Assessing data management practices: Is your business AI-ready?

Artificial intelligence (AI) is transforming the technology landscape, business environment, and societal perceptions for what the future may hold. According to reports by McKinsey and PwC, AI has the potential to positively impact the global economy by US$13 trillion to US$15 trillion before 2030.

The successful adoption and implementation of AI rely heavily on an organisation’s data. Data serves as the fuel that powers AI algorithms, vectors, and context. Consequently, the quality and quantity of data used to train an AI model significantly influence its validity, effectiveness, and power. If the data available to AI models is biased, incomplete, or inaccurate, the resulting outputs are likely to be incorrect or biased.

A constant stream of data is crucial for the development and deployment of AI. Without new data, AI models are unable to continue learning and improving their accuracy. Therefore, organisations that invest in collecting, managing, storing, protecting, and analysing high-quality data are better positioned to leverage the power of AI and gain a competitive advantage. However, this task is easier said than done. In today’s modern and distributed architectures, collecting, managing, storing, protecting, and leveraging data from workflows throughout an organisation’s data estate is a complex undertaking. This complexity is amplified as data is typically stored in hybrid or multi-cloud environments, third-party SaaS applications, and at the edge.

Consequently, organisations often find themselves storing vast amounts of unclassified, unindexed, or untracked data. This data is not only unstructured and difficult to access but also expands an organisation’s attack surface. Ineffective protection and security measures increase data management costs and prevent organisations from gaining valuable insights or making informed decisions, such as fueling their AI models.

AI-readiness dependent on indexed and searchable data

In our digital world, characterised by sophisticated cybersecurity threats and integrated IT environments, organisations require a distributed file system and architecture that allows them to back up their data across all storage environments, provides insights and analytics, facilitates replicability for disaster recovery, and enhances cyber resilience through data isolation, threat detection, and data classification capabilities.

One of the most crucial data management capabilities that render an organisation and its data AI-ready is data indexing and instant searchability. Organisations must have the ability to store and index unlimited structured or unstructured data, enabling instant search across their entire data estate whenever needed by IT teams or users. Leading modern data management and security platforms enable searching through decades-old data with immediate results and display different data variations from various time periods. Organisations that store data without indexing file and object metadata, due to their reliance on outdated data management technology, undermine the capabilities of AI deployments.

Data management capabilities that set organisations up for AI success

Organisations can position themselves to fully leverage AI models and solutions by implementing the following data management capabilities:

  • Data aggregation and unification: It is crucial to aggregate data from diverse sources and types, including on-premises, multiple clouds, and edge devices. Organisations should strive for a unified platform that provides easy access and secure data analysis for AI applications. By having a comprehensive view of data, organisations can identify patterns, trends, and anomalies that may remain hidden in isolated data silos. This enables them to eliminate dark data and gain early detection of potentially malicious changes. Additionally, indexing and aggregating backup data allow organisations to apply AI to these backups without consuming excessive storage space or computational resources from production systems. This approach safeguards production data from direct exposure to AI applications.
  • Data optimisation: Deduplication and storing data in compact structures are essential to prepare data environments for AI. Compact storage, combined with relevant metadata, improves searchability and empowers organisations to leverage AI applications in identifying trends and patterns for informed decision-making.
  • Data protection: Enterprise-grade backup, recovery, and disaster recovery solutions are necessary to safeguard data and enable isolation in air-gapped or virtually air-gapped environments. As AI relies on substantial volumes of data, organisations need the ability to rapidly recover thousands of virtual machines, databases, and files to prevent disruptions or downtime.
  • Data security: Given the constant threat of ransomware attempts and cyberattacks, organisations must be able to detect data threats promptly. Detecting anomalous changes in data, often caused by malware, and utilising threat intelligence feeds are effective methods to achieve this. Efficient classification of sensitive data and cyber vaulting techniques can minimise the impact of successful cyberattacks. Modern data security and management platforms provide the necessary capabilities to ensure AI applications continue running even in the event of a malicious cyber event or disaster.
  • Data access: Implementing granular role-based access controls (RBAC) for backup data ensures that users can only access authorised data, safeguarding sensitive information such as personal identifiable data, healthcare records, intellectual property, and financial transactions. AI models can be aligned with RBAC, ensuring that data queries and responses are compliant with assigned user permissions.

By adopting the right data management and security platforms, organisations can unlock the full potential of their data and maximise the effectiveness of their AI models and solutions. Those with clean, accurate, and accessible data environments are well-prepared for AI adoption and gain a competitive edge in deploying AI models with speed, accuracy, and efficiency.