As a marketer-turned-data scientist, I have been wrangling, wrestling with, and taming raw data into streamlined, actionable analytics since the mid-1990s. During that time, I have learned as least as much about human communication as I have about how to build powerful analyses.
If part of your day-to-day responsibilities includes leveraging data-backed analyses to entice others to take action, you will benefit from grasping four principles I have learned over the years that you can use to guide your communications to others.
Data Only Matters When It’s Connected to Real Action
My analytics toolkit has evolved significantly since I started my role as a marketing coordinator in 1997 at Hino Motors in Southern California. I started by doing data entry of truck parts into Microsoft Excel 95 (way back then!) – which I did begrudgingly but I now love (at least in Excel’s more recent incarnations). Over time, I also got proficient at running SQL and Python-backed analyses. Since 2024, I have even started to leverage fancy-schmancy analytics tech like retrieval-augmented generation (RAG) in LLMs. But after tens of thousands of hours working on and with data to build analyses for clients, there is one truth that hasn’t changed a bit all this time:
Data-driven insights are totally worthless unless you can motivate somebody to take action on it
That’s right: serving as an analytics professional necessarily requires healthy doses of both technical skills and communication skills. And when it comes to communicating your findings to clients, colleagues and partners, sometimes leveraging a bit of human psychology is in order.
Four Key Principles for Communicating Data-Backed Insights
In thinking about all of the ways I have fallen short – and then later succeeded – in my data communications journey, I have distilled four key principles that I have found have a direct bearing on how effectively one communicates data-backed insights, namely:
1. The Motivation Principle: People only care about information that helps them reach their goals or helps them avoid their fears
2. The Proof Principle: Folks’ trust level in what you say increases significantly when they can see and hear that the analysis you are sharing is backed by real, accurate, and timely data
3. The Context Principle: Numbers only make sense in the context of a real-life situation, problem domain or opportunity
4. The Ownership Principle: People put more faith into what others communicate when the speaker or sender takes responsibility for – and personally vouches for – what is being communicated
Specific Practices You Can Put into Action Starting Today
Revisiting each of the four data communication principles shared above, here are specific practices you can put into action right away, today.
The Motivation Principle
1. Start your email, report or visualization with a so-what statement, then follow with the data
If you can’t include a “so-what” statement, question whether you should be sending the data at all. The key is to frame your insights in terms of business impact or potential opportunities that matter to the recipient. For instance, instead of saying “Website bounce rate increased by 15%,” lead with “We’re losing potential customers due to website navigation issues – bounce rates are up 15% this month.”
2. Connect the finding to the recipient’s motivations
Always tie your data insights to stakeholders’ specific goals or concerns, at least as you infer them. For marketing managers, this might mean connecting data to ROI or campaign performance. For product teams, focus on user engagement metrics or feature adoption rates. By aligning your insights directly with their priorities, you make the data immediately relevant and actionable.
The Proof Principle
3. Include a screenshot of the raw data source in your email
Sending a screenshot of the raw data source or report along with your analysis validates the accuracy of your message and further illustrates the credibility of the data upon which is was based. Pro tip: Highlight the metric in question with a red circle or arrow. When stakeholders can see the underlying numbers, you build credibility and trust in your analysis. It also allows them to explore details that might be particularly relevant to their needs.
4. Call out the data source(s) you used
Don’t assume the user knows where the data came from. Being transparent about your data sources not only builds trust – it also helps others understand the context and limitations of your analysis. Clearly state whether the data comes from Google Analytics, an ad platform, your CRMs, Pedro in accounting, or other sources.
The Context Principle
5. Always include a date range when sharing report data
You want to leave no unanswered questions around which dates the data is referencing. This is crucial for meaningful interpretation and comparison. Whether you’re looking at weekly trends or year-on-year performance, explicitly stating the time frame prevents confusion and misinterpretation.
6. Add a comparison value or data series for contextual relevance
Numbers sent in isolation rarely tell the complete story. Don’t just share the data in question – share it alongside other data that serves in a comparison role. Often, this means sharing your data relative to target figures or relative to a previous time period (e.g., prior month or prior year, same month). A 5% conversion rate might seem low until you realize this is double what it was last quarter, or it might seem high until you learn that industry standard is 8%.
The Ownership Principle
7. Always include an explanatory comment with the data finding, even if you are simply responding to a request
Don’t just drop numbers on people’s desks or into their inboxes and then figuratively walk away. Instead, show ownership in your analysis by providing your interpretation and/or recommendations. For example: “While the data shows a 22% increase in traffic from mobile devices, I noticed that mobile conversion rates haven’t kept pace. This is concerning to me. I recommend we prioritize optimizing our mobile checkout process to capitalize on this traffic growth.”
8. Always offer to follow up the analysis with more details, further root-cause deep dives or to answer questions
Show your commitment to the insights you’re sharing by making yourself available for further discussion. A simple “I’m happy to schedule time to dive deeper into any of these metrics or explore specific areas of interest” demonstrates ownership and builds trust in your analysis.
Making data insights actionable isn’t just about the numbers – it’s about how you present them. By following these experience-based principles around sharing your data with others, you’ll benefit from increased visibility, effectiveness and trust among those you work with and serve.
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