Sometime around 2005, self-service business intelligence (BI) applications like Tableau and Qlik rose in prominence, displacing IT-centric solutions like Congos and Oracle Business Intelligence Enterprise Edition (OBIEE). In switching to these BI tools, we were promised actionable insights and real-time visibility for our business operations.

Now, us data-savvy business users could create our own reports, with whatever data we could access – without having to wait for IT. This paved the way for data to become the foundation of decision-making in departments like finance, human resources (HR), IT, and marketing. 

From operational dashboards to predictive analytics, BI data has flourished, and businesses rely on it to drive outcomes. But here’s a critical question: How much of your business relies on data-driven decisions, and when was the last time you questioned the accuracy of that data?

Looking at the dashboards below, what dashboard and set of metrics can you trust? Well, for many organizations, there’s no way to tell–but we’ll come back to that later in this blog.

Despite advancements in AI-driven insights, organizations still struggle with a key issue—trust. More often than not, metrics and reports fail to deliver on their promised clarity, value, and insights, leaving business leaders and knowledge workers uncertain about whether their decisions are based on reliable information. 

In reality, without trustworthy data behind those metrics surfaced in reports, even the most advanced tools can lead you astray. What is the true cost of such untrustworthy metrics? Misguided decisions, missed opportunities, and strategic failures.

More data, less trust?

As businesses scale their data-driven initiatives, investing heavily in automation, predictive models, and AI, they often assume that more data automatically leads to better decisions. But the truth is that increasing data volumes simply creates data noise, not clarity. Like the pervasive aether of medieval times, data noise is omnipresent and can obscure business reality for organizations.  

Think about it: how often do you see conflicting dashboards in your organization? Reports from different teams using slightly different datasets or definitions? This metrics sprawl leads to confusion, erodes trust in data, and ultimately makes it harder to make informed decisions. 

RaceTrac, a retail service station provider with over 800 locations had this challenge. “Each business and department used its own formulas, processes, and definitions to create reports. As a result, and even with the same data, the reports could contain wildly different conclusions and recommendations. What’s worse, reports based on transactional data could take as long as 24 hours to produce since the company had no visibility into real-time data.” 

Without a clear governance framework, more data doesn’t result in better insights—it results in more uncertainty. The real challenge for organizations isn’t simply gathering more data; it’s ensuring that the data is accurate, relevant, and trusted.

AI readiness: More than just technology

Many companies today claim they are “AI-ready.” They’ve hired data scientists, invested in machine learning (ML) models, and built data infrastructure. On paper, they appear set for success. But true AI readiness doesn’t come from technology alone—it starts with trusted data.

AI systems are only as reliable as the data they’re built on. In general, data scientists spend more time cleaning and preparing data than building AI models–they’re essentially data janitors! It’s a familiar story across industries. Even the best algorithms in the world can’t compensate for poor data quality.

Take the Domain Group, for example, a real estate marketplace company that struggled to create AI models without visibility and a comprehensive understanding of its data assets, user rights, sources, lineage, and data quality. Domain couldn’t leverage AI models for new customer insights.

AI readiness is fundamentally about data maturity. If your data isn’t trusted, well-governed, and up-to-date, your AI will produce unreliable results, leading to flawed insights and wasted resources.

The shift toward data-centric AI

For years, businesses focused on model-centric approaches to AI, refining algorithms to get better results. But there’s a growing realization that data-centric AI is the true key to unlocking value. This approach shifts the focus from improving models to improving the quality and consistency of the data.

Consider the impact of poor data quality on a predictive AI model designed to forecast customer churn. If the historical data feeding the model is incomplete or biased, the predictions will be inaccurate. No matter how sophisticated the model is, it cannot compensate for bad inputs.

Data-centric AI emphasizes iterative improvements to the data, ensuring its quality, consistency, and relevance. In the long run, improving data quality delivers far greater value than fine-tuning algorithms. This shift toward data-centric approaches is fundamental for organizations that want to harness the full power of AI.

Enhancing LLMs with metadata: the role of RAG

Large language models (LLMs), like GPT-4, is fundamentally altering how businesses conduct their operations. But these models face significant challenges: organizations often don’t control the data these models are trained on, making it difficult to ensure the accuracy and reliability of the outputs.

One way to enhance LLMs is through Retrieval-Augmented Generation (RAG), which improves model outputs by providing context from trusted data sources. This is where metadata plays a crucial role.

Think of metadata as the library card catalog for your AI systems. Just as a well-organized catalog helps you find relevant books quickly, metadata allows LLMs to retrieve the most accurate and contextually appropriate information. When LLMs are paired with metadata-driven RAG, they can deliver more reliable, accurate, and context-rich results, significantly reducing the risk of hallucinations and confabulations—where the model generates incorrect or misleading information.

Metadata is foundational to RAG implementations

For example, in a recent webinar with partner BlueCloud, we asked Snowflake Cortex to create a forecasting model based on available data. However, it could not achieve this on its own. It required the metadata from Alation’s Data Intelligence Platform to provide context to the RAG so it knew what the tables and columns meant – as well as what could be trusted and what could not.

Alation is the foundation for RAG systems

The hidden costs of ignoring data quality

It’s easy to think of data quality as a technical issue, something that’s handled by IT or data science teams. But the truth is, data quality is a strategic issue that can affect every aspect of your business.

Poor data quality leads to confusion, inefficiency, and mistrust. When different teams rely on different versions of the same data, it’s inevitable that conflicting reports will emerge. How many meetings have you attended where teams debated which numbers were correct? These debates don’t just waste time—they cause friction and erode trust across the organization.

For example, one airline confronted inconsistent definitions, leading to chaos. As one Data Lead put it, "Executives were getting inconsistent information from different areas of the business, which made decision-making challenging." The source of the problem? Inconsistent data definitions and poor data quality controls. This lack of clarity led to delayed decisions, missed targets, and diminished confidence in the data itself.

The cost of poor data quality goes beyond internal inefficiencies. It can affect customer retention, product development, and financial performance. Would you let your CFO report financials based on flawed data? Of course not. Then, why allow product or operational decisions to be driven by incomplete or inconsistent information?

Data products: From data assets to actionable value

It’s not enough to have data assets like dashboards or reports—those assets must be trusted, used, and understood across the organization. Without trust, even the most insightful dashboard is just another window into the noise.

Data products are only valuable when they are transparent, governed, and trusted. Too often, companies invest in building data products, but they remain unused because employees either don’t trust the data behind them or they don’t understand how to engage with it.

Imagine you’re building a car without an engine. It might look impressive, but without the engine, it won’t get you anywhere. Building data products without trusted data is the same.

To ensure adoption and value, data products must be well-documented, easy to access, and understood by the users who rely on them. If not, the time and resources spent creating these products will go to waste.

Key takeaways

Here are the three key takeaways on the role of trusted metrics for building trusted AI:

1. Trusted metrics require trusted data: Your AI models and business decisions are only as good as the data they’re built on. Without high-quality, trustworthy data, your metrics—and the decisions they drive—are at risk.

2. AI readiness starts with data trust, not technology: You can’t simply buy your way into AI readiness with the latest tools. You need to start by building a solid foundation of well-governed, clean, and trustworthy data.

3. Data products are only valuable if they’re trusted and usable: A data product like a dashboard, report, or model is only valuable if it’s trusted, well-documented, and understood by the business. Don’t let your data products go unused due to a lack of trust.

Summary

Remember the opening set of dashboards? I asked you to guess which dashboard is more accurate. Well, the fact of the matter is that you cannot tell–most business users never question the metrics. With a Data Intelligence Platform like Alation, you have warnings letting the user know something is amok with the dashboard.

Trusted metrics depend on trusted data.

The future of AI, whether predictive or generative, is dependent on high-quality, trusted data. Alation enables that foundation.

As Chantelle Robertson, Head of Data Governance at Domain Group puts it, “Alation empowers us to find meaning in our data and improve the quality of business decisions. It allows us to unlock the value of our data and turn it into a strategic, competitive asset.”

John Williams, Executive Director of Enterprise Data at RaceTrac echoes this sentiment when he says, “At RaceTrac … data is an enterprise-wide asset. By establishing a strong data governance program that provides a standard approach to processing, retrieving, archiving, and restoring data, the organization can not only make good decisions, but they can now make great decisions with a much higher confidence level.”

Build a data-trust foundation today

Now is the time to audit your organization’s data trust. Start by evaluating the data behind your key business metrics. Can you trust it? How do you know?

Invest in building a data culture that prioritizes governance and collaboration across departments. Ensure your AI initiatives and data products are built on a foundation of trusted data. 

Remember, trusted data leads to trusted metrics—and trusted metrics lead to better business outcomes.

Curious to learn more? Book a demo with us today.