Data quality is fundamental, with the cost of bad data running into millions for UK businesses. Here, we look at how and why poor data is costing your organisation dearly, and the practical steps you can take to improve data quality. 

Bad data is costly. Research by analysts Gartner puts the average cost to organisations at a staggering $12.9 million a year – around £10 million. Whether that figure seems high or low to you will depend on a variety of organisation-specific factors, but the key point is that poor data is costly.  

As we become more AI-reliant, that cost is likely to increase. And for most, it’s completely hidden.   

Six ways data deficiencies could be costing your organisation

Poor data typically impacts organisations through operational waste, revenue leakage, and strategic shortcomings.   

1. Lost revenue  
   Data inaccuracy can result in poor decisions, causing lost sales, poor targeting, and underachieving campaigns. Old or incomplete customer data can mean missed upsell or cross-sell opportunities.  
2. Wasted time  
   Employees waste hours finding, verifying, or correcting information.  
3. Increased risk  
   Data errors cause GDPR and regulatory breaches, possibly incurring fines and certainly requiring time-consuming corrective action. Risk models that use poor data will fail to flag preventable issues. 
4. Negative customer experience 
   Incorrect contact details, preferences, or histories lead to irrelevant messaging or service errors, which undermine customer confidence, damage brand reputation, and add to customer churn.  
5. Operational inefficiencies 
   Errors in billing, shipping, or inventory management cause rework, returns, and delays – all of which carry a cost.  
6. Strategic failings 
   Decisions based on flawed insights can misallocate investment, degrade valid opportunities, ignore key risks, and cause growth strategies to fail. 

Why data matters more than ever 

As John Doublard, CTO at Oak Group and one of the industry contributors to ‘The Data & AI Readiness Playbook’, notes: “The future is data-driven.” 

“AI can only drive genuine business value when it addresses real business issues AND is fuelled by valid data,” explains Cloud Direct’s Data & AI Practice Lead Dan Knott. 

Understanding the limitations of your data, which parts need improving, and how to make those improvements is key to this.  

Eight practical steps for data quality improvement

Improving data quality isn’t just the responsibility of IT – although there are technical actions that will help – but something for the whole organisation.   

‘When you’re about to invest heavily in becoming a data-driven business, you need to make sure that the data you’re working with is going to provide for you and not cost you more’.

1. Define what ‘good’ means 
   If you’re working with bad data, you’ll get bad outcomes, so establish clear data quality dimensions. The Data & AI Readiness Playbook outlines the importance of accuracy, completeness, consistency, validity, uniqueness and timeliness. Add to this any business-specific requirements, such as capturing an accurate address or company registration number for credit checking.  
2. Perform a data audit 
   Identify your key data assets, such as CRM/ERP and finance data, and assess current data quality. Prioritise high-impact areas and make use of tools to help you highlight errors, duplicates, gaps, or inconsistencies.
3. Fix existing issues  
   Involve data owners (see below) with data cleansing. The objective is to de-duplicate records, fill in missing fields, standardise formats, and identify old and obsolete data for archiving and removal. Tools like Microsoft Purview and Power Query can helpfully automate some of the work. 
4. Embed good data governance practices 
   Appoint data owners and data stewards who will be responsible for data quality in key areas. Involve them in creating policies for data entry, usage, retention, and updates, and make use of Microsoft Purview, or similar, to enforce policies and manage access, lineage, and classification.
5. Educate and engage users 
   Data quality needs to be seen as a shared responsibility, and not IT’s problem alone. Run awareness campaigns or training to help users understand their role in maintaining good data. Within this, celebrate success by showing how clean, reliable data is enabling better decisions and results, and continue running campaigns until data quality is embedded in the organisation’s culture. 
6. Improve data entry at the source 
   You don’t want to be continually drawn into corrective action, so employ measures to improve data entry at the source. Add validation rules, drop-downs, and formatting controls where data is input, and ensure that departmental training covers the importance of entering accurate data. Wherever possible, automate data capture utilising integrations, forms with logic, and barcode scanners. Improving data quality requires both technical and cultural change. Start with what matters most, fix the root causes, and embed quality into everyday workflows. Over time, better data leads to faster decisions, happier customers, and lower costs.

Next Steps

Download a copy of The Data & AI Readiness Playbook to learn more. You’ll discover how others are preparing for and using AI, and a seven-step process to unlock the value of your business data.

Written by Cloud Direct CTO Paul Sells

In today’s fast-paced digital economy, artificial intelligence (AI) is a business imperative. From automating routine tasks to uncovering deep insights from data, AI is transforming how business operate, compete, and grow. 

But here’s the challenge: while many companies are eager to embrace AI, few have a clear strategy for doing so effectively and sustainably. That’s where an AI Centre of Excellence (AI CoE) comes in. 

What is an AI Centre of Excellence? 

An AI CoE is a dedicated team or function within a business that leads and governs AI initiatives across departments. It’s an internal AI consultancy, with the aim of bringing together the right people, processes, and technologies to ensure your AI investments deliver real business value. 

Rather than scattering AI efforts across siloed teams, an AI CoE provides a centralised hub of expertise, best practices, and reusable assets. It helps your organisation move from isolated experiments to enterprise-wide impact. 

Why Do You Need an AI CoE? 

Implementing AI isn’t just about buying tools or hiring data scientists. It’s about embedding intelligence into the fabric of your business. Without a structured approach, AI projects often suffer from: 

• Lack of strategic alignment: Teams build models that don’t solve real business problems. 

• Duplication of effort: Different departments reinvent the wheel with similar use cases. 

• Talent bottlenecks: Skilled AI professionals are spread too thin or underutilised. 

• Governance gaps: Ethical, legal, and compliance risks go unmanaged. 

An AI CoE addresses these challenges head-on by providing a unified framework for AI adoption. It ensures that your AI efforts are not only technically sound but also strategically aligned, ethically responsible, and scalable. 

Benefits of an AI CoE 

Establishing an AI CoE isn’t just a technical investment—it’s a strategic one. Here are some of the key benefits: 

1. Faster Time to Value with centralised expertise and reusable assets, AI projects can be delivered more quickly and efficiently. 
2. Improved ROI by focusing on high-impact use cases and avoiding duplication, the CoE ensures that AI investments generate measurable business outcomes. 
3. Stronger Governance the CoE provides a structured approach to managing AI risks, from data privacy to algorithmic bias. 
4. Scalable Innovation as AI maturity grows, the CoE helps scale successful pilots into enterprise-wide solutions. 
5. Empowered Workforce through training and support, the CoE builds a culture of innovation and continuous learning. 

Real-World Example: AI Centre of Excellence in a Law Firm 

Picture a growing law firm under increasing pressure to manage an ever-expanding volume of contracts, case files, and compliance documentation. Different practice areas are starting to explore AI—contract review here, legal research tools there—but it’s all happening in pockets, with little coordination. The result? Duplication, inefficiency, and missed opportunities. 

This is exactly where an AI CoE can make a measurable difference. 

By putting an AI CoE in place, the firm can: 

• Spot the highest-value opportunities, like automating contract intelligence and streamlining legal research and other use cases that benefit the entire firm, not just individual teams. 

• Bring together the right mix of people including legal operations, IT, data scientists, and senior partners, to co-create a firm-wide solution for reviewing, classifying, and extracting key terms from contracts.  

• Scale smarter, by adapting AI models across departments for consistent results and slashing time spent on manual reviews. 

• Build trust through governance, embedding legal ethics, client confidentiality, and compliance into every AI workflow. 

• Invest in training and change management, so that associates, paralegals, and support teams feel confident using these tools—not threatened by them. 

The impact? Engagements move faster. Risk reduces. Compliance becomes easier. And your top legal talent gets to focus on strategic advisory, not repetitive admin. Ultimately, it’s about delivering better outcomes for clients, and creating space for the kind of work that grows the firm. 

When external expertise is brought in to fill internal skill gaps which is often the case with AI projects, the AI CoE acts as the strategic bridge. Allowing alignment between external parties efforts and the firm’s goals, standards, and governance frameworks. 

How to Get Started

Ready to build your AI CoE? Here are the first steps: 

1. Secure Executive Sponsorship 

An AI CoE needs strong backing from senior leadership. Showcase how AI aligns with your strategic goals, whether that’s improving customer experience, reducing costs, or driving innovation. 

2. Define the Scope and Structure 

Decide whether your CoE will be centralised (a single team), federated (embedded in business units), or a hybrid of these. Start small with a few high-impact use cases and scale over time. 

3. Assemble the Right Team 

Look for a mix of technical and business talent. You don’t need a huge team to start—just the right people with a shared vision. 

4. Establish Governance 

Define clear policies for data usage, model validation, and ethical AI. Set up review boards or steering committees to oversee major initiatives. 

5. Invest in Tools and Infrastructure 

Choose platforms that support collaboration, version control, and model deployment. Cloud-based solutions often offer the flexibility and scalability you need. 

6. Measure and Communicate Impact 

Track KPIs such as cost savings, revenue uplift, or customer satisfaction. Share success stories to build momentum and secure ongoing support. 

Final Thoughts 

AI is a journey not just a one-off project. And like any journey, it needs a map, a guide, and a destination. An AI Centre of Excellence provides all three. 

By investing in a CoE, you’re not just adopting AI—you’re building the foundation for a smarter, more agile, and more competitive organisation. 

So, whether you’re just starting out or looking to scale your AI efforts, now is the time to consider: Do we have the right structure in place to make AI work for us? 

It’s no secret that businesses are producing more data than ever, and you will only be able to drive greater efficiencies and enable innovation once you have a clear strategy with the right tools in place.

Earlier this year, Microsoft launched Fabric, their all-in-one data solution, for general availability. Microsoft Fabric combines some of Microsoft’s most powerful tools, such as Data Factory, Synapse Analytics, Data Explorer, and Power BI into a unified, cloud-based platform to help simplify your data workflow. The combination of the tools will enable you to innovate with AI safely and securely by managing your data in a single user-friendly platform.