Remember when we had to search through encyclopedias, newspapers and books to find answers to our questions? Maybe not — Google has been around for over a quarter-century now. But there was a time when it was new and novel and truly revolutionized how humankind accesses information.

Well, AI is the new Google of this era. Both individuals and organizations have realized the value AI provides and are using it to do everything from drafting emails and generating meeting summaries to more strategic applications, like:

• Laser-accurate targeting
• More precise segmentation
• Hyper-personalization throughout the customer journey
• Deeper data analysis
• Proactive customer support to drive satisfaction and loyalty

While some businesses have seen the positive impact of AI, many are struggling to realize the value of their AI initiatives. So, what gives?

Hint: it's the data. Reliable and trustworthy data is the foundation for high-quality AI outputs. The outputs needed to power personalization, a better customer experience (CX) and more.

We're exploring the role data quality plays in getting more value from your AI and how master data management (MDM) can help turn your AI efforts into a strategic driver of innovation and sustainable growth.

Garbage in, garbage out

As the saying goes, when you put garbage in, you can expect to get garbage out. What that means for AI is that the outputs you get will be only as good as the data you feed it.

Think of AI as a house and data as the foundation. No matter how great the building materials are, if your foundation is shaky, the house will eventually fall apart. You need quality data to fuel AI.

"Even the most sophisticated models and platforms can be undone by a single point of failure: poor data quality. This challenge remains deceptively overlooked despite its profound impact on strategy and execution. The decisions you make, the strategies you implement and the growth of your organizations are all at risk if data quality is not addressed urgently."
- Vipin Jain, IT Strategist at Transformation Enablers
*CIO's Data’s dark secret: Why poor quality cripples AI and growth

Clean, governed, trustworthy data will get you reliable insights, better decision-making and better campaign performance. But low-quality, fragmented data? That'll get you:

• Flawed insights or predictions that could lessen the effectiveness of your marketing
• Biases introduced, spread and amplified by skewed or incomplete data — these could introduce legal and ethical risks
• Broken customer trust that leads to decreased loyalty and potential financial losses
• Unreliable outputs that can lead to missed opportunities
• Reputational damage from decision-making based on bad data and insights
• Financial losses and fines caused by inaccurate forecasting and compliance issues

These are just a few of the ways low-quality data can transform your AI models and tools from a strategic advantage into a potentially harmful and costly mistake.

AI’s Achilles heel

With so much potential, we often forget that AI does have its limitations. The biggest hindrance to AI success? Bad data. Much like the arrow in the mythological story of Achilles, low-quality data makes your AI vulnerable, rapidly destroying any value you hoped to get from your AI outputs.

"AI can personalize, predict and perform — but only if fueled with data that is accurate, complete and governed. Otherwise, you’re just scaling mistakes.”
- Gustavo Cyrillo Amorim, CMO at Stibo Systems

You cannot trust AI if you're training it on inconsistent, duplicated, outdated and siloed data. So, before you jump on the AI bandwagon, take some time to get your data right and build a solid data foundation — one built around quality and governance.

If you skip this step, expect:

• Your AI to learn the wrong patterns, make flawed predictions and provide biased outcomes
• AI models to deliver unreliable outputs that lead to poor decision-making and diminish trust in AI-driven decisions
• Drops in efficiency caused by an increased need for human intervention to course-correct
• Slowed AI deployments and scaling efforts caused by inaccuracies
• To fail compliance checks — especially under regulations like GDPR or the EU AI Act

It sounds all doom and gloom, but there's a straightforward solution — fix your data. Cleanse it. Deduplicate it. Govern it. Move it out of silos. Make data quality a priority. Once you do, you have AI-ready data that can help you get the most value out of your AI efforts.

So, what's AI-ready data? Simply put, it's reliable, consistent and governed data that allows for effective AI implementations.

AI-ready data in action

You know what AI outputs look like when the data's not great, but what about when your AI's powered by high-quality data? Take a look:

• It increases efficiency by automating mundane tasks and using AI agents to provide exceptional, proactive customer support
• It helps CX teams deliver real-time, personalized interactions by providing instant access to nuanced customer insights
• It scales personalization by creating tailored content and helping marketers more accurately segment and target customers
• It increases customer satisfaction and loyalty by providing on-point customer insights
• It generates product descriptions in seconds to help marketers speed up launches and boost efficiency
• It localizes content quickly to make your content accessible to global audiences — driving revenue growth

The use cases are virtually limitless if you have clean, governed and trustworthy data to power your AI.

Addressing data quality issues before they destroy your AI

Data quality issues aren't something you can put off until next week, especially if you plan to launch AI across your enterprise or you're already using it.

But fixing your data problem isn't as simple as governance or making sure new data is clean — it's about fixing existing data, establishing governance frameworks for all data and making sure any new data that comes in is clean and reliable.

Not sure where to start? Follow this checklist:

While this checklist is a strong starting point, MDM is the most effective way to ensure data quality, delivering trustworthy and reliable data to fuel AI initiatives.

What is master data management?

MDM centralizes, cleanses and manages your data, creating a single source of truth for your master data.

"Organizations that prioritize trusted data don’t just make better decisions. They create a foundation for lasting advantage."
- Vipin Jain, IT Strategist at Transformation Enablers

It delivers high-quality data by making sure all data is accurate, complete, consistent and trustworthy — sharing that data across enterprise systems to ensure consistency.

That trustworthy data helps you create tailored touchpoints along the customer journey and get more ROI out of your marketing campaigns by giving you a complete (and accurate) view of your customers.

And because master data management remedies data quality issues for new and legacy data, it speeds up the time to value for your AI initiatives.

How MDM helps AI reach its potential

MDM gives AI the foundation it needs to provide actual value — high-quality data. But it does more than simply deliver reliable data. Marketers and CX leaders can turn AI potential into results using MDM in a few ways.

1. It gives you AI-ready data that's precise enough for laser-accurate targeting, on-point personalization and unforgettable experiences
2. It helps you understand your customers better by providing a 360° view — the foundation for any marketing or CX initiative
3. It applies governance to ensure you're compliant with industry regulations like HIPAA and GDPR, building trust with customers (and internal teams)

Think of MDM as the gasoline that drives your AI. Without it, you're stuck, you can't go anywhere. But with it? The potential is limitless.

Isn’t it time you started getting real value from your AI?

There's no denying that AI has the potential to do big things. And when it's powered by trustworthy data, it just might be unstoppable. To get that high-quality data, though, you need a strategy and the tools to support that strategy — like MDM.

See how to get the most value from your AI with MDM by checking out our ebook, Get More Value from AI with Master Data Management: A Guide for Marketing and CX Teams Embracing AI.