscape of data management. We observe a pronounced shift towards prioritizing data security and optimizing data for AI-driven insights and business outcomes. Lenovo ISG is committed to empowering our customers with agile, hybrid cloud solutions that streamline data management by up to 85%, enhancing operational efficiency. This underscores our continuous dedication to delivering tangible business value in an increasingly competitive market.”
Data Where You want it, How You Want it
29% of enterprises in the Asia Pacific region identify “Complexity of Data Management” as a major challenge in their organizational data initiatives, with India reporting one of the highest concerns at 30%. This highlights the critical need for robust data management solutions to streamline processes and drive efficiency across the region.
As a result, the top 3 priorities for Indian CIOs are data backup, analytical data stores, and data security. For Asia Pacific, data security tops the list followed closely by data preparation for AI projects and data architecture.
David Mooney, VP of Storage Sales – WW, Lenovo ISG, added, “As 30% of Asia Pacific organizations prioritize data security for 2024, Lenovo ISG reaffirms its commitment to delivering fast, simple, and secure data management solutions. Our ThinkSystem DG and DM Storage solutions, with built-in ransomware protection, safeguard critical business data against evolving threats. As we strive to be the world’s leading end-to-end infrastructure solutions provider, we deliver innovations that help customers manage and protect their data, enabling AI integration and business transformation.”
Top data quality concerns when using AI / GenAI
In 2024, the top 3 data quality concerns when using Gen AI for Indian CIOs are data manipulation, absence of data quality assurances and AI hallucination (misleading info generated by AI models). This contrasts with what we are seeing for CIOs across AP where AI training on false information and absence of data tags & data assurances are the top 3 concerns.
Similarly, the key limiting factors for use of AI/ Gen AI in organizations in India are:
- High infrastructure costs
- Lack of clear business case
- Concerns over Data security & control
For Asia Pacific the #1 factor is data security and control while the other two are exposure to brand or regulatory risk as well as data accuracy and its potential for unethical use.
Repurposing existing hybrid architectures for AI emerged as the top approach Indian companies will take to address AI infrastructure requirements with 33% CIOs favoring this.
David Mooney, VP of Storage Sales – WW, Lenovo ISG, noted, In today’s digital economy, data is the new currency, unlocking valuable insights that drive business growth and competitive edge. IDC’s research highlights the #1 data quality challenge in GenAI is training AI on false or undetected incorrect information. To tackle this, we deliver Smarter Data Management and Storage solutions for diverse workloads, from AI and analytics to data-intensive enterprise tasks, ensuring our customers have a robust and reliable data foundation.
Key AP Trends among CIOs Across BFSI, Telco, Manufacturing, Healthcare and Retail in 2024
| Vertical | View Data Complexity as key challenge | Top 3 Data Quality Concerns while using AI | CIOs Top Approaches for Addressing AI Infrastructure Needs |
| BFSI | 32% | · AI training on false information that is unknown or undetected
· Absence of data quality assurances · Data manipulation |
· 11% are building out new on-premise environments with dedicated compute, networking and storage
· 25% are building out a new hybrid cloud environment for AI · 36% are leveraging cloud vendors to host the AI infrastructure demands · 17% are repurposing existing on-premise technology for AI · 11% are repurposing existing hybrid architectures for AI |
| Manufacturing | 30% | · AI training on false information that is unknown or undetected
· Data manipulation · Absence of data quality assurances |
· 20% are building out new on-premise environments with dedicated compute, networking and storage
· 23% are building out a new hybrid cloud environment for AI · 34% are leveraging cloud vendors to host the AI infrastructure demands · 14% are repurposing existing on-premise technology for AI · 9% are repurposing existing hybrid architectures for AI |
| Retail | 31% | · Data manipulation
· AI training on false information that is unknown or undetected · Inaccurate or absent code documentation |
· 11% are building out new on-premise environments with dedicated compute, networking and storage
· 34% are building out a new hybrid cloud environment for AI · 36% are leveraging cloud vendors to host the AI infrastructure demands · 13% are repurposing existing on-premise technology for AI · 7% are repurposing existing hybrid architectures for AI |
| Telco | 26% | · Data bias
· AI training on false or incorrect information that is unknown or undetected · Data manipulation |
· 8% are building out new on-premise environments with dedicated compute, networking and storage
· 28% are building out a new hybrid cloud environment for AI · 38% are leveraging cloud vendors to host the AI infrastructure demands · 12% are repurposing existing on-premise technology for AI · 13% are repurposing existing hybrid architectures for AI |
| Healthcare | 42% | · Absence of data quality assurances
· AI training on false information that is unknown or undetected · Data manipulation |
· 15% are building out new on-premise environments with dedicated compute, networking and storage
· 24% are building out a new hybrid cloud environment for AI · 30% are leveraging cloud vendors to host the AI infrastructure demands · 14% are repurposing existing on-premise technology for AI · 17% are repurposing existing hybrid architectures for AI |
Lenovo’s Data Management solutions span systems, platforms, and software, optimizing data management from edge to core data centers and across cloud deployments. Leveraging block, file, and object data, Lenovo enables actionable insights and enhances application performance. Their solutions support seamless data mobility across diverse IT infrastructures, empowering next-gen data and AI/ML applications.