Long before artificial intelligence became a major business priority, Joe Leavitt was already focused on a question that sits at the center of every successful data strategy, What are customers actually telling us about their behavior?
Joe did not begin his career building advanced AI systems or designing complex data platforms. His journey started with customer service, where he became curious about how people interacted with websites and what those interactions could reveal. That curiosity led him to Google Analytics and eventually into the world of marketing analytics, tracking systems, automation and data infrastructure.
Over the years, he has seen the data landscape evolve from basic reporting to advanced analytics and now to AI-powered workflows. Through every stage, one lesson has remained consistent: technology only creates value when it helps people make better decisions.
During a recent conversation on The Executive Outlook, Joe Leavitt, Director of Data Analytics, shared his perspective on how businesses can use AI in data analytics to improve decision-making, strengthen data quality and create more efficient ways of working.
Joe’s entry into data came from understanding customer behavior.
While working in customer service, he discovered Google Analytics and realized that digital activity could tell a much deeper story about customers. A website visit was no longer just a visit. It represented intent, interest, and a potential relationship between a customer and a business.
That realization changed the direction of his career.
With the guidance of a mentor, he started exploring how marketing activities could be connected with analytics through tools like Google Ads, Facebook Ads, Google Tag Manager and tracking systems.
He learned how campaigns could be measured, how customer journeys could be understood and how data could help businesses move beyond assumptions.
What started as curiosity about website behavior eventually became a career focused on helping companies turn scattered information into insights they could trust.
For him, analytics is not just about explaining what happened yesterday. Real value comes from using past information to make better decisions about the future.
He shared how marketing analytics helps companies understand where their investments are creating impact. For example, when a business considers moving the budget from one advertising platform to another, the decision should not be based on opinions. Data can reveal which channels are attracting the right customers, which campaigns are improving results and where opportunities exist.
But the analysis does not stop campaign performance.
He explained that businesses also need to understand what happens after customers arrive. Are visitors reaching the right page? Are they finding the information they need? Are they moving toward a purchase? Or is the company attracting the wrong audience or creating confusion through its digital experience?
These questions are where analytics becomes valuable. It transforms marketing data from simple numbers into a story about customer behavior and business opportunities.
One of Joe’s biggest lessons from building dashboards was understanding that more data does not always mean better decisions.
Earlier in his career, he approached dashboard creation by asking stakeholders what information they wanted to see. The result was predictable. Everyone wanted everything. Dashboards became longer, more complicated and harder to use. Instead of helping executives make faster decisions, they became another report people had to study.
Over time, He changed his approach. Instead of simply collecting requirements, he started bringing his own perspective. His belief was simple: data professionals should guide conversations because they understand what information actually matters.
A strong dashboard should help a leader understand the health of their business quickly. If deeper investigation is needed, teams can explore further.
The purpose of analytics is not to display every possible metric. It is to highlight the information that helps people take action.
When discussing AI, he focuses less on replacing people and more on improving the way teams work.
One of his most practical examples is to improve data quality monitoring. In many organizations, a dashboard issue is discovered only after someone notices incorrect information. By that point, trust has already been affected.
He explored how AI could help identify these problems earlier. Instead of waiting for a client or business user to report an issue, AI could monitor data patterns, identify unusual changes and alert the team before the problem creates impact.
For example, if a marketing data field suddenly contains an unexpected value, AI can highlight the issue and help the team understand where to investigate.
This changes the role of data teams. Instead of spending hours searching for problems, they can focus on solving them. For him, this is where AI in data analytics creates real value: not by replacing expertise, but by helping experts work faster and more confidently.
The conversation around AI often focuses on automation and replacement. He sees the future differently. He believes AI will become a powerful assistant for data professionals, but human understanding will remain essential.
The reason is business context. When an executive says, "We need more revenue," that statement does not directly translate into a technical solution. Someone still needs to understand: What is driving revenue today? Which channels are creating value? Where are customers dropping off? Which decisions will create sustainable growth?
AI can analyze information quickly, but people need to ask the right questions and connect insights with business goals.
The future is not human versus AI. It is humans using AI to make better decisions.
One of his strongest messages for business leaders is that AI cannot create reliable outcomes from unreliable data.
Many organizations want to immediately use AI tools with their existing information. However, the quality of that information determines the quality of the results.
He shared examples from marketing data where the same platforms could appear under different names or abbreviations. Google Ads might be written differently across files. Facebook campaigns might follow inconsistent naming patterns.
Without clear definitions and reliable processes, AI spends more time understanding the data than helping the business.
For organizations exploring AI in data analytics, improving data quality should be the first step. Clean data creates confidence. Confidence creates adoption. And adoption creates meaningful business impact.
Perhaps the biggest lesson he shared was about how organizations should approach AI implementation.
Many companies try to begin with large transformation projects. They purchase AI tools and attempt to introduce them across multiple departments immediately. He believes this approach creates unnecessary complexity.
His own experience followed a different path.
Initially, he imagined building a system that could automatically manage large parts of the data correction process.
Then he realized the idea was too broad.
So he started smaller. First, AI only identified errors. Then it suggested possible solutions. Later, it helped recommend code changes that the team could review.
Each step built more confidence.
For him, this is the right way to approach AI. Find one repetitive problem. Solve it well. Build trust. Then expand.
Small successful experiences create the foundation for larger transformation.
His perspective highlights an important reality about the future of analytics.
The biggest advantage will not come from companies that simply adopt the newest technology. It will come from companies that understand their problems, improve their data foundations and use technology with purpose.
AI in data analytics has the potential to help organizations identify problems faster, automate repetitive tasks and make more informed decisions.
But technology alone is not the solution.
The real transformation happens when reliable data, human expertise, and intelligent tools work together.
As Joe’s journey shows, better decisions do not come from having more dashboards or more technology. They come from asking better questions and using data to find meaningful answers.
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