Every day, businesses generate vast amounts of data, making efficient Data Lifecycle Management a crucial aspect of modern IT strategy. While SharePoint is a powerful collaboration platform, its storage limitations and rising costs can become a major challenge. Organizations struggle with excessive data accumulation, leading to higher expenses, reduced productivity, and potential security risks. Without proper governance and archiving solutions, businesses face unnecessary operational burdens.
To effectively manage the entire data lifecycle—from creation to archival and disposal—organizations need a smart, automated solution that optimizes storage while maintaining accessibility.
Data Lifecycle Management (DLM) is the process of managing data from its initial creation through its various stages, including active usage, archival, and eventual deletion. A well-structured DLM strategy ensures:
Optimized storage utilization to prevent excessive costs
Enhanced compliance with regulatory requirements
Improved productivity through better data accessibility
Reduced security risks by eliminating outdated, unnecessary files
However, many companies lack the necessary tools to implement an effective DLM strategy, resulting in data sprawl and inefficiencies.
Data Lifecycle Management (DLM) involves systematically managing data from its creation to its secure disposal. It ensures efficient storage, compliance with regulations, and cost optimization. The lifecycle typically includes five key stages:
An effective DLM strategy not only enhances data security and compliance but also helps organizations manage storage costs and maintain optimal system performance.
Let’s face it—managing data isn’t always straightforward. As businesses grow, so does the amount of data they generate, and with that comes a whole new set of challenges. From skyrocketing storage costs to security concerns and productivity slowdowns, it’s easy to feel overwhelmed. But don’t worry! By understanding these common pain points, you can tackle them head-on and keep your data under control.
SharePoint’s default storage allocation is limited, and exceeding these limits results in significant additional costs. Without an offloading strategy, businesses may face unnecessary expenditures. A structured approach to data management mitigates this issue by offloading rarely used files to alternative storage solutions, ensuring that critical data remains in SharePoint while lowering storage expenses.
Unstructured data—such as emails, documents, images, and videos—accounts for the majority of business information today. In SharePoint, this type of data often accumulates without a clear organization strategy, leading to a chaotic environment where users struggle to find what they need. According to recent studies, employees spend up to 20% of their workweek searching for information, which significantly impacts productivity.
Unstructured data doesn’t just clutter storage; it also poses security and compliance risks, as sensitive information may be scattered across multiple locations without proper governance. This lack of organization can lead to versioning issues, duplicate files, and inconsistent data usage.
To combat these challenges, implementing automated archiving rules is crucial. By configuring rules based on file size, last modified or accessed date, and specific SharePoint locations, businesses can streamline data management. Automation ensures that less frequently accessed files are seamlessly moved to an archive. As a result, users can of course no longer access this data as usual and IT departments may have additional work if this data needs to be restored. Ideally, users should therefore be able to access the data via SharePoint.
This strategic approach not only reduces storage costs but also enhances productivity by creating a cleaner, more organized digital workspace. Employees can easily locate relevant information, collaborate more effectively, and focus on high-value tasks rather than wasting time navigating through cluttered data repositories.
By optimizing unstructured data management, organizations can improve overall efficiency, enhance data security, and ensure compliance with regulatory requirements.
Old, redundant, or stale data poses security risks. Cyberattacks often target large data repositories, and keeping unnecessary data increases vulnerabilities. Businesses can apply automated retention and deletion policies, reducing risk exposure while remaining compliant with data protection regulations.
As data volumes continue to grow exponentially, managing the complete data lifecycle—from creation to archival and deletion—becomes increasingly complex. Traditional data management methods struggle to keep up with the sheer volume and variety of data. This is where Artificial Intelligence (AI) comes into play, revolutionizing how organizations handle Data Lifecycle Management (DLM).
AI can automatically classify and tag data based on content, context, and usage patterns. This eliminates the need for manual sorting, reducing human error and enhancing searchability. By categorizing data accurately, AI ensures that the right information is available to the right users at the right time.
AI algorithms can analyze data usage trends and predict which files are likely to be accessed frequently and which are candidates for archiving. This predictive capability allows organizations to optimize storage costs by moving less relevant data to cheaper storage options while keeping critical information readily accessible.
Compliance with data protection regulations is a major challenge for organizations. AI can identify sensitive or regulated data (such as personal information or financial records) and apply the appropriate retention and access policies automatically. This proactive governance minimizes security risks and helps companies avoid costly compliance violations.
One of the most significant challenges in DLM is managing data retention and disposal. AI-powered systems can automate retention schedules and ensure secure data deletion once regulatory requirements are met or when data is no longer needed. This helps organizations reduce unnecessary storage costs and mitigate data breach risks.
By automating routine data management tasks, AI allows IT teams and employees to focus on strategic activities rather than time-consuming administrative tasks. This boost in productivity translates into cost savings and improved operational efficiency.
A strategic approach to SharePoint storage can lead to major cost savings, improved efficiency, and enhanced security. When considering storage solutions for SharePoint, organizations must weigh cost, accessibility, integration, and automation capabilities. Traditional SharePoint storage, while natively integrated, can quickly become costly as data volume increases. Exceeding SharePoint’s allocated storage limits results in expensive additional costs, making it essential for businesses to explore smarter alternatives.
Modern storage solutions offer significant advantages, including seamless integration with SharePoint, automated archiving, and user-friendly access to archived data. Unlike conventional SharePoint storage, which often requires manual data management, smart storage solutions automate the classification and transfer of data based on predefined rules, ensuring that only essential files remain in active SharePoint storage. This approach not only optimizes costs but also enhances efficiency by providing advanced tracking, reporting, and compliance management.
By implementing a strategic storage solution, businesses can achieve cost savings of 50%, improve productivity through a more organized data environment, and mitigate security risks associated with excessive data accumulation.
For businesses looking to optimize SharePoint storage efficiently, ShArc by Layer2 leading solutions provides the automation, intelligence, and cost-efficiency needed to transform Data Lifecycle Management.
Book a free demo today and discover how ShArc can revolutionize your SharePoint data management strategy!
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Here we'll answer some frequently asked questions about Data Lifecycle Management (DLM):
Data Lifecycle Management is a policy-based approach that oversees the flow of an information system's data throughout its lifecycle—from creation to deletion. It encompasses stages such as storage, backup, archiving, and disposal, ensuring data is accessible to the appropriate users at the right time.
Data Lifecycle Management is imoportant for organizations because it helps to manage data effectively, ensuring its security, integrity, and availability. It aids in compliance with regulatory requirements, optimizes storage costs, and enhances decision-making by maintaining high-quality data.
The six steps of Data Lifecycle Management typically include the following stages:
Each stage involves specific processes and best practices to manage data effectively.
Data Lifecycle Management differs from Information Lifecycle Management regarding the focus on managing data throughout its lifecycle. The first one concentrates on the management of entire data files or records, whereas the second one considers the value of information to an organization, focusing on how information is used and its importance to business processes.
Yes, Data Lifecycle Management can help reduce storage costs by identifying and eliminating redundant, obsolete, or trivial data. It ensures that data is stored in appropriate storage tiers based on its value and usage, optimizing storage resources and reducing expenses. Implementing a robust Data Lifecycle Management strategy enables organizations to manage their data efficiently, ensuring it remains a valuable asset throughout its lifecycle.