Nasuni is a unified file data platform for enterprises facing an explosion of unstructured data, providing business resiliency, driving IT efficiency, and reducing infrastructure costs by up to 65%. Pulse 2.0 interviewed Nasuni Chief Innovation Officer, Data Intelligence and AI Jim Liddle to gain a better understanding of the company.
Jim Liddle’s Background
What is Jim Liddle’s background? Liddle said:
“My journey in the tech industry began over 25 years ago – starting with hands-on coding and development, which eventually led me to start my own company, Storage Made Easy. There, we provided a multi-cloud data management and data protection solution that unified on-premises and on-cloud company storage assets. I served as CEO at Storage Made Easy for 10 years before its acquisition by Nasuni, where I now serve as Chief Innovation Officer, Data Intelligence and AI.”
“At Nasuni, my role focuses on data intelligence and AI, looking at both Nasuni AI product opportunities, feeding into the product team, and our own internal use of AI. My main responsibilities include working alongside our SVP Product, Nick Burling, to help lead the development and execution of the company’s data intelligence and AI strategies. This includes a major focus on helping to drive product innovation to enhance the Nasuni File Data Platform, and ensuring it continues to provide the cutting-edge features our customers have come to expect.”
“I also work closely with other cross-functional teams, to drive p improvements to ensure that businesses can ultimately leverage the full potential of their unstructured data sets for more informed decision-making.”
Latest Innovations
What are the latest innovations that differentiate Nasuni from other data management solutions in the age of AI? Liddle shared:
“Since joining Nasuni in 2022, our technology has continued to evolve to meet customer needs. Nasuni was founded on the premise of being a highly-scalable, secure, accessible, collaborative, and, importantly, cost-effective platform for all customers.”
“Within the last few months, Nasuni has released several products to help customers get to grips with their instructed data on the age of AI, including the launch of Nasuni IQ to unlock data silos for AI services. The NIQ platform provides data intelligence capabilities to help enterprises manage, assess, and prepare their unstructured data environment for artificial intelligence (AI). With Nasuni IQ, businesses can quickly monitor usage patterns, make proactive data management decisions, and better enable the delivery of intelligent insights.”
“Nasuni also expanded its integration with Microsoft 365 Copilot, which enables Nasuni managed data to be fully accessible and operational with Microsoft Search and Microsoft 365 Copilot, significantly expanding data access for Microsoft’s AI services.”
“Updates like these set Nasuni apart in this space, enabling companies to enhance their data strategy through painless migration to the cloud, hybrid cloud capabilities for where data has gravity, built-in security, and opportunity to scale easily – all necessary components to deploying AI effectively.”
Challenges Being Faced
What are the biggest challenges organizations face when trying to leverage their data for AI initiatives? Liddle acknowledged:
“According to Nasuni’s recent survey, up to 80% of a company’s data is unstructured, representing a massive and complex portion of the overall data landscape. This type of data, which includes files like documents, images, videos, and emails, has been traditionally more difficult to organize, analyze, and integrate into AI workflows compared to structured data stored in databases.”
“LLMs rely on an AI-ready infrastructure to process and analyze data efficiently. However, many organizations are held back by legacy, hardware-based storage systems that create fragmented silos of data. These silos make it nearly impossible to apply AI or analytics at scale across billions of files and petabytes of data. To overcome this, organizations should look to modernize their storage strategies by consolidating their unstructured data to make it more accessible. Doing so not only enables seamless access and analysis but also creates the foundation for unlocking the true potential of AI.”
Preparing For Successful AI Model Training
How can organizations best prepare their data for successful AI model training and deployment? Liddle noted:
“When it comes to training and deploying AI models, data is critical. Organizations need to be cautious that the correct data is being used within these models, as using incomplete, old, or unapproved data could result in bias, incorrect outputs, and a variety of data privacy violations.”
“A recent survey conducted by Nasuni found that almost half of firms will prioritize AI investments over the next 18 months, but in order to capitalize on this strategy, firms are recognizing the need to become more data-centric. The survey also found that when it comes to AI, better management and visibility of data are seen as more valuable outcomes than lowering costs, automating workflows, or improving customer experience.”
“This data underscores the necessity and validity of having a robust data strategy when looking to train and deploy AI models.
The best thing an organization can do when getting ready to deploy AI models is to ensure that its data is ready. This requires implementing three fundamental strategies:
— The first step is to assess the state and spread of your organization’s data. Organizations need to figure out how much data they have, its fundamental qualities and characteristics, and even where it lives, from the geographic location to the technical storage systems.
— Next, and what will likely come to light during the assessment phase, is that much of the unstructured file data is probably anchored to specific locations. These could be offices, data centers, factories, manufacturing plants, or creative design centers. If your entire corpus of data is distributed across multiple sites and stuck in these silos, your organization will not be able to get the most out of AI. Services need data, and that data needs to be in one place if they are going to do their best work; therefore, consolidating that data to the cloud is extremely important.
— The final step in making data AI-ready is to strengthen and tighten its security. You want to make sure you have control over your datasets and who can leverage them. The people using AI services must have the right and the authority to see and leverage only the data that they should have access to.”
Role Of Data Scientists And Engineers
How do you see the role of data scientists and data engineers evolving as AI technologies continue to advance? Liddle pointed out:
“With GenAI, we’re seeing an increase in non-technical users being able to wield the technology. GenAI models and LLMs are taking over the data science role by automating coding tasks, creating basic analyses and visualizations, and making data querying accessible through natural language.”
“However, even with this democratization of AI, data scientists remain essential. While some tasks may be automated, data scientists bring expertise to normalize data sets, validate LLM outputs, tackle sophisticated statistical modeling, and address AI bias and edge cases within unique business contexts. Data scientists remain crucial for strategic tasks, like framing the right questions, designing robust frameworks, and making critical decisions on methodologies. They also aid in providing governance by managing data quality, validating results, and promoting responsible AI practices.”
“This shift highlights that while technology has made basic design accessible to the everyday user, experienced architects, such as data scientists and data engineers, are indispensable for overseeing complex projects and ensuring structural integrity.”
AI Trends
What are the most notable data and AI trends that organizations should prepare for? Liddle concluded:
“As we enter the new year, it’s clear that data will no longer just support AI – it will shape and limit the scope of what AI can achieve. A robust data management strategy will be essential as AI continues advancing into unstructured data. The continued advancements in AI’s ability to process different types of unstructured data that reside within an enterprise require organizations to know what data they have and how and where it’s being used.”
“Companies that strategically curate and manage their data assets will be the most successful with AI, while those lacking a clear data strategy may struggle to move beyond the basics. A data-ready strategy is the first step for any enterprise looking to maximize AI’s full potential in the coming years.”
“Additionally, I think that in 2025, one of the most prominently used trends in AI for data management will be the widespread enterprise adoption of Agentic and Multimodal A. The latter of which combines and processes multiple types of data (such as text, images, audio, video, and structured data) to provide a more comprehensive understanding of information. Whereas generative AI text generation has had a lot of attention, multimodal AI unlocks a lot of use cases and benefits for a company’s file assets – as well as changes how organizations can interact with the reams of business data have stored in this type of structure.”
“Agentic AI will also have a huge impact on the enterprise on 2025. Think of agents as discrete AI driven business processes that focus on specific tasks that leverage organizational data autonomously, which brings us full circle back to the data that underpins any success companies will have with AI.”
