Unstructured data continues to be a dominant force, presenting both opportunities and challenges for organisations across industries.
As we venture further into 2024, several key trends are shaping the way businesses approach the management of unstructured data. From advanced analytics to data privacy concerns, here’s a closer look at what’s on the horizon.
Storage Teams Drive Self-Service Initiatives
As IT transforms towards a service-oriented model and interest in AI grows, enterprise storage teams are seeking ways to efficiently manage data across diverse storage platforms while enhancing data services for business users. According to the Komprise 2023 State of Unstructured Data Management, 85% of IT leaders advocate for non-IT users to participate in managing their data, with 62% having already implemented some level of user self-service for unstructured data management. To adapt, storage professionals must prioritise collaboration with various departments, leveraging tools like showback reporting to identify cost-saving opportunities through the identification and tiering of inactive data and the removal of redundant copies.
Empowering end-users with self-service capabilities entails enabling swift file searches and facilitating communication with IT regarding data handling preferences, allowing IT to establish appropriate policies for data movement, including migration to cloud-based AI services. This shift towards user-driven data management not only enhances operational efficiency but also ensures alignment between IT resources and business needs, fostering a more agile and responsive organisational ecosystem.
The Role of GenAI in Data
Studies have shown that organisations are generally permitting employee utilisation of GenAI, with many establishing certain limitations on data or application usage. However, the implementation of guardrails is hindered by the evolving nature of the technology, coupled with a lack of insight into its underlying mechanisms and the measures taken by vendors to safeguard organisational data. Controlling employee usage, akin to addressing shadow IT, presents challenges due to these uncertainties.
To address these concerns, the initial step should involve establishing and enforcing a comprehensive data governance framework. This framework should address key aspects such as Security, Privacy, Lineage, Ownership, and Governance (SPLOG), governing the interactions between data and AI technologies. By prioritising the establishment of robust governance measures, organisations can mitigate risks associated with GenAI usage, ensuring compliance with regulations and safeguarding sensitive data assets.
Layered Approach to Data Governance
AI Data Governance is poised to adopt a layered approach to address the diverse array of threats posed by generative AI technologies. Rather than relying on a singular solution, a tiered system of AI security tools will be implemented, each serving a specific function within the governance framework.
Beginning at the network layer, measures will be taken to prevent unauthorised access to restricted data by AI tools, as well as to restrict users from transmitting corporate data to unauthorised AI services. This initial layer serves as a foundational barrier against data breaches and unauthorised usage.
Moving to the data layer, a secondary level of protection will be employed to track and audit data movements, monitoring details such as the type of data transferred, its destination, timing, and the identity of the user initiating the transfer. This layer will also include mechanisms to detect and alert against the sharing of personally identifiable information (PII) or other sensitive data.
At the user layer, a final tier of security mechanisms will be implemented to provide real-time feedback and guidance to users interacting with AI prompts. This includes warnings when prompts involve corporate or sensitive data, as well as feedback on prompts that may inadvertently disclose excessive corporate frameworks. By integrating these layered security measures, organisations can establish a robust AI data governance framework capable of effectively mitigating risks associated with generative AI technologies.
Cost Optimisation Strategies Essential for Cloud Migrations
Effective cost optimisation strategies will be essential for successful cloud migrations. These strategies encompass various tactics aimed at minimising cloud waste and maximising cost efficiency. One common approach involves leveraging cost savings plans and promotional pricing options offered by cloud vendors. Additionally, organisations can employ commercial spend monitoring tools to track and manage cloud expenses more effectively. Deleting duplicate and orphaned data, along with implementing measures to reduce cloud sprawl through automated discovery and corporate policies, are also vital steps in cost optimisation.
An independent unstructured data management solution plays a pivotal role in informing cloud migrations by providing storage and IT managers with comprehensive visibility and analysis of data assets across all storage systems. This solution enables automated data movement to the most cost-effective storage solution based on current requirements, preventing data from languishing on high-priced storage tiers when it is no longer actively utilised. Furthermore, continuous data lifecycle management facilitated by automated policies ensures that data is relocated to the optimal storage location as it ages or its business value evolves. By integrating these cost optimisation strategies into cloud migration plans, organisations can mitigate unnecessary expenses and enhance the efficiency of their cloud infrastructure.Top of Form
Unified Storage and Data Metrics
In response to the escalating complexity brought about by the exponential growth of unstructured data and the proliferation of storage solutions from various vendors, IT departments are actively seeking a unified approach to storage and data metrics. Currently, storage managers lack a centralised console capable of providing comprehensive insights into both storage infrastructure and data assets. This lack of visibility poses significant challenges, including heightened strain on IT budgets, increased operational complexity, and elevated security and compliance risks.
The fragmented nature of storage procurement, with storage solutions sourced from multiple vendors, further exacerbates the situation, making it challenging for storage managers to discern trends and optimise resources effectively. This lack of centralised oversight hinders the ability to identify opportunities for cost savings or to manage capacity, performance, and security requirements efficiently for end-users.
Recognising these challenges, our partner, Komprise recently introduced Storage Insights, a solution designed to empower customers with enhanced visibility and productivity. By offering a unified platform for monitoring storage and data metrics, Storage Insights equips IT departments with the tools needed to navigate the complexities of modern storage environments more effectively. Through centralised access to detailed usage and capacity data, organisations can make informed decisions, streamline operations, and mitigate risks associated with unstructured data growth and storage diversity.
Drawing upon the expertise in networking and storage, IDS understands the critical role that intelligent data management systems play in the success of any project. Unstructured data, whether acknowledged or not by organisations, holds immense potential for shaping pivotal organisational decisions. With the exponential growth of unstructured data projected to double every two years, it becomes increasingly imperative to effectively manage this influx while controlling costs. We are committed to helping you navigate the complexities of data management and achieve your organisational objectives efficiently and effectively.
Speak to us today to know more.
