How Data Hydration Enables Scalable and Trusted AI

It’s accepted wisdom that AI models, whether they underpin large language model (LLM) chatbots or reasoning models that power autonomous agents and robots, need to ingest and process vast quantities of data for both training and inference.

Training – which gives the model its intelligence – is a batch-oriented process performed before the model is pressed into service, and then possibly at periodic intervals to keep it current. Depending on model complexity, it can draw on anywhere from thousands to millions, or even billions, of data elements.

While training is not considered to be a real-time operation – it can typically take from hours to months to perform – improving the performance of the training infrastructure and software processes is fundamentally important in delivering new models to the marketplace as quickly as possible.

Meanwhile, Inference, where a trained model applies its intelligence to new data inputs to make predictions, decisions, or generate content, needs to perform in real-time with as little latency as possible.

For sure, there’s an awful lot of data in this world – one prediction suggests there will be 394 zettabytes of it by the end of 2028. A zettabyte is 10²¹ bytes, or 1,000 exabytes, 1 billion terabytes, or 1 trillion gigabytes. Just 1 zettabyte is the equivalent of 250 billion DVDs worth of data.

So, the quantity of data for AI is not a big issue per se. But to be useful for AI, in terms of delivering accurate outputs and enabling efficient real-time processing, data needs to be of high quality. This is where Data Hydration comes in.

See also: The Cost of Doing Nothing About Your Data: Why IT Is Drowning

Why Businesses Need Data Hydration

Before we dive into what Data Hydration is, let’s first consider why it is needed. For starters, raw datasets – whether they be sourced from a stock exchange ticker feed, an e-commerce website, or an IoT temperature sensor – are often not sufficiently accurate for AI to readily interpret them. They can contain erroneous data elements that have not been validated and cleansed (by removing data or correcting it) – activities that are time consuming and costly to perform.

Furthermore, datasets are often not complete, either because specific data is simply hard to come by or because the owners of the data want to keep it private for personal or commercial reasons. Increasingly, the notion that data has real value is becoming understood, giving rise to the narrative that “data is the new oil.”

Even in today’s zettabyte universe, AI model builders are finding it increasingly difficult to harvest high-quality data for free from the public internet, forcing them to agree commercial deals with content owners, such as media companies, book publishers, healthcare providers, retailers, or even individuals.

Commercial arrangements often require model builders to comply with governance directives and implement provenance functions to ensure that data sourcing and enrichment is tracked and audited, and that data itself is leveraged ethically, according to contractual terms.

Understandably, training AI models on erroneous or incomplete data can cause them to produce outputs that are below par. Outputs can be biased or include hallucinations. In such cases, the models can generate outputs that are misleading, or even nonsensical, but which are presented as facts. All too often, the impression is that AI models guess what a response should be, though they don’t always state that it’s not certain when presenting results.

Sometimes the best route to compiling a complete dataset is to tap into synthesized data. But this process often requires the use of cutting-edge, though time-consuming and power-hungry, cryptography to ensure that the synthesized data mimics actual data but does not inadvertently reveal it, or the owners of it.

Even when datasets are accurate and complete, they often lack organization or structure, making it difficult to extract data elements to use for AI processing. This includes determining the relationship between data elements and the context of what the data might relate to.

Data organization approaches – including contextual labelling, construction of ontologies, and creation of knowledge graphs and time series – can be adopted to organize data in a way that is optimized for efficient AI processing. But these processes take time and burn compute cycles.

And efficiency is important given the high cost of setting up and running AI infrastructure, including costly GPU chips, datacenter facilities, power costs, and water costs (for cooling). Scaling AI to meet the demands of business and consumers is a constant challenge, especially as interest in models to power AI agents or to generate video is rapidly increasing.

Meanwhile, AI model builders are still attempting to determine what business and consumer customers will pay for AI services. There is no doubt that many AI services provide value and make humans more efficient, but quantifying that value to set price points is tricky, especially in a competitive market.

It’s in the context of these technical challenges and commercial experimentation that model builders are, by necessity, looking to improve the quality of the raw data required by increasingly complex models.

See also: Why Data, Not Tech, Drives Digital Transformation

What is Data Hydration?

Enter Data Hydration, an emerging term that was coined just a couple of years ago by venture capitalist Gerry Buggy of Iona Star Capital. His firm is focused on investing in technologies that operate at the convergence of AI and access to data. A key focus of the firm’s investment thesis is minimizing the cost base of delivering AI to customers who want to use it.

So, where does the hydration word come from? In nature, hydration is the process of absorbing essential water, or the state of an organism being supplied with adequate water to thrive. Relatedly, Data Hydration covers the activities that support AI models being supplied with an adequate supply of essential information to allow them to thrive. Such activities include:

• Validation and Cleansing: Ensuring data is accurate by eliminating (and correcting where possible) errors and bias.
• Enrichment and Organization: Combining data inputs and leveraging synthetic data to fill critical data gaps, and leveraging ontologies, knowledge graphs, and time-series to organize and add context to datasets.
• Governance and Provenance: Establishing transparent data sourcing and audit trails for data ownership and usage compliance, and to ensure ethical AI.

The technology landscape of companies engaged in developing Data Hydration tools and services is expanding as AI model builders and service providers come to understand the data challenges associated with delivering AI efficiently at scale, and which is trusted by customers.

See also: Why Layered and Agentic AI Demand a New Kind of Data Infrastructure

A Final Word

While Data Hydration is a new term to cover a set of disciplines that have often been addressed separately in the past, practitioners are increasingly considering holistic approaches to the data quality needs of AI. Expect the coming year to see much innovation in this space as businesses continue to adopt AI and as agentic AI applications become more commonplace.