AI is no longer hype. Today the global Artificial Intelligence market is worth nearly $235 billion, with projections expanding to over $631 billion by 2028, according to IDC. Yet when it comes to implementing these technologies, it’s still early days, according to the fourth-annual Komprise 2024 State of Unstructured Data Management.
Most (70%) organizations surveyed are still experimenting with these new technologies as “preparing for AI” remains a top data storage and data management priority for IT leaders. Many don’t have a specific budget for AI as IT departments are focusing on cutting costs and waste. The survey also uncovered the latest viewpoints on AI data governance, AI infrastructure plans, unstructured data management challenges and future needs.
Here are the 5 top trends we uncovered in our latest industry survey.
1. Top Data Storage Priority: Cost Optimization
For the fourth year in a row, IT directors say they will spend more on storage this year than the previous. In 2023, preparing for AI was the top data storage priority but this year, it is cost optimization during a tough economy. IT leaders, while certainly thinking about AI, are zoning in on workplace productivity and care most about making data accessible, highly available and easy to move as needed.
2. Unstructured Data Management Challenges: No Disruption & Data Classification
As in 2023, moving data without disruption to users and applications is a top technical challenge (54%) for unstructured data management. IT leaders are always looking to deliver superior performance to their internal customers and avoid conflicts and unnecessary calls to the help desk. This means ensuring that data is easy to find and use after data tiering and migrations. Read about Komprise Transparent Move Technology. The second leading technical challenge is using AI to classify and segment data (48%), an emerging tactic to add structure to unstructured data so it can be discovered and leveraged for new value. AI-enhanced data classification is a highly efficient way to do this, but best practices are still emerging here. Read more in this blog.
3. Scrappy Times for AI
Many IT leaders have plans for AI but first, they must select and/or build the stack of tools and infrastructure to host the programs. Getting ready for AI may involve upgrading storage and computing infrastructure, cleaning and preparing data, training or developing custom LLMs, acquiring new IT skills, and beefing up security tools. How organizations pay for all of this technology is unclear: only 30% say they will increase the IT budget for AI. Strategies will likely entail leveraging existing budgets – such as cloud, say 34% – to fund AI. To help, IT leaders can optimize data management for savings and be strategic with cloud spending to avoid waste.
4. Unstructured Data Management Evolves for AI Governance
Unstructured data management solutions are maturing far beyond giving IT users a way to easily migrate and tier data to new storage and analyze and model costs. IT teams now want features supporting AI data governance and security, such as the ability to quickly find, tag and classify sensitive data and move it automatically by policy to secure storage where it can’t be ingested into GenAI. Other capabilities include creating AI data workflows which integrate data management tools with AI tools to find and tag sensitive data sets like PII across large data estates.
5. AI Strategies Focus on Infrastructure
IT leaders are setting their sights today on creating “AI-ready data infrastructure.” There are many pathways for this–between procuring and developing internal technology, using cloud services or combining those strategies in a hybrid model. IT teams are split on developing AI models internally versus using commercial services and/or the cloud. AI technology decisions will need to factor in internal expertise and resources to support these new technologies, budget and data security concerns.
Download the full report to see all the data and insights.
