Data analysts are skilled at uncovering insights, but these aren’t always actionable. Let me share a story from my experience to illustrate the difference.

I used to our performance marketing team by analyzing SEM ROAS. I checked WoW and YoY trends, breaking down the data by keyword intent (high/mid/low). I also compared the results against MTA to assess SEM’s halo effect on SEO. My deliverable to the marketers was: “SEM shows a strong YoY ROAS increase and gains more credit from MTA, though high-intent keyword performance is slightly down. Let’s increase SEM spend and shift budget from high-intent to mid- and low-intent keywords.” At first glance, this seemed reasonable, but my recommendation was flawed.

First, I overlooked the most recent MMM results, which indicated that SEM was nearly saturated – additional spend would yield minimal incremental returns. Second, high-intent keywords were critical to maintain visibility. Reducing spend risked losing ground to competitors, whose pages would appear when users searched those terms. Given this business context, the correct recommendation to performance marketers should have been: “Let’s maintain the current spend level.”

The difference between insights and actions lies in their foundation. Insights are rooted in statistics and mathematics, while actions incorporate business context, operational constraints, and even political dynamics. Reporting insights is straightforward, even AI can do it, but it doesn’t always help stakeholders make decisions and can sometimes mislead them into poor choices.

As data analysts, we must understand the business and think beyond the numbers. When stakeholders ask, “Li, how did our Super Bowl campaign perform?” they’re not just seeking a summary. They want actionable steps for future campaigns, such as targeting specific audiences, choosing effective platforms, or avoiding pitfalls identified in the Super Bowl campaign. As I’ve learned: juniors report numbers, mid-level analysts report insights, and seniors deliver actions.

Conclusion

Moving from insights to actions requires blending analytical rigor with business acumen. By understanding the broader context, data analysts can provide recommendations that drive meaningful decisions, not just data-driven observations.