Creating AI foundations with strategic data management

The journey to AI success often starts with a solid data strategy – and for good reason. Think of data as the fuel for AI. If it’s reliable and accessible, your tools can perform at their best. But when data is inaccurate, inconsistent, or trapped in data siloes, even the smartest AI will struggle.

That’s why building strong AI foundations matters. A practical data strategy should make data easy to access, break down barriers to ROI, and lay the groundwork for long-term AI success.  

While it’s possible to launch an AI deployment without a defined data strategy, doing so is often a recipe for disaster. Common challenges may inconvenience users but can have a much more drastic impact on AI tools, which can’t problem solve in the same way human users would in order to get around these roadblocks:

• Data siloes – siloed data is not a new challenge for data strategy, but it can significantly hamper AI, as well as your users. AI can only work with the information that’s fed into it, and disparate systems limit the range of information it has access to. This limits the context available and hampering the insights it can offer, as well as its ability to surface information.  
• Unprepared infrastructure – depending on your storage strategy, your infrastructure may prove to be a roadblock to AI success. Legacy systems can slow AI down to a crawl with poor response times, while large volumes of unstructured data can hamper the ability for AI tools to search for specific items, and hide other potential issues that impact AI performance.
• Limited governance and oversight – in larger environments, it’s all too common for teams to lose track of what data their users are storing, creating a significant compliance risk. AI tools with access to ungoverned data sources can serve confidential information to users who shouldn’t have access, heightening the risk of a breach and jeopardising data security frameworks. 

Research conducted by IDC and sponsored by Lenovo has found that data strategy is a blocker to AI progress, holding back businesses that are still planning their AI deployment, and those who have already adopted AI alike.¹ By taking a critical look at your data strategy, and using it to lay the foundations for AI success, your organisation can help maximise the ROI of your first deployment, and set yourself up to scale rapidly in the future. 

Preparing your data strategy for AI may seem like a mammoth undertaking, but by focusing on a few focal points, you can maximise progress, break down data siloes, and roll out your AI foundations.

First, it’s critical to understand the difference between structured and unstructured data. Structured data is typically organised into a rigid format – databases, spreadsheets, and other similar file types that are easy for computers (and AI) to query. Unstructured data is everything else – emails, documents, meeting notes – it's all useful, and adds vital context for AI, but it needs some preparation before use.  

Below are four practical steps that can help streamline your approach to data and bring it into sync with your AI plans:

1. Organise Unstructured Data

   Around 90% of data in an average business is unstructured² – that doesn’t mean it’s useless, but to deliver the most value for AI, it needs to be properly organised. For example, huge amounts of useful information are stored in text documents, images, and video files. While many AI models can process these files, if unstructured, finding the right details slows processes down to a crawl. In contrast, organising the metadata of files to track useful information – such as transcripts or the customers the documents relate to – allows for faster searching, and, in turn, more useful input from the AI.

2. Connection and Integration

   By breaking down data siloes to ensure information can flow freely between teams and platforms, you can establish a unified data repository that acts as a single source of truth. This helps AI tools and users alike draw connections between data and business outcomes- such as linking marketing and sales data to provide insightful reports into the success of an advertising campaign. 

3. Improving Accessibility and Awareness

   Data accessibility is a critical success factor for AI – but it’s equally important to make sure you can preserve it as you grow. By building awareness in your users, and establishing automated checks, you can make sure new data is properly tagged, organised, and accessible, helping support your AI strategy in the long term and providing an easy way to ensure and prove compliance. 

4. Preparing for Growth

   As your data volumes grow, they can fuel your AI strategy, if managed well. Make sure data storage solutions have room to scale without compromising on other considerations, like data sovereignty. Lenovo ThinkSystem™ solutions make scaling simple and keeps your AI on-premises, keeping you in control. When processing data at scale, energy efficiency matters. Lenovo Hybrid AI Advantage™ with NVIDIA helps address this with a portfolio of powerful AI-ready infrastructure – featuring 6^th Gen Lenovo Neptune^® Liquid Cooling technology. It delivers supercomputing to organisations of all sizes and efficiently powers the NVIDIA Blackwell platform and AI workloads at scale – helping you stay in control while optimising for sustainability and cost. 

Focusing on these fundamentals can help you identify how your data strategy needs to change to support AI and makes it simple to lay the necessary groundwork. 

Once your data is in order, AI needs the right environment to run efficiently. Hybrid infrastructure allows data to be collected, stored, processed and fed into AI models for training in the hybrid cloud, providing inferencing at the edge, or on devices with training models. The result is greater flexibility and control, without compromising on scalability.

Lenovo Hybrid AI Advantage™ with NVIDIA provides a flexible hybrid AI framework of production-ready use cases for scalable, secure AI.  Built on Lenovo’s AI-optimised infrastructure and devices, together with NVIDIA AI Enterprise, accelerated computing, and networking, it helps break down the barriers to ROI and tackle common pain points – delivering content creation 8x faster, and boosting productivity with AI assistants by up to 80%.³  

Lenovo’s high-performance infrastructure features prevalidated configurations, ThinkShield™ security, responsible AI frameworks, and integrated governance with AI-powered data orchestration. Together with secure storage solutions such as Lenovo ThinkSystem™ DM and DE Series, you can ensure your data is protected, accessible, and seamlessly managed across hybrid environments – helping you scale AI confidently and efficiently.

Access this solution brief to see how Lenovo, Centific, and NVIDIA enable AI-powered data orchestration to accelerate adoption, reduce deployment risks, and unlock new revenue opportunities through intelligent automation and deep insights. 

We offer expert-led Ideation Workshops – backed by Lenovo and NVIDIA - sessions that can help you breeze through your first AI deployment by building a comprehensive roadmap to success. Whether you’re finding a target your initial deployment, aligning your data strategy to your AI goals, or want to further optimise on tools you have in place, we can help you find your next step.

If you want to transform your data into a genuine enabler for AI, book an Ideation Workshop today and build the data strategy that will support your next stage of innovation. 

1. IDC (2025), CIO Playbook 2025: It’s Time for AI-nomics. (Sponsored by Lenovo).
2. McKinsey & Company (2024), Charting a path to the data- and AI-driven enterprise of 2030.
3. Lenovo (2025), Lenovo Hybrid AI Advantage with NVIDIA boosts business productivity and efficiency with new scalable agentic AI solutions.