Yes, AI is overhyped… but it’s also underutilised. So get your data in order.

Over the last two years, you’ve been told repeatedly that Artificial Intelligence (AI) will revolutionise how we do business in 2025 and beyond.

AI promises to enable organisations to improve customer relationships, automate activities, optimise processes, generate content and create competitive advantage while delivering deeper insights from their data.

However, there is a massive difference between potential being available to your business and realising that potential. Strong data foundations are assumed to be in place ubiquitously across financial services, health and energy, but this couldn’t be further from the truth.

For AI to truly become the positive, transformative force that Silicon Valley VCs suggest it will be, we need to trust and have faith in our data… And for many organisations, their data is siloed, disparate, structured, unstructured, across multiple cloud platforms and more.

So, let’s talk about getting your data ‘house’ in order.

—

Most technological advancements come from cheaper access to two things… data and compute.

Data touches all parts of every organisation. Customer data gives you access to your customers’ attitudes and behaviours, allowing you to understand their needs and build better experiences. Data collected from operational functions and backend systems lets you see how an organisation works as a system, a value chain, and how it is wired up.

Knowing your data, put simply, means truly understanding your organisation.

The challenge that many organisations face into is they have some data in central data warehouses, some in relational databases, some in disparate 3rd party platforms and some sitting around in XMLs and Excels! We even come across SOAP APIs this past month (not that there’s anything wrong with SOAP) but it doesn’t mean the data is clean!

Unifying your business data in a modern, scalable architecture can allow you to take all types of data (structured, semi-structured, unstructured and raw data) and prepare it in a central repository that allows you to open up the opportunities for optimisation, refinement and leverage of data for business benefit.

Why your data preparation process is critically important

Preparing your data is not only about collection. Collection is a tiny part of the data preparation lifecycle. Data preparation to ensure compatibility, accuracy and consistency is an extensive process that includes:

1. Data collection: Data collection is about mapping and collecting data from relevant data sources, this is the foundational step in a modern data practice. If you don’t capture it. Does it even exist?
2. Data classification: Before data is ingested into a data pipeline, you need to gather it from across your organisation and pinpoint the relevant data. This part of the process requires an in-depth understanding of the data sources, systems and the context of the data.
3. Data cleansing: Dirty data is a major problem impinge on the accuracy of your  insights. Organisations believe that about a third of their data is inaccurate, which goes to show how widespread the issue is. Cleansing involves the detection of corrupt, irrelevant, or missing data – and the correction process during which it is modified or replaced.
4. Data structuring: It’s vital for any organisation to decide on a specific data structure that the preparation process should map into. Structuring the data based on business entities (such as people, products etc.) can be highly effective in making the data suitable for both analytical and operational workloads, or you might wish to structure the collected data using common business terminology and a schema that matches the business objects at the core of your workloads.
5. Data enrichment: This step is equally about additional information, context, modification and anonymisation/ pseudonymisation. It is about transforming your data to fit the chosen structure from the previous step and ensure the viability of the data within the chosen output structure.
6. Data validation: This is the closing step of your data preparation process, and it will ensure that the transformed data is accurate and ready for your data structure. Data delivery assures that the prepared data is securely pipelined to the relevant target applications, data lakes, and data warehouses.

Not only are each of the above steps vital to establishing great data fundamentals but once they are in place, you will want to establish standards to ensure that the process as simple, error-free, automated (where possible) and up-to-date as possible.

Your data preparation positions you to create value

Machine learning and AI is only ever as good as the data it is fed – in other words, poor input, poor output.

Alongside a modern data repository and a fundamentally well-structured data preparation process, your business will also need a solid data engineering practice that includes processes, roles and responsibilities, technology, and ways of working.

By embedding a robust IT and operational infrastructure, you will able to handle large amounts of data, alongside the dynamic and flexible storage and processing capabilities.

Your data engineering approach is incredibly important here, as your organisation will really need to understand the alignment between engineering, ops and IT. In many businesses we see analysts doing data engineering work and ML engineers assuming the responsibilities of a platform or release engineer and whilst it’s great to have polyglots in the team willing to take this on, deeper specialist expertise will always win out, when it comes to setting up an enterprise data team.

Alongside your data engineering approach, you will need a data governance framework to ensure compliant data handling in line with regulations and policies.

A good data governance framework includes rules and standards that enable consistent, secure access to data across the whole organisation. The framework must ensure data is collected, managed, and used securely and ethically to promote transparency and accountability.

If all of this sounds like the kind of data talk you need to hear, well, you are in luck.

Waracle’s view on data foundations

At Waracle, we have supported our clients in highly regulated spheres with modern, scalable data infrastructure and have planned and architected the data pipelines that allow data analysis, advanced analytics and data science.

One of our client engagements began with the development of multiple health apps, which lent itself to data collection on a vast scale, with tens of thousands of engaged participants delivering real-time data in tandem.

So once we had proved ourself as a high-quality provider of the health companion apps, we were asked to build the related infrastructure to store, manage and present the petabytes of data being generated. We collaborated with our client to bring our expertise to data collection, data management, cloud infrastructure, data movement, data validation and data visualisation.

Our team understands the intricacies of businesses both from a consumer-facing side and from an operational infrastructure perspective, so we are uniquely positioned to support you build great data foundations that will underpin the intelligent digital products and experiences your business will build in 2025 and beyond.

Reach out to one of our team today to discuss your needs today.