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The pharmaceutical industry is experiencing a rapid surge in data generation, driven by advances in research, clinical trials, and patient data collection. While this data explosion offers immense potential for innovation and improving healthcare outcomes, it also presents significant challenges in terms of managing and storing vast amounts of information. As the volume, velocity, and variety of data continue to grow, so do the costs associated with its management and storage. In this context, storage divestitures have emerged as a strategic approach to address the increasing costs of managing data in the pharmaceutical industry. By strategically offloading storage assets and leveraging alternative solutions, such as the cloud, organizations can optimize costs, improve operational efficiency, and unlock new opportunities for innovation. This article explores how storage divestitures can be a cost-effective solution to mitigate these challenges while ensuring data availability, security, and compliance. But first, we need to understand what is a storage divestiture?
Exploring the Concept of Storage Divestiture
Storage divestiture refers to transferring or offloading storage assets or resources from on-premises infrastructure to a cloud provider or from one cloud service provider to another. It involves the strategic decision to reallocate or redistribute storage-related responsibilities and resources to optimize costs, performance, security, or other factors. Storage divestiture may occur due to various reasons, such as:
Cost optimization: Organizations may seek alternative storage providers or solutions that offer better pricing models, more competitive rates, more capacity utilization or improved cost efficiency. For instance, the average cost of storing a single TB of file data can go over $3,351 a year irrespective of usage. Instead, an enterprise can leverage pay-per-use cloud storage to reduce costs.
Performance or scalability requirements: Changing storage needs, such as increased data volumes or performance demands, might necessitate divestiture. Organizations may move their storage assets to a provider with better performance capabilities or scalable storage options to meet their evolving requirements.
Vendor lock-in avoidance: Organizations may choose to divest storage assets from a particular provider to avoid being overly dependent on a single vendor. By diversifying their storage solutions across multiple providers or infrastructure options, they can reduce the risk of being locked into a specific vendor’s ecosystem.
Security and compliance considerations: In some cases, organizations may divest storage assets from a particular provider if they have concerns about security, data privacy, or regulatory compliance. They may opt for a provider with stronger security measures or better compliance certifications.
Storage divestiture involves migrating data from one storage service to another, reconfiguring storage infrastructure, and updating applications or systems to work seamlessly with the new storage provider. The process typically requires careful planning, data migration strategies, and coordination between the organization and respective service providers to ensure a smooth transition without data loss or service disruptions. Here are seven steps to consider before moving ahead with a divestiture.
Seven Steps for Optimizing Data during Divestitures
When embarking on the journey of storage divestiture, the first step is to thoroughly assess and evaluate your current storage infrastructure and requirements. This step helps you understand your existing storage environment and sets the foundation for making informed decisions throughout the divestiture process. While the assessment process itself does not directly minimize your data estate, it provides crucial insights that can inform subsequent steps focused on data optimization. By assessing your existing storage infrastructure and data assets, you can discover ways to reduce your data estate, allowing you to migrate and store only necessary data, thereby controlling data sprawl. Here are some steps to get you started
Data inventory and assessment: During the assessment, you conduct a comprehensive inventory of your data assets and evaluate their relevance and value to your organization. What and where is the data? Do I need all the data? Who has access? Is it secured and compliant? – are just some of the overarching inquiries can be addressed that can be answered during this process.
Data Classification & Tagging: This step allows you to identify, classify, tag and index data based on usage, age, access, importance, sensitivity, and legal or regulatory requirements. Metadata, content and context analytics can be used to help you gain a better understanding of data sets that can be retained (hot data), archived (cold data) or eliminated (redundant, obsolete, and trivial (ROT) data) as part of the divestiture.
Data retention policies: As part of the assessment, you establish policies defining how long different data types should be retained. This step helps identify data that has exceeded its retention period (ROT) typically due to age, access, ownership and compliance and can be safely disposed of during the divestiture process, thereby reducing the data estate.
Data archiving and purging: Implement a data archiving strategy to move inactive or rarely accessed data (Cold) to long-term storage. Archiving helps free up primary storage resources while ensuring data availability when needed. Establish proper procedures and safeguards for data retrieval from archives.
Data deduplication and compression: Implement data deduplication and compression techniques to reduce storage requirements. Deduplication identifies and eliminates duplicate data, while compression reduces the size of data files. These techniques can significantly reduce storage needs, particularly for backup and archival data.
Review data storage practices: Evaluate your data storage practices and identify opportunities for optimization. Determine if all data needs to be stored in high-cost primary storage. Consider moving infrequently accessed or archival data to lower-cost storage options, such as cloud-based object storage or tape archives.
Data lifecycle management: Implement robust data lifecycle management practices that define the stages of data from creation to disposal. Automate data lifecycle processes to ensure consistent and systematic data minimization practices across your organization.
Cloud computing plays a pivotal role in storage divestiture by providing organizations with a flexible and scalable platform for data management. Organizations can offload the responsibility of managing and maintaining physical storage infrastructure, thereby reducing operational costs and complexity. But the silver lining here is the ability to tier your data based on usage.Not only can you achieve cost savings, but you can also enhance efficiency and productivity in your core business functions by harnessing the power of high-speed computing. Let’s explore the concept of cloud tiering and its benefits for businesses.
How can Cloud Tiering help?
Cloud tiering refers to the process of intelligently categorizing and migrating data across different storage tiers based on its frequency of access, importance, and cost considerations. It involves segregating data into multiple tiers, such as hot, warm, and cold storage, and dynamically moving it between these tiers as per predefined policies.
Hot storage, also known as primary storage, houses frequently accessed and critical data that requires low latency and high-performance. Warm storage contains data that is accessed less frequently but is still relevant and requires moderate performance. Cold storage comprises rarely accessed or archived data that demands minimal performance at a significantly lower cost.
Storage Tiers: The first step is to set up multiple storage tiers based on their performance, cost, and accessibility characteristics. Typically, there are two or more tiers involved, such as high-performance solid-state drives (SSDs) and lower-cost, high-capacity hard disk drives (HDDs).
Data Analysis: Cloud tiering systems analyze the data usage patterns within the storage infrastructure. This analysis includes monitoring the frequency of data access, the size of the data, and other relevant factors. This step helps identify data that is frequently accessed and data that is less frequently accessed.
Data Classification: Based on the data analysis, the cloud tiering system classifies the data into different categories or tiers. Frequently accessed or “hot” data is placed in the higher-performance tiers, such as SSDs, while less frequently accessed or “cold” data is moved to lower-cost, high-capacity tiers like HDDs.
Tiering Policies: Administrators define tiering policies that determine when and how data should be moved between different storage tiers. These policies may consider factors like data age, access frequency, file size, or specific user-defined rules. For example, a policy might state that data not accessed for a certain period should be moved to a lower tier.
Transparent Data Movement: Once the tiering policies are defined, the cloud tiering system automatically and transparently moves the data between different storage tiers. The data movement process is usually orchestrated by the cloud storage platform or a dedicated tiering service. It may involve copying, migrating, or archiving the data based on the policies and the available storage resources.
Data Access and Retrieval: Cloud tiering ensures that data remains accessible to users and applications regardless of its physical storage location. When a user or application requests data that has been moved to a lower tier, the tiering system retrieves the data from the appropriate tier and presents it seamlessly, as if it were stored in the original location. This process is transparent to the end-users.
Tiering Optimization: Over time, the cloud tiering system continues to monitor data usage patterns and adjusts the data placement accordingly. It dynamically optimizes the storage infrastructure by promoting hot data to higher-performance tiers and demoting cold data to lower-cost tiers. This ongoing optimization helps ensure that frequently accessed data remains readily available while reducing costs associated with storing less frequently accessed data on expensive storage media.
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