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In 2020, the World Economic Forum predicted that, by 2025, 50 percent of all employees would need to reskill because of technology change. Now that it’s 2025, this seems highly relevant, especially as employees across most disciplines are expected to understand AI and use it in their jobs. Job ads increasingly call for a data-driven approach to leading teams and projects.
So where does this leave the IT professional, particularly those managing data storage and IT infrastructure? Until recently, anyone handling data storage – whether it is on premises or in the cloud – has been able to continue their focus on performance, capacity and data protection.
Times are changing. Storage IT professionals are increasingly managing data movement and access across complex hybrid cloud and multi-vendor environments. Addressing security threats from AI and cyberattacks is critical, as is managing infrastructure and data workflows for AI.
Reimagining Work With Data at the Center
Storage architects and engineers should consider how they can energize their careers by moving from a storage steward, managing and procuring storage technology, to becoming a data steward. This entails reducing risks, using cost modeling and FinOps tools, preparing data and infrastructure for AI and delivering data services to departments.
Below are some ways that the work of storage IT teams will change, as data-centric and AI-centric processes spread across organizations.
8 Ways Storage IT Pros Can Evolve in the Age of Analytics and AI
- Collaborate and listen.
- Get data on your data.
- Cost modeling and FinOps.
- Improve SLAs and data metrics for business.
- Reduce risks from ransomware.
- Prepare infrastructure for AI.
- Prepare data for AI.
- Protect data for AI.
1. Collaborate and Listen
Thank you, Vanilla Ice, for elevating the words that matter. Data stewards need to adopt a cross-disciplinary mindset. The job of managing technical configurations and resolving issues now requires a broader understanding of the full IT infrastructure. Storage professionals will thrive as trusted advisors to other IT and business roles to help set the requirements for data management vis-à-vis business objectives. This will require a collaborative mindset and business-savvy approach to meet user and departmental needs while satisfying IT objectives for standards, security, compliance and cost efficiencies.
2. Get Data on Your Data
As Peter Drucker said, “You can’t manage what you don’t measure.” Although this isn’t entirely true, it certainly helps to have metrics that inform data management decisions and serve internal customers. In the AI age, storage professionals will need to work with data scientists, data analysts, project teams and department leaders, all bringing more knowledge about data in storage. This entails identifying, segmenting and defining data types and managing that data granularly, according to business and user needs. For instance, you will need to know data volumes and growth rates across the organization by department, common file types and sizes, and lead efforts at data classification via metadata enrichment.
3. Cost Modeling and FinOps
Large organizations spend millions of dollars annually on data storage, backups and disaster recovery. On balance, there’s nothing wrong with that since data is the center of everything today. Not all data should be treated the same, however.
Using cost modeling tools, the storage manager can enter actual storage costs to determine up front new projected costs and actual usable capacity based on data growth rates. These costs must factor into backups and disaster recovery, which can be three times that of storage spending and should compare on-premises versus cloud models. An unstructured data management system that indexes all data across all storage can supply metrics on data volumes, costs and predicted costs and then model plans for moving less-active data to lower-cost archival storage such as in the cloud. Then, they can select the optimal storage platform for hot or active data when it’s time for a storage refresh.
4. Improve SLAs and Data Metrics for Business
Service-level agreements for storage typically include data availability and uptime [a.k.a. the Three Nines or Four Nines], latency for hot and cold storage, backup frequency and windows and RTO and RPO and response times for issues such as critical data loss. There may be new metrics, however, that storage professionals need to start tracking as they deliver more services to business. These could include departmental chargeback or showback, percent of non-compliant data in storage (such as personal videos or legacy system files that must be deleted), top data owners by department and individual, and the amount of duplicated and orphaned data reduced.
5. Reduce Risks From Ransomware
Storage teams must mitigate ransomware risks associated with file data. One way to do this is by implementing hybrid tiering strategies that offload infrequently accessed (cold) files to immutable cloud storage, which reduces the active attack surface by as much as 70 or 80 percent. Immutable storage ensures that once data is written, it cannot be altered or deleted, providing a robust defense against ransomware attempts to encrypt or corrupt files.
6. Prepare Infrastructure for AI
Storage teams play a crucial role in AI by ensuring the reliable, scalable and efficient storage and management of the massive data sets required for AI model training and deployment. Procuring the right infrastructure to run AI workloads will vary significantly depending upon the size and budget of the organization, the level of AI customization needed and other considerations such as security.
Launching an AI initiative in your enterprise may require model development and training if you need to build your own generative AI model. This typically begins with acquiring adequate high-performing computational resources — the pricey CPUs, GPUs and TPUs that are required to host machine learning models and process data at warp speed. Although pre-baked infrastructure, public models and cloud services offer cost and ease-of-use benefits, IT organizations must also weigh the benefits of keeping AI in-house or developing a hybrid cloud AI model for better controls.
7. Prepare Data for AI
Big data analytics and AI projects often involve processing large and diverse unstructured data sets, such as images, videos, documents, messages and sensor data. Storage teams are responsible for classifying and preparing this data for model training. The first step is gathering basic insights about the data sets, such as its size, location, file type, access and usage patterns and if data needs enrichment (via additional metadata tagging) for better context and classification.
Storage leaders also need to organize and manage data so that end users such as data scientists and researchers can easily search for and leverage the specific data required for their projects. Using technology for automated data workflows streamlines this process of discovering, enriching, copying and/or moving data to the optimal location for analysis.
8. Protect Data for AI
Storage professionals need to prevent sensitive data leakage to AI, as employees use generative AI and other tools daily. This begins with identifying and segregating sensitive and proprietary data into a private, secure domain where it can’t be shared with commercial applications. It’s also vital to maintain an audit trail of your corporate data that has fed AI applications.
A healthcare organization, for instance, would need to verify that no patient PII data has been leaked to an AI solution per HIPAA rules. Storage IT managers will need to help institute an AI governance framework that covers privacy, data protection, ethics and more.
A New Mindset for Storage IT Professionals
The age of AI and data is not just transforming technology and business models — it’s redefining careers. For IT storage professionals, the shift from managing infrastructure to orchestrating data presents both challenges and incredible opportunities.
Now is the time to ask yourself: Are you simply maintaining storage, or are you preparing data to drive business success? By expanding your skills in data intelligence, automation, FinOps, security and AI governance, you can position yourself as an indispensable leader in the modern enterprise.