Artificial intelligence can only ever be as intelligent as the data it consumes. As enterprises race to embed AI into decision-making, the battle for business advantage will be won not through algorithms but through data quality, structure, and governance.
The story of AI adoption is, at its core, a story about data. For all the talk of generative breakthroughs and digital reinvention, the technology’s success depends on the reliability of the information that feeds it. Without structure, context, and governance, machine learning quickly becomes machine guessing. For Stuart Harvey, Chief Executive Officer of Datactics, this is the reality many organisations are now confronting.
Harvey describes his company’s role in pragmatic terms. “Our customers have large, complex, and potentially messy datasets that they need to measure, improve, and match,” he explains. “We are right in the centre of all the potential that AI offers, because we provide clean, well-structured, deduplicated data into AI models. Without that foundation, nothing else functions.”
The analogy he offers is strikingly simple. “Everyone wants a fancy new kitchen or bathroom, but without a reliable feed of water, none of it works,” he continues. “Data quality is that reliable feed. It is what allows every other system to perform.”
The hidden plumbing of the AI economy
In an era where attention is fixed on large language models and generative systems, it is easy to forget that the real infrastructure of intelligence lies several layers beneath the surface. The unseen processes of cleaning, matching, and contextualising data form the foundation of every AI-driven enterprise.
For Datactics, this has placed its work in what Harvey calls “the data plumbing” of digital transformation. The company specialises in enterprise data management tools designed to measure and improve data quality continuously. Its platforms are used by financial institutions, government agencies, and healthcare providers—sectors where decisions have high stakes and errors carry real consequences.
“The bias of our clients is towards regulated environments,” Harvey explains. “The majority are banks, asset managers, insurance companies, and major government departments. Every police force in the UK uses our software for crime classification, and the NHS applies it across multiple systems. These are institutions that cannot afford uncertainty in their data.”
The lesson for executives is clear: AI without clean data is like automation without electricity. Yet while most organisations acknowledge the importance of quality data, few have embedded it into their operating models. The challenge is not conceptual but cultural.
Harvey observes that many companies have migrated data to the cloud without addressing underlying structural weaknesses. “There is a wave of change driven by the move to cloud environments,” he says. “Clients are facing the challenge of moving data off their existing databases and into the cloud. Migration itself is costly and complex, but the bigger issue is governance, ensuring that the data remains reliable once it is moved.”
Governance as the backbone of intelligence
Every enterprise with AI ambitions eventually faces the same question: who owns the data, and who ensures its accuracy? The answer, Harvey argues, lies in the rise of the Chief Data Officer (CDO) and the frameworks that support them.
Across both public and private sectors, organisations are turning to governance standards such as the Enterprise Data Management Association (EDMA) and the Data Management Association (DAMA) to formalise oversight. These frameworks define how data is catalogued, accessed, and maintained across the business, introducing the same rigour that finance departments apply to audit and compliance.
“Governance is the starting point for sustainable AI,” Harvey says. “A Chief Data Officer will oversee a range of tools, from master data management systems to data catalogues and lineage tracking. Our role is within that ecosystem: providing the means to measure data quality at every point in its journey.”
This continuous measurement is critical. Harvey outlines how organisations must assess data completeness, accuracy, and timeliness at multiple stages of processing, from ingestion to predictive modelling. “Only by measuring quality at each point can the CDO manage the process effectively,” he explains. “If a fault occurs at ingestion, you need to know what downstream systems are affected and how that impacts decisions.”
Such vigilance is not just best practice but a regulatory requirement. Financial institutions, for instance, operate under frameworks like BCBS 239, which mandate the ability to track and verify critical data elements in real time. “The demand for continuous, end-to-end data quality is now embedded in regulation,” Harvey notes. “Executives are expected to know the state of their data at any given moment.”
The misconception of built-in quality
A persistent myth within the data industry is that modern platforms handle quality automatically. Vendors such as Snowflake and Databricks often promote their own integrated data management functions, leading many executives to assume that quality assurance is embedded. Harvey believes this confidence is misplaced.
“These systems have some degree of data quality functionality, but it is quite rudimentary,” he explains. “They work at a column level, checking whether a value fits within certain bounds or whether a field contains legitimate characters. The real challenge is at a business level, joining data across multiple tables and sources to implement complex rules.”
True quality, in other words, is not about technical validation but contextual understanding. It is the difference between confirming that a postcode field exists and confirming that it matches an actual address. “We are working at a higher level of abstraction,” Harvey says. “That requires business logic, not just data rules.”
This distinction becomes critical as enterprises begin to integrate unstructured data, what Harvey describes as semantic data, into their workflows. “We have clients dealing with both structured and unstructured information,” he explains. “Structured data is easy to query and manage. Unstructured data is far more complex. It may come as a PDF attached to a record or as a text report that needs to be decomposed, analysed, and classified.”
In one example, police forces use the company’s systems to perform semantic analysis on crime reports. “You might have a three-page narrative describing an incident,” Harvey says. “From that, you must extract subjects, verbs, and objects to determine whether a knife was used and who the victim was. That requires a combination of natural language processing and domain expertise.”
Data readiness as a strategic discipline
Among the concepts Harvey returns to repeatedly is data readiness, a term often used but rarely defined. At its simplest, it refers to ensuring that data is accurate, complete, and available in a form suitable for regulatory or operational use. In practice, it is a measure of organisational maturity.
He describes a practical example from the UK’s Financial Services Compensation Scheme (FSCS), which requires banks to guarantee customer deposits up to a defined limit. In the event of a financial crisis, each institution must provide a complete list of customers and aggregate their holdings within 24 hours.
Behind this seemingly simple requirement lies a formidable technical challenge. “The bank needs to roll up all its customer records, even if the same person appears in different forms across multiple accounts,” Harvey explains. “They must reconcile spelling variations, incomplete addresses, and typographical errors to create a single golden record. The regulator will not wait for a week while the bank cleans its data.”
This scenario captures the essence of data readiness: being able to trust your data when the stakes are highest. “If the Bank of England calls, the data must be ready,” Harvey says. “That means accuracy, completeness, and traceability are non-negotiable.”
In the age of AI, readiness is not limited to compliance. It is the prerequisite for effective automation. Machine learning models cannot distinguish between high-quality and corrupted data; they will process whatever they are given. Organisations that neglect readiness risk building AI systems on unreliable foundations.
From regulation to real-time control
The evolution of data governance is now moving towards real-time measurement and control. Harvey points to global banking regulations that require continuous monitoring of data quality across all critical elements. “It is no longer about a single snapshot taken once a day,” he says. “Executives must be able to demonstrate quality at multiple points in the data’s lifecycle. That is about understanding systemic risk in real time.”
This shift reflects a broader truth about enterprise AI: automation cannot replace accountability. The same principles that govern financial risk management, transparency, auditability, and verification, must apply to algorithmic decision-making. “AI will only be effective if it is supervised, auditable, and explainable,” Harvey says. “We are dealing with technology that can make recommendations and suggestions, but human expertise is still required to recognise when it has gone wrong.”
That, he argues, presents a profound societal challenge. “AI can make experts, people with domain experience like accountants, lawyers, or doctors, far more effective,” he says. “It is like having a hundred free interns handling junior-level tasks. But if those junior roles disappear, how do we train the next generation of experts? Who supervises the machines when today’s experts retire?” It is a question that reaches far beyond data quality into the future of work itself.
The disciplined pursuit of intelligence
For Harvey, the real disruption of AI lies not in replacing human judgment but in amplifying it. The enterprises that will succeed in the next decade are those that combine technical sophistication with organisational discipline, those that view governance not as bureaucracy but as a competitive advantage.
Across both public and private sectors, he sees progress accelerating. “In government, there is a strong move towards adoption of tooling, staff training, and cross-departmental data governance,” he observes. “The motivation is often efficiency, they need to do more with less, but it is driving meaningful change.”
The same applies to small and medium-sized enterprises. “Even within our own company, we have realised the risks of data leakage, data poisoning, and model misuse,” he says. “We have put in place internal committees and principles to manage AI use systematically. Two years ago, I might not have thought it necessary, but it absolutely is.”
In an industry obsessed with speed, Harvey offers a reminder that transformation depends on patience, precision, and structure. Clean data, well-defined governance, and human oversight may not capture headlines, but they define the limits of what AI can achieve. “Without good quality, well-structured, well-matched data, AI will be ineffective,” he concludes. “Intelligence is not about what the machine can do; it is about what the organisation is ready to support.”
