Scenic Group (US)

Vanessa Drago

Business Intelligence Strategist

Data analytics is allowing leaders to move from relying primarily on historical performance and instinct to making more informed, forward-looking decisions. This is especially important in travel, where the customer journey can be long and complex. A traveler may interact with an advertisement, visit the website several times, receive emails, speak with a travel advisor or contact center, and book months later. Looking at any one of those interactions in isolation gives an incomplete picture. The real value comes from connecting CRM, marketing, website, call-center, booking, and operational data to understand the entire journey. When leaders have that visibility, they can answer more meaningful questions: Which audiences are most engaged? Which campaigns are creating qualified demand rather than just generating clicks? Where are customers experiencing friction? Which channels are influencing bookings, even if they are not the final point of conversion? Analytics is no longer simply a way to report what happened. It is becoming a tool for deciding where to invest, where to adjust, and where the greatest opportunities exist.

Making Business Data Actionable Insights

AI has the potential to significantly reduce the time between identifying a change in performance and responding to it. Organizations often have more data than their teams can realistically review manually. AI can help detect patterns, surface anomalies, summarize large volumes of information, and identify relationships that may not be immediately visible. For example, it could help recognize an unexpected change in call quality, customer engagement, booking behavior, or campaign performance before it becomes a larger issue. It can also make analytics more accessible by allowing business users to ask questions in everyday language instead of depending on a specialist for every report. However, I view AI as an accelerator, not a replacement for business judgment. It can identify what is changing, but people still need to understand why it matters, whether the conclusion is reasonable, and what action should follow. The strongest results will come from combining AIs speed with human context and experience.

AI can move quickly, but it can also amplify existing data problems just as quickly. If customer records are duplicated, business definitions are inconsistent, system integrations are incomplete, or historical data has not been preserved correctly, AI may produce an answer that appears confident but is fundamentally unreliable. Organizations need strong data governance before they can expect strong AI outcomes. That includes consistent definitions, clear ownership, reliable customer and campaign identifiers, documented data sources, quality controls, and transparent calculation methods. This becomes particularly important during platform migrations or when information moves across several systems. Seemingly small differences such as how a booking, cancellation, rebooking, address, qualified call, or customer record is defined can materially change the result. Trust also requires privacy, responsible data use, and human oversight. The goal should not be to give AI access to every available field. It should be to provide the appropriate, accurate, and necessary data for a clearly defined purpose.

  The strongest results will come from combining AIs speed with human context and experience.  

Driving Strategic Decision-Making

My biggest lesson is that producing a dashboard is not the same as creating insight. A dashboard is only successful if someone understands what to do differently after seeing it. The most effective analytics begins with the business question, not the report. Teams need to agree on what they are trying to understand, how the metrics are defined, and what decision the analysis is intended to support. I have also learned that data needs a narrative. It should clearly explain what changed, why it may have changed, what the business impact is, and what action should be considered. Ideally, that action also has an owner and a timeframe. Analytics becomes meaningful when it creates a shared understanding and moves a conversation forward.

The Sage Advice

Develop technical skills, but do not underestimate the importance of business knowledge, curiosity, and communication. Learning SQL, visualization platforms, statistics, automation, and AI tools is valuable, but the strongest analysts also understand how the organization operates and how decisions are made. Stay close to the people using the data. Ask how a metric is created, what happens operationally before it reaches a report, and whether it reflects the business reality. Learn to question results respectfully and validate them before presenting conclusions. Professionals should also become comfortable working with AI while maintaining a critical mindset. Use it to explore, automate and accelerate but continue asking whether the answer is accurate, explainable, and useful. Ultimately, the future belongs to professionals who can connect technology, data, and human judgment. The most valuable skill will not simply be producing more information; it will be helping organizations understand what that information means and what they should do next.