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Build Trust & Influence With Your Data
- Oct 21
- 9 min read
How to attribute greater trust & transparency in your analyses
You just presented an analysis that took three weeks to produce. The data is solid. The insights are sharp. And this time, something different happens. The room leans in. Questions shift from "how did you calculate this" to "how fast can we move on this." By the end of the meeting, you have budget approval and a request to brief the executive team next week.
What changed? You made your work trustworthy in ways leaders could see and verify.
Good analysis isn't defined by providing all the right answers. The assumptions, sources and any limitations are clearly shared, so that executives focus on the insights and recommendations as opposed to focusing on how the data was compiled. Shifting from reporter to advisor boils down to five practices that make your analysis auditable, understandable, and defensible.
These practices transform how leaders engage with your work and how quickly they will act on your recommendations.
The Truth Behind Trust
Leaders don't act on numbers they can't verify. Even if your analysis is perfect, uncertainty kills action. When executives can't see how you got from raw data to recommendation, they default to skepticism. The goal is simple: build work that someone can audit, challenge, and use without you in the room. That's when you stop being the person who makes reports and become the person who shapes strategy. These five practices create that shift.
Make Everything Traceable
Transparency means showing your work. And for anyone I've worked with and for in my career, the question is always the source of the data, and being able to match or trace it back accurately to its origin. Anyone reading your analysis, and data reports, should be able to trace your numbers back to the source and reproduce your results independently. This speeds up decisions. When leaders can check your logic as fast as they read your chart, debates shift from "can we trust this" to "what should we do about it."
What this looks like in practice:
Keep three documents for every deliverable you create.
First, maintain a detailed source. List the dataset owner, when you pulled the data, how often it refreshes, where it lives, and who has permission to access it. This takes two minutes and saves hours of trust-building later. Not sure what this looks like? I have one available for FREE in the Quark Shop - Quark Source/Citation Template.
Second, create a list of assumptions. Document your filters, your assumption, data filter rules, how you defined each variable, what transformation steps you took, and version numbers for code or spreadsheets. If someone questions your numbers at 9 p.m. on Friday, they can find answers in an easy to find supplement connected to your work.
Third, keep your original data reports. These are untouched versions of your original data sets that you can go back to that match back to the original source you pulled from your data provider or databases. I find in producing analysis that I will create iterations from that original source to pivot, filter, calculate (all noted so others can see & follow my work!), but having a copy of the original data is critical to the credibility of the source, and being able to match it back.
It will feel like extra work until the first time leadership reviews your analysis at midnight and approves your recommendations with clarity on all you took into consideration in your analysis. Then you can breathe a sigh of relief, confident that what you prepared is clear, thorough and understood - but most of all, trusted.
Protect People and Show Fairness
Ethics becomes trust when you make consent, data minimization, privacy protections, and fairness checks visible instead of invisible. This matters because protecting individuals protects decisions and brand reputation. It also aligns your work with professional standards that keep you and your organization out of trouble.
What this looks like in practice:
For each dataset, here are four additional tips in a world where the handling of personally identifiable information (PII) is critical.
Use & Permissions tied to your data. Write down the lawful or contractual reason you can use this data. Keep only the fields necessary to answer the stated question.
Specify any privacy controls. Mask or remove personally identifiable information where feasible. Limit access through role-based permissions and set a retention schedule for when you'll delete the data.
Sanity checks. Show performance results by demographic or customer segment. If one group gets worse outcomes, note what fixes you deployed before releasing the analysis. It may help to get another set of eyes to look a the data - ever hear how two heads are better than one? This might help in spotting something you might not have considered.
Lastly, document all of this in a short ethics note that travels with your deliverable. When stakeholders see you've thought through consent, privacy, and fairness, they trust the entire analysis more.
Stress Test Your Numbers
Validation is the discipline that keeps compelling stories from collapsing under scrutiny. It catches defects and proves robustness before the meeting starts. Leaders need reassurance that your findings survive reasonable challenges and aren't artifacts of bad data or system quirks.
What this looks like in practice:
Run a quality assurance (QA) checklist on every analysis. Check for completeness, run type and range checks, scan for duplicates, and review outliers against documented thresholds.
Reconcile your core totals to a trusted control source. Define acceptable variance based on decision risk. If you're recommending a million-dollar budget shift, your variance tolerance should be tighter than for a routine monthly report.
Perform sensitivity analysis. Show how results move under plausible parameter shifts. If changing one assumption by ten percent flips your recommendation, leaders need to know.
Triangulate with an independent source or method. Verify direction and magnitude through a second lens. If web analytics says traffic dropped twenty percent but sales stayed flat, something needs investigation.
A huge help I use every time is a sanity check of my data reporting. Running your reports and the formulations you used to produce your insights past another set of eyes will give you the peace of mind. Don't wake up in the middle of the night questioning whether that report you shared missed a critical element, or if your recommendation might be off from a decimal point. Having another set of trained eyes from a colleague can help ensure you are on track with what you've done, that your data is accurately represented, and maybe even point out something you had overlooked. Win!!
Keep all these checks in a validation summary that travels with your deck. When someone challenges your numbers, you pull up the summary instead of scrambling for answers.
Communicate in Plain Language
Influence comes from clarity. Lead with the decision, show the evidence, state the implications, and define the next step in words anyone can act on. Executives move faster when they understand the point without translation. Clarity also prevents misinterpretation when people are stressed or rushing between meetings. Don't make the mistake of focusing on demonstrating your knowledge of the data, focus on the business purpose and most critical priorities for your business in the insights. Think, if you were your CEO, what in the data most benefits the vision and direction to move your business forward.
What this looks like in practice:
Build a one-slide headline. State the decision, the impact, the key caveat, and the immediate action in under sixty seconds of talk time. If you can't fit it on one slide, you haven't clarified your thinking yet.
Pick a clear and simple chart to answer the question. Use consistent scales and units across visuals. Label uncertainty when it affects the choice. Fancy visualizations impress other analysts but confuse decision-makers.
Create a shared glossary that maps vendor terms to house definitions. Different platforms call the same thing by different names. Standardize terminology to preserve apples-to-apples comparisons and prevent teams from talking past each other.
Write like you're explaining something to a smart friend over coffee, not presenting at an academic conference. Use short sentences. Define acronyms the first time they appear. Cut jargon. Never make assumptions that your readers know what you're talking about - best to presume they need to be briefed on everything. A short glossary as a supplement to your report may be the valuable reference someone needs.
Frame Every Number With Context
Context is critical, and in analysis it heads off countless questions and values the time of your audience. Numbers without boundaries get misused. Every analysis needs frame and limits: time window, geography, definitions, breaks in data series, market shifts, uncertainty ranges, and conflicts in the evidence. Context prevents misuse, reduces rework, and builds respect for your judgment. It shows the limitations of the data and assumptions supporting the analysis. Tapping into the tactics of rank, trend, and profile that I rail on about relentlessly in my blog are tactics to support better context.
What this looks like in practice:
Include a data context in every deliverable. Was this the best ever performance? Does the data show an efficiency or savings compared to the past? Do you have a competitive advantage? There are a million ways to provide context, understanding what your business could accomplish with the data can provide the clues on how to put your data & insights into context. Don't get called out in a meeting when someone asks, "So, what exactly does this mean?"
Quantify uncertainty where affects the decision. Use simple ranges or bands. State what would change your recommendation. If the confidence interval spans from "minor improvement" to "total transformation," that affects whether to invest millions. Document these thoughts in notes for when sources conflict. Log the disagreement and document your rationale for choosing one source over another. This keeps debates focused on methods instead of personalities. And spoiler alert, occasionally the absence of something may actually be the insight. Touch base with your colleagues to confirm and sanity check to make sure. You'll thank me later for that suggestion.
Be honest about what you don't know. If the data can't answer part of the question, say so. Leaders respect analysts who acknowledge limits more than analysts who overreach.
Putting It All Together: What Does it Look Like?
Imagine you're analyzing a test for a retailer. They ran connected television (TV) creative against paid search ads for new customer orders over eight weeks across three regions. Leadership wants weekly readouts and makes budget decisions every Friday.
Your deck opens with a one-slide headline recommending scaling by region with a frequency cap. The appendix holds your source log, ethics note, validation summary, glossary, and context panel.
When the chief marketing officer asks about data quality at 11 p.m. Thursday, she clicks to your validation summary and sees the reconciliation to order management system totals, the sensitivity test showing results hold under different attribution windows, and the triangulation with brand survey data.
When the chief financial officer questions privacy, he finds your ethics note documenting consent basis, field minimization, and the ninety-day retention schedule.
When the chief executive officer wants to know what could go wrong, she reads your context panel noting the seasonal baseline, the one region with incomplete tracking, and the uncertainty range around incrementality.
Decisions move faster because leaders can audit the logic in one click. You've positioned analytics as a strategy function instead of a reporting function.
Start Small and Build Momentum
You don't need to adopt all five practices tomorrow. Pick one artifact this week and add it to your next readout. Start with the methods appendix if people keep asking how you calculated things. Start with the context panel if decisions get delayed by missing information. Start with the validation summary if stakeholders question data quality.
Try it once and measure the result. Do you get fewer follow-up questions? Do decisions happen faster? Does someone forward your analysis to another team because they trust it?
Share the before-and-after effect with your colleagues. Standardize the winning artifact in your workflow so trust compounds across projects.
Reporting vs. Advising: The Real Difference
In analytics, speed to decision follows trust. Trust follows visible standards that anyone can inspect, challenge, and reuse. Build these habits into your approach, and something shifts. You stop being the person who reports what happened. You become the person who helps leaders decide what to do next. That's when you earn a seat at the table. Not because your analysis is technically perfect, but because your work is auditable, understandable, and defensible. And that's the difference between making reports and shaping outcomes.
What Will You Try First?
Which tactic will you add to your next analysis? The methods appendix? The context panel? The validation summary? Try one and see what changes. Then share what you learned with someone on your team. The fastest way to become a trusted advisor is to make your work trustworthy. Start this week.
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