Is Your Data Storage Slowing You Down? - Hitachi Vantara Federal

In today’s digital age, the volume of data continues to surge at an unprecedented pace. From individual files to enterprise-level information, now, more than ever, effectively managing this data deluge is essential to thriving in the digital landscape.

Rising data volumes create unique challenges for government missions. Agencies must balance data utilization with security, tackle outdated storage systems lacking scalability for data-intensive tasks, and address resulting vulnerabilities impacting operations and citizen services.

To address agencies’ critical data storage needs, we tackle the top three challenges and provide solutions for managing them.

Challenge 1: Fortifying Your Data Fortress – Security in Data Storage

Ensuring the security of stored data is a significant challenge. Data breaches and cyberattacks can result in unauthorized access, loss, and privacy violations. Implementing robust encryption, access controls, and security protocols is essential.

Likely, the most prominent data security measure is Zero Trust. While there is no single piece of legislation or executive order that mandates the implementation of Zero Trust, several federal laws, policies, and initiatives, including FISMA, Cybersecurity and Infrastructure Security Agency (CISA), and the National Institute of Standards and Technology (NIST) Guidelines have encouraged and supported its adoption within U.S. government agencies and departments. These provide a framework for agencies to develop and implement cybersecurity strategies aligning with their needs and risk profiles. Since there isn’t a specific mandate, the adoption of Zero Trust within U.S. government agencies varies based on their cybersecurity requirements and priorities.

So, where does data storage come into play with Zero Trust? In the Storage Area Network. SANs are critical components of modern IT infrastructure, and implementing Zero Trust principles in SANs is essential for enhancing security and protecting sensitive data.

Zero Trust can be applied to SANs through various strategies and technologies, including:

Segment your SAN with WWPN zoning, employ Virtual SANs or Virtual Fabrics, and enforce strict access policies for authorized system access.
Enforce robust authentication and authorization through RBAC for precise permission levels. Deploy IAM solutions with centralized user management, MFA, and identity lifecycle control.
Encrypt data at rest and in transit using secure protocols and robust key management for a Zero Trust SAN.
Continuously monitor and log data access, use anomaly detection, SIEM, and log analysis to identify and respond to suspicious activities, and analyze logs for potential security threats.
Conduct regular security audits and assessments to identify vulnerabilities and ensure compliance. Naturally, Remediate any security issues promptly.
Establish and enforce Zero Trust policies for SAN resource access, continuously verifying permissions based on user identity, device health, and behavior.
Educate SAN administrators, users, and stakeholders on Zero Trust principles and best practices to clarify their roles and responsibilities in securing the SAN.

Zero Trust in a SAN is an evolving process, necessitating constant monitoring, assessment, and customization. Tailor strategies to your storage infrastructure’s unique needs and risks, ensuring you’re prepared for future security challenges.

Challenge 2: Unraveling the Know – Complexity in Data Storage

Managing data across different platforms can be complex as data storage environments become more diverse, with on-premises, cloud, and hybrid solutions. Data may need to be migrated, integrated, and synchronized across various storage systems.

77% of IT decision-makers distrust the data they use
89% of Enterprises have data distributed across multiple clouds
56% of CDOs feel that access and prep is the most significant data activation challenge

Complex data environments lead to inefficiencies, errors, and higher operational costs. Simplifying complexity allows organizations to operate more efficiently, reduce downtime, and make data-driven decisions more effectively.

The causes of data complexity include:

Data Volume in Multi-Cloud Environments: Data can increase rapidly in multi-cloud setups. You might find duplicates of data across various cloud providers and on-premises systems. This poses challenges in keeping everything in sync and managing data effectively.
Data Variety in Multi-Cloud Environments: Multi-cloud environments often involve diverse data sources. Each cloud provider might have its data formats and structures. This creates integration complexities, especially when dealing with various types of data.
Data Velocity in Multi-Cloud Environments: Data velocity is another challenge. In multi-cloud setups, data moves in real time between different cloud services. Ensuring consistency and availability in this rapidly changing environment can be demanding.
Data Governance and Veracity in Multi-Cloud Environments: Security is a significant concern in multi-cloud architectures where you must ensure consistent security policies across different cloud platforms. Additionally, maintaining data accuracy and consistency can be challenging when dealing with data from various sources.
Data Value in Multi-Cloud Environments: Lastly, there’s data value. Data migration and portability can be complex when moving data between clouds while preserving its integrity. Cost management is also crucial. Optimizing data storage and access costs across multiple cloud providers is essential to maximize the value of data stored in a multi-cloud environment.

Managing data in multi-cloud architecture is a multifaceted task. It involves handling data volume, variety, velocity, veracity, and value. Agencies need a robust strategy for data integration, governance, and optimization to navigate these challenges effectively while harnessing the full potential of a multi-cloud environment.

An End-to-End Data Management Framework will help agencies simplify all data management activities across disparate data silos and applications. It will also accelerate data-driven outcomes, reduce costs and governance risks, and support agencies in achieving data agility and efficiency goals.

By implementing an End-to-End Data Management Framework, government agencies can streamline data storage and ensure that data is protected, well-managed, and used effectively to support their missions and operations. This framework helps address the complexity of data storage while promoting data security, efficiency, and compliance.

Challenge 3: Unlocking Efficiency – Maximizing Data Storage Potential

Do you remember the datasphere? By 2025, it’s projected to reach 175 zettabytes, emphasizing the need for efficient data management due to factors like digital transformation, IoT, and cloud adoption. Inefficient practices can hinder performance, scalability, cost-effectiveness, and sustainability.

A few ways agencies can optimize their data storage efficiency include:

STORAGE VIRTUALIZATION

Through multiple procurements and programs, an enterprise finds itself utilizing multiple vendors for its data storage needs. This leads to complexity in managing multiple tools, best practices, and skill sets. Storage virtualization is a transformative approach in the world of data management. It’s all about breaking down the traditional barriers of storage infrastructure. Instead of being confined to specific physical storage devices, your data is abstracted into a unified virtualized pool. Some efficiency benefits of storage virtualization include:

a. Eliminate Data Storage Islands: Imagine your data scattered across isolated systems, causing inefficiencies and complexity. Storage virtualization acts as a bridge, merging these islands into a unified, scalable, and flexible storage environment.

b. Centralize Operations: Storage virtualization serves as a control center for your data storage, simplifying administration, backup, recovery, and resource allocation while reducing skill and tool complexity for your team.

c. Optimize Asset Investment: Storage virtualization maximizes your hardware investments, revitalizing older equipment by integrating it into the virtualized environment and fully leveraging your assets.

d. Enhanced Performance and Availability: Storage virtualization enhances performance and data availability by load balancing, automated failover, and vendor-agnostic data replication.

DATA COMPRESSION AND DATA DEDUPLICATION

Two methods of adaptive data reduction that enable agencies to store more data with less storage are data compression and data deduplication.

Data compression starts by identifying and eliminating redundancy in the data. That’s the repetition of information that can be expressed more efficiently. Government agencies first determine the types of data they need to compress and select the appropriate compression algorithms. Standard compression algorithms include Lempel-Ziv-Welch (LZW), DEFLATE, GZIP, BZIP2, and various lossless and lossy compression methods. The algorithm choice depends on the data’s nature and the trade-offs between compression ratios and speed.

Agencies can choose between software and hardware compression tools based on their needs. Software tools are installed on servers or storage systems, while hardware compression may be integrated into storage appliances. Integrating compression into storage systems or software-defined solutions reduces storage space by compressing data upon storage, particularly beneficial for archived and backup files. This is crucial for data redundancy and disaster recovery optimization.

Data compression can significantly contribute to data storage efficiency for government agencies by optimizing storage resources, reducing costs, and improving data accessibility and security. However, selecting suitable compression algorithms and tools is essential, considering the trade-offs between compression and data access speed when implementing data compression strategies.

Another technique to enable agencies to store more data on less storage is data deduplication, also known as deduplication or dedupe, which is a data reduction technique that eliminates redundant data within a dataset or storage system.

The critical concepts of deduplication include:

Identification:

Deduplication algorithms scan data to identify duplicate copies of the same information.
Duplicates can occur at various levels, such as within files, across files, or within storage blocks.

Hashing:

Deduplication employs hash functions to create unique identifiers (hashes) for data blocks.
These hashes are used to compare and identify duplicate data efficiently. You can think of these as the fingerprints of data.

Single Copy Retention:

After identifying duplicate data, deduplication systems retain only a single copy of each unique piece of data.
All subsequent occurrences of the same data are replaced with references or pointers to the retained copy.

Fixed vs. Variable Block Deduplication:

Fixed block deduplication divides data into fixed-size blocks, while variable block deduplication uses variable-sized blocks.
Variable block deduplication is more efficient in identifying duplicate data of varying sizes and is most common in storage arrays.

Inline vs. Post-Process Deduplication:

Inline deduplication happens as data is ingested or written to storage, preventing duplicate data from being stored in the first place.
Post-process deduplication identifies and eliminates duplicates after data has already been stored and cleans up duplicate data through garbage collection.

SUSTAINABLE DATA STORAGE

The increasing energy consumption of data centers and storage solutions contributes to environmental concerns. Implementing energy-efficient storage solutions and practices is becoming more critical.

In the data-driven world, organizations aim to save costs and promote environmental sustainability through efficient data storage. Rising operational expenses due to power-hungry data centers drive the need for power-efficient solutions. Choosing hardware like Solid State Drives (SSDs) over traditional Hard Disk Drives (HDDs) boosts performance and reduces power consumption. Consider hardware with energy-saving certifications and comprehensive carbon footprint information to minimize environmental impact.

A strategy to help with data storage sustainability is data tiering, storing frequently accessed data on fast, low-power devices and less frequently accessed data on slower, energy-efficient storage. Adopting data deduplication and compression techniques also plays a role here by reducing storage needs and removing duplicate data, saving energy and costs.

Cooling and location play a role, too. Efficient cooling reduces power consumption, and choosing data center locations with access to renewable energy and cooler climates lowers environmental impact.

Considering these techniques will help agencies cut operating costs and make them more environmentally responsible. Adopting power-efficient storage hardware and practices isn’t just about saving money; it’s about creating a more efficient and eco-friendly future.

CONCLUSION

Data storage plays a significant role in today’s data-driven world. With storage, you can make data decisions. But without the right strategy, even with a data storage solution, agencies will struggle to enable access to the right audience, anywhere to any source – data center, cloud, edge. By taking a strategic, comprehensive approach to these challenges, federal government agencies can enhance their data storage capabilities while safeguarding sensitive information and improving overall efficiency and collaboration.

Hitachi Vantara Federal has architected a holistic vision for modernizing the digital core, enabling organizations to process data and workloads across their enterprise for availability and access to data and insights. Our mission is to help agencies modernize their digital core by bringing advanced computing power and data storage to the data wherever it resides – in the data center, the public cloud, at the edge, or all the above.

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