Setting the Foundations for AI: Choosing Metrics that Matter

In today’s data-driven world, we’re drowning in numbers. With every system, tool, and platform designed to make our lives easier, we’re being inundated with fresh data streams and it’s becoming increasingly easy to fall into the trap of tracking everything but understanding nothing.

Years ago, we struggled to finance or build the data collection pipelines, but the challenge has evolved. It is not about how to collect data anymore — it’s about knowing which metrics actually matter.

As a data leader, your success will soon hinge on your ability to separate signal from noise. This insightful piece from Clekt’s Head of Engineering, Steve Haines explores how you can focus on the right metrics, avoid common pitfalls, and drive meaningful impact through data.

The Paradox of Too Much Data

Organisations today have access to more data than ever before. But instead of making decisions easier, this abundance often leads to analysis paralysis. The key question is: Are we measuring what truly drives business success, or are we just tracking what’s easy to quantify?

Consider the difference between vanity metrics and actionable metrics. Vanity metrics (for example, page views, app downloads, or social media likes) may look impressive but often lack direct impact on strategic goals unless baked into an overarching strategy. Actionable metrics, on the other hand, tie directly to business outcomes and provide a clear path for decision-making. At the same time, there’s a growing trend for organisations to rush into machine learning (ML) models and Agentic LLM AI model builds on their data, hoping these technologies will uncover hidden insights.

But, the paradox remains. After hours of innovation and development, your new AI model will return even more data, and with no clear parameters as to which metrics matter to your business I’d be willing to bet that your analysis paralysis will only increase.

Instead of crunching every single piece of data you have through, I’d suggest spending a bit more of your time at the start of your innovation process deciding upon the metrics that matter. A focused approach, combined with AI-driven insights will return information that is actually assisting you rather than just adding to the noise.

Defining Metrics That Drive Business Impact

To identify what truly matters, data leaders must step outside of the silo and align metrics with overarching business objectives. Here’s how:

1. Start with Business Goals: Every metric collected should have a clear link to a strategic objective. Are you aiming to increase customer retention? Improve operational efficiency? Drive revenue growth? Define your goals first, then determine the key performance indicators (KPIs) that best measure success.
2. Identify Leading vs. Lagging Indicators: Lagging indicators measure past performance (e.g., revenue, churn rate), while leading indicators predict future trends (e.g., customer engagement, trial-to-subscription conversion). A balanced mix of both helps data leaders make proactive decisions.
3. Ensure Metrics Are Actionable: A good metric should drive decision-making. If tracking a metric doesn’t lead to any meaningful action, it’s likely not worth focusing on.
4. Context Matters: Raw numbers don’t tell the full story. Always analyse data in context—benchmark against industry standards, historical trends, and qualitative insights to get a comprehensive view.

Building a Culture of Data-Driven Decision Making
As highlighted above, the importance of data goes beyond selecting the right metrics. The process should be collaborative and involve business leaders across differing functions to have their say and bring the data to life. Fostering a culture where data informs decision-making is by no means easy, but it is crucial. This requires:

• Clear Communication: Ensure that stakeholders understand why specific metrics matter and how they tie to business objectives. This will help to ensure good data hygiene practices, and maximise usage by obtaining senior level buy-in.
• Data Accessibility: Make sure the right people have access to the right data at the right time. If friction exists then people will find shortcuts, and (most likely) Excel or Google sheets as a work around. You want all your data in one place, so work hard to make it accessible.
• Continuous Improvement: Regularly review and refine your metrics to adapt to changing business needs and market conditions. Data should be relied upon daily by all, and if usage of your BI platform slips away take note and look into reasons why the data points aren’t resonating with users.

Common Pitfalls to Avoid
Even experienced data leaders fall into common traps when selecting metrics. Here are some key mistakes to watch out for:

• Tracking Too Many Metrics: As highlighted at the beginning, ‘more’ does not equal ‘better’. Focus on quality of data points as opposed to quantity.
• Chasing Vanity Metrics: Big numbers might look impressive, but if they don’t translate into business impact, they’re distractions and will undermine your goal to encourage internal adoption.
• Lack of Consistency: Constantly changing what you measure makes it difficult to track progress over time. Tying measures to overarching business objectives will ensure stability and consistency.
• Ignoring Data Quality: Bad data leads to bad decisions. Lay solid foundations for your data by ensuring sources are accurate, reliable, and up to date. This will also help with future data projects.
• Overloading AI with Irrelevant Data: Feeding an LLM or ML model massive amounts of unfocused data won’t lead to better insights—it will just produce noise. Instead, you should be defining what the right data points are, and let AI assist you by analysing the results.

Conclusion: Less is More When It Comes to Metrics

The best data leaders understand that focus is a competitive advantage. By identifying the right metrics—those that directly impact business success—you can cut through the noise, make smarter decisions, and drive real results.

In a world of abundant information, your ability to prioritise and act on the right data is what sets you apart. Instead of trying to get an AI model to process everything, refine your metrics first, then let AI enhance your insights. Focus on what truly matters, and let the rest fade into the background.

Should you like to talk to the Clekt team for advice about your data strategy, reach out via the Contact Us page or buttons below.

Bio

Former Lead Data Engineer and Senior BI Developer for Lloyds Banking Group. Steve holds 10+ years of multi-cloud data engineering and solution architect experience.

About Clekt

Clekt are a Snowflake specialist data consultancy helping enterprises to deliver transformational end-to-end data projects.