Data governance and AI readiness

Introduction

Many organisations today are inundated with data and face significant challenges in managing it effectively to derive value. Despite investing millions into different data management systems, point solutions, and data infrastructure, many businesses still can't seem to extract real value from all that information. Many organisations are intrigued by the promises of AI capabilities, yet for many, these benefits remain frustratingly out of reach due to unstable data foundations.

Industry research, and our work with clients across various industries shows a clear pattern: those who get their data governance house in order first are the ones succeeding with data and AI. This is not merely about establishing policies; it is about have the necessary monitoring controls that enable trustworthy data that is accessible, and usable throughout the organisation.

This article outlines practical steps to transform your organisation into one that's truly AI-ready.

Common data challenges and solutions

Poor data quality: Garbage in, garbage out

The problem: Low-quality data—incomplete, inaccurate, or outdated—undermines analytics efforts and erodes trust in insights. Industry surveys show, with consistency, that data quality issues are among the top barriers to successful data-projects and AI implementation.

The solution: Rather than attempting to solve all data quality issues at once, focus on high-value data domains first - usually customer, product, and financial data. Establish dedicated data stewards and implement general IT controls around your systems. These controls should encompass, but are not limited to, access management, change management, and automated validations.

Fragmented governance

The problem: When different departments define and manage data within silos, this gives rise to inconsistencies and fragmentation. These variations create reporting discrepancies and compliance risks that can derail analytics initiatives.

The solution: Establish a cross-functional data governance function with clear decision-making authority. Implement regular governance reviews where business units present their compliance with data standards. This accountability-driven approach helps ensure consistent data practices across the organisation.

Integration gaps - data trapped in silos

The problem: Data siloed across multiple systems prevents organisations from developing a comprehensive view of their operations or customers. This fragmentation limits the effectiveness of AI and data initiatives. As an example, B2C organisations might be unable to personalise experiences because they cannot see the whole customer journey.

The solution: Modern approaches like data mesh architectures balance centralised governance with distributed ownership. These frameworks enable faster integration of data sources while maintaining consistent standards and controls.

Building strong data governance foundations

Strong governance isn't about creating bureaucracy—it's about enabling faster, more reliable decision-making. Focus on these foundational elements:

1. Clear data ownership

Assign specific people (not committees) to own critical data domains who are responsible for quality, access, and compliance. When everyone owns the data, nobody is responsible for it. These owners should collaborate with technical teams to establish standards and resolve issues. Organisations with well-defined data ownership structures report fewer data quality incidents and faster resolution times.

2. Unified taxonomy - speaking the same language

Develop standardised definitions that cross departmental boundaries and enforce uniform data formats and standards across the organisation. When everyone speaks the same data language, collaboration becomes possible.

3. Metadata management - know what you've got

Document your data assets properly. This isn't just ‘busywork’ - it's how you discover you're maintaining three separate customer databases with 70% overlapping information. Good metadata management helps you consolidate redundant systems and find data when you require it.

4. Implement a robust general IT controls framework.

Having robust general IT controls around your systems is critical, as the absence of such controls can severely impact data quality. Essential IT controls include:

Data validation: Automated checks to ensure data accuracy and consistency during entry and processing. For critical data inputs maker-checker controls may be considered.

Enforcing role-based access controls: Ensuring that only authorised users have access to add, amend, or delete functionalities.

Implement a change management programme: Ensure that changes, customisations, upgrades, and patches implemented on our systems do not adversely affect the previously vetted data structures and controls.

Regular audits: Periodic reviews and audits to identify and rectify data quality issues.

Analytics and automation: leveraging your foundation

With strong governance in place, organisations can build with confidence capabilities that deliver business value.

1. From descriptive to predictive - crawl, walk, run

Organisations should follow a maturity curve, starting with descriptive analytics (what happened) and progressing to predictive (what will happen) and prescriptive (what actions to take) analytics. Industry leaders have used their trusted operational data to build predictive models that reduce downtime and optimise operations.

2. Automation that works

Automation built on bad data just creates problems… faster. By contrast, organisations with clean, well-governed data can automate with success complex processes across functions - from customer onboarding to supply chain management - while maintaining control and compliance.

3. AI readiness in practice

Before deploying AI solutions, organisations should assess their readiness across several dimensions: data quality, governance maturity, compliance requirements, ethical frameworks, and technical capabilities. This assessment helps identify critical gaps that need to be addressed before implementation.

Getting started

1. Assess where you really stand: Benchmark your current state against industry standards.

2. Prioritise by impact: Focus on a data domain with clear business value and visible problems.

3. Secure leadership commitment: Link governance improvements to strategic outcomes and to those which executives are interested in.

4. Implement a data governance programme: Establish a clear set of policies and procedures that clearly defines who does what. Define a minimum set of general IT controls that aid in achieving the policies objectives and have these monitored to ensure compliance.

5. Start small, learn fast: Data governance is a journey - begin with manageable initiatives, build momentum and review periodically to optimise and improve your practices over time.

Conclusion

Organisations leading in the AI race are not necessarily those with the most advanced technology or the largest data science teams. They are the ones that undertook the essential yet unglamorous task of organising their data infrastructure first—aligning systems, integrating controls, and developing a coherent strategy across their technology ecosystem.

By addressing these governance fundamentals, you will position your organisation to extract real value from AI, while others continue to chase superficial solutions. Those who neglect these foundational elements inevitably contribute to the growing number of failed AI initiatives.

Ask yourself: Are you building your data and AI aspirations on solid foundations or unstable ground?