The Importance of Data Quality and Cloud Storage Management in Singapore’s Educational Institutions
Singapore has since been the leader in integrating technology in various industries, including education.
Even before the world was forced into e-learning, Singapore had already begun the Singapore Student Learning Space (SLS), an online portal dedicated to national schools. Similarly, the Ministry of Education's (MOE) SkillsFuture for Education (SFEd) framework uses the e-pedagogy plan to create guidelines for teachers on using technology for learning experiences.
These initial steps toward e-learning signify Singapore’s dedication to utilising technology to improve learning processes for training institutions through technology.
However, as the training institutions further embrace digital transformation, they will become increasingly reliant on data to drive decision-making, enhance learning experiences, and improve administrative efficiency. Ensuring high data quality and effective cloud storage management is crucial for achieving these goals.
Data Quality: The Foundation for AI Readiness in Educational Institutions
High-quality data is essential for the successful implementation of AI tools in education. Accurate, complete, and timely data enables AI systems to provide meaningful insights and support decision-making processes.
Poor data quality presents multiple challenges when implementing AI solutions:
Flawed AI outputs. Training institutions may use AI-powered analytical tools to assess learner performances or attendance. Having accurate information from which to pool this data is vital. If the data is incomplete, outdated, or incorrect, the system may produce inaccurate results, leading to misguided decisions and ineffective interventions.
Operational inefficiencies. AI-driven systems often analyse data, such as the number of enrollees in a given period, class size forecasts, staffing needs, and resource allocation. If the data contains errors – like duplicate records or outdated enrolment statuses – training institutions may overestimate or underestimate trainee numbers, which can lead to overstaffing or understaffing and misallocated budgets and classroom resources.
Ineffective personalisation. AI-driven personalisation, such as tailored learning experiences, depends on comprehensive data about students’ strengths, weaknesses, and learning preferences. Poor data quality can result in inappropriate recommendations, diminishing the effectiveness of AI in the classroom.
Training institutions that proactively address these issues position themselves to fully leverage AI’s transformative potential, while those that neglect data quality risk implementing systems that deliver limited value or, worse, produce misleading results.
Storage Cost Optimisation: Managing Education Data Effectively
Effective cloud storage management is essential for optimising costs and ensuring data security. With the growing volume of data generated by training institutions, it is crucial to apply appropriate treatment to redundant, trivial, and obsolete (ROT) data. Automatically managing ROT data helps reduce storage costs and minimises the risk of data breaches and sprawl.
Since 2024, Microsoft has discontinued free unlimited storage plans as part of efforts to curb rising security risks and fraud. Instead, school tenants now receive 100 TB of free pooled storage across Microsoft 365 applications like OneDrive, SharePoint, and Exchange, with additional storage available for certain paid users.
While this storage allocation is good news for educational institutions, helping them manage data more effectively, it comes with risks: data sprawl and breach.
Unmanaged data opens numerous challenges for training institutions:
- Higher risks of cyberattacks. More data translates to an expanded attack surface for threat actors.
- Unauthorised data access. Growing uncontrolled data in a digital workspace increases the possibility of users gaining unauthorised access to sensitive information.
- Ineffective AI utilisation. With sprawled data, training institutions may face challenges in getting accurate results from AI due to ROT data sitting in institutions’ digital workplaces.
Here are some key strategies to consider:
- Implement data classification and retention policies. By categorising data based on its importance and sensitivity, institutions can ensure that unwanted information does not accumulate in the workspace with selected data systematically archived or deleted through set rules.
- Automate data management processes. Leveraging automation tools for data management can help streamline the identification and handling of ROT data, reducing manual effort of deleting unnecessary files and ensuring consistent application of data policies.
- Regular data audits. Conducting periodic audits can help identify and address data sprawl, ensuring that only relevant and necessary data is retained.
- Staff training and awareness. Educating staff and learners about data management best practices and the importance of data security can foster a culture of responsibility and vigilance, helping with best information management practices.
- Utilise AI and machine learning. Employing AI and machine learning tools can assist in identifying patterns and anomalies in data, improving the accuracy of data management and security measures, which helps in having an optimised storage.
By adopting these strategies, educational institutions can effectively manage their data, save storage costs, and mitigate the risks associated with unmanaged data. This proactive approach not only enhances data security but also supports the institution’s overall mission by ensuring that valuable information is readily accessible and protected.
Data Lifecycle Management and Storage Optimisation with AvePoint
Prioritising data quality and cloud storage management are vital for the public education sector in Singapore. It can help training centres and institutions with enhanced data security, collaboration, and cost savings.
The AvePoint Confidence Platform provides comprehensive solutions to help educational institutions in Singapore implement effective data lifecycle management and storage optimisation — all in a unified solution.
With the Confidence Platform, institutions can:
- Automate data classification, retention, and disposal. With the Confidence Platform’s AI-powered classification feature, institutions can ensure that data is properly managed throughout its lifecycle, from creation to disposal.
- Meet compliance requirements. By implementing rules based on different document criteria, institutions can automate archiving processes, ensure compliance with data protection regulations, such as the Personal Data Protection Act (PDPA), and reduce risks of data breach and sprawl.
- Optimise storage. Through automated data management, the Confidence Platform also helps avoid storage overages, ensuring that volumes of data do not take up a lot of storage space in workspaces. Archived data can be kept in lower-cost cloud storage, improving workspace data quality and ensuring compliance.
Want to know how much storage you can save with AvePoint’s Confidence Platform? Check our data storage calculator. Find out more about improving data quality and optimising storage for your training institution’s digital workspace.
Phoebe Magdirila is a Senior Content Marketing Specialist at AvePoint, covering SaaS management, backup, and governance. With a decade of technology journalism experience, Phoebe creates content to help businesses accelerate and manage their SaaS journey.
