AI was on the verge of mass adoption for years but only partially taking off until generative AI exploded onto the scene and turned heads worldwide.
Nowadays, businesses of all sizes across all sectors are actively exploring using AI in their day-to-day work as a strategic priority.
And for good reason. AI has become more accessible than ever, its value more visible than ever, and its ROI faster than ever. A recent Forrester study found that AI-enabled organisations can gain an ROI of 183% over just three years.
The issue is many organisations still need to reach the level of data maturity required to get the most out of AI. Some are collecting and storing data in an orderly fashion but may need the tools or know-how to analyse it and put it to good use. Others are only using data analytics for operational reporting and some strategic decisions.
But you must create a solid foundational layer to become a truly data-driven organisation that uses AI data analytics across all workflows to help inform and influence decisions. This means designing a modern data architecture, a framework for organising, storing, managing, and processing your data.
The anatomy of an AI-ready data architecture
So, what does a good AI-ready data architecture look like? It depends on your specific business needs — your architecture should be designed to support all use cases in your organisation. This includes not only technical work but business tasks, too.
Your data architecture needs must be flexible and scalable to be AI-ready. This means creating a data repository capable of handling and storing large volumes of data in various formats.
But don’t forget about data quality. Unfortunately, 90% of the data generated worldwide is replicated data, according to a recent IDC study, which predicts 7PB of data being generated globally per second globally by 2026.
In many organisations, departments and systems are siloed, and each team uses its own technology populated with its own data language, causing confusion and bottlenecks. For AI to do what you want it to, you need to provide clean, accurate, and up-to-date data that represents the real world. This is especially important when you’re training models to make predictions.
Designing a data architecture that’s fit for purpose
The first thing to remember when considering your data architecture is how it aligns with your business goals. Deploying an AI project that works great but misses the mark on your goals won’t net you the fast ROI you’re hoping for.
So, think about what you want to achieve with AI — streamlining processes, boosting productivity, retaining customers or something else entirely. Once you know what you’re trying to accomplish, identify the data you need to measure your performance.
Take a deep dive to find out how data-ready you are
Data governance is critical to data readiness — the policies, procedures, and standards ensure data is managed well.
Assess your organisation’s data capabilities and find any weak spots. A comprehensive evaluation should consider the entire data lifecycle — from data quality assessment to metadata management and mapping data lineage. Remember to closely review roles, responsibilities, and data security and privacy policies to ensure compliance with national and international laws.
A solid governance structure creates a shared language that enables all stakeholders to communicate effectively and ensure that data definitions are consistent across various tools. Simply put, it creates a single source of truth, and everyone has the same knowledge to work with, delivering unbiased, reliable insights and more intelligent decision-making.
Putting the pieces of your data infrastructure together
You need structured, unstructured, and semi-structured data from many sources to make AI work. Storage and access are key for running deep learning, natural language processing, and machine learning algorithms.
There are easy-to-use tools that allow companies to consolidate all their data in a single location — an approach that’s been heavily promoted for its promise of simplifying data management. But there is a downside to this approach that you need to keep in mind — centralised data can be inflexible and more challenging to scale, preventing your AI model from delivering quality insights.
An alternative option to data centralisation is the data mesh, which decentralises data by spreading it across the business, letting each team have ownership over the data they work with and take responsibility for sharing the data when asked for it.
Once you’ve sorted your data, you must find the right tech stack to support your use cases. Many options exist, like pre-built solutions, custom development, or the best of both worlds. Just think about data security, computational availability, your team’s technical skills, and how scalable the solution is.
Remember this is not a one-time setup; your data architecture should be regularly reviewed and improved. Whether it’s code updates, schema changes, or configuration updates, stay watchful to ensure your data architecture is aligned with your evolving business needs.
Don’t forget the human element
In the excitement of deploying AI tools, it can be easy to overlook the importance of human involvement in AI initiatives. While AI can automate many tasks, generating insights from data requires a diverse skill set and collaboration.
Make sure to consider the cross-discipline requirements of AI, invite input from multiple teams and stakeholders, and factor the human layer into your data stack.
Putting AI into action
Now, we’re at the extra fun part — integrating your AI models into your data architecture. Start with a proof of concept to test, refine, and validate assumptions and hypotheses using the data generated from your use case.
Once you’ve refined your model, deploy it across the enterprise. Continuously monitor your model’s performance and fine tune it where needed to improve accuracy, reduce false alerts, and ultimately meet your business goals.
As AI hype escalates, now’s the time to build a data architecture ready for AI. Remember, this process requires time, resources, and commitment. But once you have a solid foundation in place for all your data-related activities, you’ll be all set to unlock the full potential of AI in your organisation.