How to prepare your data and infrastructure for AI

The potential for AI to revolutionise operations, streamline processes, and boost efficiency is undeniable for businesses of all sizes. AI is dominating headlines and is a hot topic in businesses across the UK who are seeking to deploy AI solutions at speed to maintain their competitive edge.

In fact, a recent Gartner poll found 55% of organisations are in piloting or production Mode with generative AI. Meanwhile, more than half of organisations have increased generative AI investment in the last 10 months. This raises alarm bells. Diving headfirst into AI deployment without proper preparation can lead to complications, limited return on investment, and frustrated stakeholders. 

It is critical to do the right prep work. If you’re still at this stage, there are two key areas you need to focus on initially: data and infrastructure.

Start with your data.

Data is the lifeblood of AI. Having a deep understanding of your data gives you a significant advantage. AI thrives on large, high-quality datasets, which generally improve its accuracy and effectiveness. However, embarking on your AI journey without properly preparing your data can lead to a number of challenges:

• Insufficient or poor-quality data: This can lead to inaccurate or misleading results from your AI models.

• Outdated systems: Legacy systems may not be able to manage the volume, variety, and velocity of data required for AI.

• Unexpected costs: The cost of acquiring, storing, and processing large datasets can be significant if not planned for properly.

Fortunately, there are various data preparation tools available on the market that can help you cleanse, transform, and structure your data while reducing costs and simplifying your setup.

A holistic approach to data management

Once you have sorted and organised your data – how do you keep it that way? Establishing a robust data management process is crucial for successful AI implementation. Traditionally, data management and infrastructure have been viewed as separate initiatives, each with its own set of priorities. However, AI necessitates a more holistic approach.

A strong data management process ensures that the data feeding your AI models is clean, relevant, and well-organised. This comprehensive approach involves not only efficient storage and organisation but also guaranteeing data quality, security, and compliance. Collaboration between data engineers, data scientists, IT professionals, and business stakeholders is essential for defining and implementing best practices for data collection, storage, processing, and governance.

By integrating data management seamlessly into your AI initiatives, you create a solid foundation for building and deploying powerful AI solutions. With clean, reliable data at your fingertips, you can unlock the full potential of AI and transform your ideas into tangible realities.

Building the right infrastructure

Unfortunately, data is only half of the equation. To truly be AI-ready, businesses must invest in solid digital infrastructure. What does this mean? Think of AI like a jigsaw. Data is the puzzle pieces you're trying to assemble, and infrastructure is the table or surface where the puzzle is built. Therefore, the initial question you need to answer is where the puzzle pieces will be put together. 

Many businesses are opting for a cloud-first approach, which can help with reduced expenditure, regular new innovations, and generalised access to advanced tools. Alternatively, some businesses may choose to build and maintain their own on-premises infrastructure – potentially for reasons such as data security or regulatory compliance. Or, for some, a hybrid approach works best, which combines elements of both cloud and on-premises infrastructure, offering a balance of flexibility, security, and control.

The preferred approach will differ from one business to another, but the main goal is to find an infrastructure set-up which can be adaptive to your long-term needs. 

Once you’ve decided on your infrastructure at large, the next step is to navigate the vast landscape of tools and resources available to businesses. Broadly, these could include:  

• Pre-trained models: These are pre-built AI models that have already been trained on a massive dataset for a specific task, such as image recognition, natural language processing, or machine translation. They are like pre-made components that developers can leverage in their applications to save time and resources compared to training a model from scratch. 

• Customisable frameworks: These are software platforms that give you the foundation for building and using your own AI models. They provide all sorts of tools and features that developers can use to tweak their models and make sure they fit their project's needs. Imagine them as construction sets with different bits and pieces (functions, algorithms) that can be put together in different ways to build different structures (AI models) that are right for you.

• Open-source libraries: These are a bit like free code recipe books that developers can rummage through, modify, and use in their projects. They are a brilliant resource, letting developers benefit from other people's knowledge and work, saving them time and hassle when making their own AI solutions. 

• Low-code/no-code platforms: Low-code and no-code platforms are revolutionising the way software is built. They offer a visual way to design and develop applications, with little to no traditional coding required. They enable rapid deployment, empower people without a coding background to build tools and solve the problems they are facing, and reduce the burden on IT. 

Once you understand your needs, it's essential to understand how readily they can integrate with your data and infrastructure. This can be a challenging task for organisations with complex and fragmented data environments that have evolved over time. However, there are solutions available that provide a unified platform and the tools you need to simplify data integration and streamline operations. Remember: think broadly and long-term about your goals, and don't be restricted by your existing tools or infrastructure.

Hastings Direct is a great example of a company that has successfully migrated to the cloud (in their case, Azure VMware Solution) to enable AI to supercharge their operations. By investing in data and infrastructure, Hastings Direct boosted their application performance by 1.6 times, allowing them to adjust customer quotes four to five times faster and gain a market edge. This is just one example of how companies can benefit from sufficiently preparing their business to deploy AI models. 

The transformative potential of AI is clear for businesses of all sizes. Businesses just need to make sure they’re investing in data and infrastructure ahead of time if they want to truly realise AI’s potential and drive real innovation, efficiency, and growth.