Revolutionizing Remote Data Management: Powering the Next Generation of Flexibility

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Revolutionizing Remote Data Management: Powering the Next Generation of Flexibility

Workstations remain essential in industries requiring high computational power for tasks such as engineering, architecture, and data science. However, their usage is evolving from traditional desk-based setups to centralized data centers. This transition caters to users needing workstation performance and IT teams aiming to lower costs while ensuring security and productivity for a decentralized workforce. The rise of remote work has further emphasized the importance of reliable remote data management, which addresses longstanding concerns about security and productivity.

“Organizations increasingly want the power of a workstation along with cloud flexibility. Lenovo ThinkStation BMC achieves this, offering comprehensive out-of-band management for Lenovo ThinkStations in the data center, blending maximum performance with simple management.”

Manage Data Anywhere

To meet this demand, Lenovo has introduced ThinkStation BMC, the industry's first true add-in out-of-band management tool, available for its workstation products. This solution allows IT administrators to perform tasks like troubleshooting, configuration, and updates remotely, speeding up response times and minimizing business disruptions.

Lenovo ThinkStations featuring BMC are powered by Intel, including the ThinkStation P3 Ultra with fast Intel® Core™ processors and Intel vPro®, the ThinkStation P5 with Intel® Xeon® W-Series processors, the ThinkStation P7 with Intel® Xeon® W processors, and the ThinkStation PX with dual Intel® Xeon® Scalable processors.

For data center administrators already using BMC in their servers, ThinkStation BMC is the final step toward building a private workstation cloud. It reduces latency, boosts productivity, and cuts costs. ThinkStation BMC integrates with third-party IT management tools such as Redfish APIs, IPMI 2.0, and HTML5 Web UI.

Key Benefits of ThinkStation BMC:

• Monitoring: Users can monitor system health, log events, receive alerts, and access notifications, including legacy ThinkStation diagnostic codes. It also provides remote access to system logs and can capture Blue Screen of Death (BSOD) events. The system supports SNMP v2/v3 traps and captures video when triggered.

• Management: ThinkStation BMC enables remote firmware updates for BIOS and BMC, and allows control over power functions, including powering on/off, power cycling, hard resets, and ACPI shutdowns. It also features a virtual KVM console and supports serial-over-LAN for added control.

• Provisioning: Users can retrieve system inventory, deploy operating systems remotely, and access virtual media. It supports HTTP/HTTPS, NFS, CIFS protocols, and offers flexible network connectivity with dedicated or shared ports.

• Security: ThinkStation BMC offers FIPS 140-2 compliant encryption, certificate management, integration with directory services like Active Directory (AD) and LDAP, role-based authority, IP blocking, and signed image support for enhanced security.

“As more workstations are moved from desks to data centers or off-premises locations, the need for reliable remote management has surged,” said Marc Pamley, Workstation Chief Technologist at Lenovo. “We’ve listened to our customers and developed a BMC solution that delivers enterprise-level management for those who need it in their workstation environment.”

Maximizing GenAI Potential Through Effective Data Management

The emergence of generative AI (GenAI) marked a pivotal moment for enterprises seeking exponential growth through its transformative potential. However, enthusiasm for GenAI may be tempered by various challenges and risks related to scaling the technology. Since GenAI relies heavily on data, safeguarding customer trust and sensitive information is paramount, and businesses cannot ignore the associated risks.

The effectiveness and value of GenAI initiatives depend largely on data quality. To harness these data effectively, organizations need the right combination of skills, budget, and resources. Additionally, data must be housed in environments—whether in private or public clouds—that can meet both business and technical requirements.

Given these factors, it's increasingly important for business and IT teams to collaborate and align their AI priorities. Organizations must consider how to leverage AI to seize new opportunities, engage employees, and ensure secure access without compromising data integrity and regulatory compliance. Addressing these concerns at all levels of the business is essential.

A key first step in managing high-quality data and properly sizing AI initiatives is to define specific GenAI use cases. Depending on business needs, large language models (LLMs) may not always be necessary, as they are trained on vast amounts of text and are often designed for general use. LLMs may not be the most cost-effective option, as they are highly compute-intensive.

Alternatively, smaller models tailored to specific domains or enterprises may offer greater value at a lower cost while delivering more accurate, context-specific insights than LLMs.

Optimizing GenAI Through Data Management
As businesses navigate these challenges, they are exploring the best data management strategies for GenAI. Many are adopting a dual approach. The first involves experimenting with tactical deployments to better understand the technology and data use, a process known as data preparation. This short-term step involves identifying relevant data sets, defining data requirements, and ensuring data is cleansed, labeled, and anonymized. Data pipelines are also built to integrate the data into AI models.

This preparation phase should be coupled with a long-term strategy focused on GenAI use cases, such as content creation, digital assistants, and code generation. This phase, known as data engineering, includes setting up a data lake or lakehouse that integrates data with GenAI models. In addition to extending the capabilities of the GenAI data repository, such infrastructure enhances data management, establishing a strong foundation for GenAI.

Choosing the Right Infrastructure for Data
A critical decision for business leaders is selecting the right infrastructure to support their data management strategy. Computational needs, including the types of GenAI models, the number of users, and data storage requirements, will influence this choice.

Enterprises should consider a holistic, end-to-end approach to adopting and deploying GenAI, from the endpoint to the data center, by building a robust data operation. Dell Technologies, for example, offers Enterprise Data Management, which includes the Dell Data Lakehouse for AI. This platform, built on Dell’s AI-optimized hardware, includes a comprehensive software suite for discovering, querying, and processing enterprise data. It eliminates data silos and provides data teams with self-service access to create high-quality data products, accelerating AI-driven outcomes.

However, breakthrough AI innovations are only possible when the value of data is unlocked. Solutions like the Dell AI-Ready Data Platform are designed to help enterprises run AI at any scale, unlocking the value of unstructured data. This platform enables efficient access, preparation, training, and fine-tuning of AI models across on-premises, edge, or cloud environments, all from a single access point while maintaining peak performance.

Dell PowerScale further enhances AI innovation by offering a scalable, energy-efficient storage platform for high-performance AI workloads. This solution allows enterprises to gain rapid insights, leverage multicloud flexibility, and benefit from federal-grade security and storage efficiency. A successful example is McLaren Racing, which used AI to translate data into speed. By analyzing over 100,000 parameters from more than 300 onboard sensors in real time, McLaren boosted car performance and speed.

Hitachi Vantara Leads the Way in Helping Partners Harness the Power of Generative AI

The rise of generative AI (GenAI) offers enterprises a new way to automate and increase efficiency. However, without proper oversight, AI can become a risk, leading to loss of control over its impact.

Hitachi Vantara Promotes Data Best Practices For AI to be a valuable asset rather than a liability, managing and understanding data is crucial. Hitachi Vantara, with its proven expertise in data management, is now helping customers grasp the significance of AI. Many organizations are eager to adopt GenAI, but may not fully understand how to integrate it into their operations.

“You need a controllable environment,” said Greg Bucyk, VP of Partner Strategy, GTM, and Strategic Alliances at Hitachi Vantara in an interview with Channel Insider. He emphasized the need to determine which data to feed into AI engines, as they learn and improve over time. Organizations must also grasp how machine learning impacts their infrastructure, as AI generates additional data, raising questions about whether it replaces the original data or becomes the new reference point.

Bucyk also highlighted data security concerns, noting that increased data creation opens more opportunities for potential security risks. He stressed the importance of treating AI-generated data securely.

Bucyk advises caution, reminding organizations that rushing into new tools and technologies without proper planning can be costly. Clear goals and thorough implementation plans, including security protocols, are essential.

AI and Data Management Insights Hitachi Vantara is also leveraging GenAI to enhance customer experiences by using AI tools and chatbots to quickly resolve issues, analyzing data across multiple holdings simultaneously.

“We build scalable systems based on your requirements,” said Bucyk. He emphasized the importance of understanding infrastructure and data capabilities before scaling AI. Assessing whether the current infrastructure is secure and scalable is crucial before introducing more complexity.

Hitachi Vantara takes a well-rounded approach to AI adoption, recognizing that customers need to approach AI in stages—starting with basic steps before progressing to more advanced implementations.

To prepare its partners for AI’s growth, Hitachi Vantara focuses on education and collaboration, ensuring customers receive the desired outcomes by aligning with the right partners.

The Future of AI and GenAI AI is becoming more integrated into everyday life, with conversations around AI now reaching wider audiences, unlike earlier technologies like cloud computing. AI’s presence in day-to-day life means businesses will face increasing pressure to address how they manage and regulate its use.

Bucyk pointed out the need to evaluate the long-term relevance of data generated by AI, cautioning that endless data creation could become a liability. As machine learning accelerates data production, organizations must determine how to manage it effectively.

Bucyk emphasized the importance of the DMAIC approach—defining, measuring, analyzing, improving, and controlling—especially for data management, which plays a vital role in maintaining control over AI implementations.

He also noted that future AI strategies must include updated security protocols and compliance measures, especially as regulations like the Digital Operational Resilience Act (DORA) come into effect.

Education and collaboration with the right partners will be key to preparing for the future of AI and ensuring the successful adoption of emerging technologies.

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