Precision in Data Presentation

The scariest formula in year-end close? =ROUND() Looks harmless. Precise. Comforting even. Until you discover it’s been quietly rewriting your truth. I once traced a $480K variance back to rounding logic. Not fraud. Not formula error. Just 400 tiny ROUND()s shaving off decimals across tabs. Each one said, “Close enough.” Together, they said, “We’re off.” That’s the problem with precision by appearance — ROUND() hides noise by muting it. It makes messy data look perfect, and perfect data look clean. Here’s what I do now: I round at presentation, never in calculation. Every intermediate sheet keeps full decimals. Only the final output — board, deck, dashboard — gets ROUND(). Because rounding early is like editing evidence before the trial. It feels tidy. But it kills your case. The model isn’t supposed to look right. It’s supposed to be right. And sometimes, the truth lives past the decimal.

Most data presentations fail before the presenter finishes their first sentence. The moment a chart appears on screen, decision makers stop listening. For the next ten to fifteen seconds, their attention shifts to decoding the chart on their own. They are trying to answer one question: What is this telling me? If the insight is not immediately clear, you lose valuable time. You also lose control of the conversation. Instead of moving toward a decision, you are forced to explain the chart, justify the data, and walk people through details that should have been obvious. This is why effective data presentation is not about producing charts. It is about designing for speed to insight. How quickly can someone see the point? How quickly can they understand the priority? If the main insight can be seen in two seconds, the meeting stays on track. The discussion stays focused. Decisions are made with confidence because the information is clear. This is the skill that separates analysts who report data from professionals who influence strategy. It is also one of the most overlooked areas in marketing and analytics training, despite being one of the most requested skills by executives.

In data communication, when you’re presenting information in percentages, sometimes it is better to include absolute values as well. Relying solely on percentages can occasionally offer a biased perspective, potentially leading to inaccurate decisions. By including absolute figures, you mitigate the risk of misinterpreting the true implications of data. To understand this better:  Imagine a company reports 30% increase in profits, from $1M in 2014 to $1.3M in 2015. On the surface, while 30% increase sounds like a significant improvement, a deeper look into the profit margin (profit divided by revenue) reveals that the profit margin in 2015 was actually lower than 2014. The visual below highlights this disparity very clearly. You’ll notice how each graph gives a totally different perspective! So, before deciding what information to present, pause and consider if the number accurately reflects the reality of the situation or if it's merely a sensational figure that embellishes the story. Incidentally, I have been in situations where I was deliberately asked to remove absolute figures, to paint a more favourable narrative. Manipulating the narrative by hiding absolute values is unfortunately far too common in businesses. When you succumb to this kind of pressure (typically from higher management), your message is not grounded in integrity and accuracy anymore. I firmly believe in erring on the side of caution. Depending on the broader context of the communication, offer both relative and absolute numbers so audiences can interpret the data correctly and make informed judgments. The point is, if you prioritize a falsely painted narrative over transparency, you'll not just be paving the way for misguided decision-making, you’ll be risking jeopardizing trust and credibility with your audience as well.

When reporting any number or measurement, don’t just look at its absolute value always relate it to a meaningful reference value. The relative comparison reveals the true significance and impact of the number, and shows how far you are from the required target. For example, a -10ps slack may seem small. At 100 MHz, it’s minor. At 4 GHz, it’s serious. Also, choosing the right reference matters. Comparing slack only to the clock period may not indicate enough, but comparing it to something like the average buffer delay can better indicate how difficult it's to fix the violation. This principle applies to any measurement

90% of data presentations fail to drive decisions Most professionals focus purely on data quality. But even perfect data fails without effective translation. Numbers are a foreign language to human brains. We evolved to understand experiences, not statistics. Transform your data presentations: Remove meaningless comparisons like "5 Empire State Buildings" Replace percentages with human scales: - "47% increase in costs" becomes "Every $2 now costs $3" - "14% of employees" becomes "1 in 7 team members" - "20% efficiency gain" becomes "saving 1 full day per week" Connect numbers to business impact: - Link metrics to current priorities - Show immediate implications - Demonstrate practical value My team implemented this framework last quarter: - Proposal approvals tripled - Meeting time decreased 50% - Decision cycles shortened by 4 days Start translating your data into human experiences. Your audience deserves clarity, not just accuracy.

Accuracy isn’t always your friend. It may look simple: ‘Out of 100 predictions, how many did I get right?’ But here’s the trap: if your dataset is 95% one class, even a lazy model can score 95% accuracy by predicting that class every time. Useful? Not at all. As a junior Data Scientist, I used to make the same mistake, looking always at #accuracy alone. Even on interviews I couldn’t explain the difference between #precision and #recall, until I understood what they really mean. Let’s say we’re building a model to detect fraudulent transactions. Out of 1,000 cases, 980 are normal and 20 are frauds. Now suppose your model makes these predictions: • It predicts 990 as ‘normal’. • It predicts 10 as ‘fraud’. ✅ Out of those 10 predicted frauds, 5 are actually fraud (true positives). 😣 The other 5 predicted frauds are actually normal (false positives). 😣 And since there were 20 actual frauds, the model missed 15 of them (false negatives). Here’s how to interpret that: ➡️ Precision: “Of all the frauds I predicted, how many were actually fraud?” High precision = few false alarms (good when false positives are costly). ➡️ Recall (Sensitivity): “Of all the real frauds, how many did I find?” High recall = fewer missed frauds (good when missing one is costly). ➡️ F1-score: Balance between precision & recall. Use when both matter and you need a single measure. ➡️ Accuracy: “How many predictions overall were correct?” Misleading here because the model ignores some important frauds. ➡️ AUC-ROC: Measures how well the model separates positives from negatives across thresholds. It reflects ranking quality rather than classification at a fixed threshold. 💡 Example insight: Your model might have Precision = 0.5 and Recall = 0.25, meaning: • Half of the predicted frauds (5/10) were real (Precision = 50%). • But you caught only 25% of all actual frauds (5/20), which means low recall. So what should you do? • If missing a fraud is worse than a false alarm → aim for higher Recall. • If false alarms hurt user trust → aim for higher Precision. 👉 Next time you see 95% accuracy, ask: “95% of what? What about its precision and recall ?” Let’s get back to #MachineLearning basics 😉 —————— Tired of #AI FOMO? Subscribe to “my2cents” newsletter for 2 key points from AI research papers every weekend 👉 https://lnkd.in/dYrbkyMQ Thanks for reading!

Making Smart Data-Driven Decisions, Faster At Amazon, we pride ourselves on being data-driven while maintaining a bias for action. As leaders, we're accountable for making sound decisions quickly. These dual imperatives—being right and moving fast—create a healthy tension that drives our business forward. Here's a common scenario: You're reviewing two options where A (new feature) shows 93.2432% on a business metric and B (the current feature) shows 92.7835%. The decision seems clear—go with A and move forward quickly, right? Not so fast. You always have to look beyond averages. Digging deeper you can find that these precise-looking numbers come from just 69/74 and 90/97 observations. When properly represented with confidence intervals: - 93.2% ± 8.1% (n=74) - 92.8% ± 6.9% (n=97) The reality? These options perform essentially the same. The apparent difference is statistical noise, not a true business advantage. This matters because false precision leads to: 1. Wasted resources chasing illusory improvements 2. Slowed innovation as teams fixate on insignificant differences 3. Lost credibility when "improvements" fail to materialize at scale To justify reporting 93.2432% (four decimal places), you'd need approximately 100 million observations! For context: - 1 decimal place: ~1,000 samples - 2 decimal places: ~100,000 samples - 3 decimal places: ~10 million samples - 4 decimal places: ~100 million samples In my experience, the highest-performing teams understand data limitations. They dive deep into the numbers, insist on proper statistical rigor, and still maintain a bias for action by: 1. Including sample sizes with every metric 2. Showing confidence intervals alongside point estimates 3. Making decisions appropriate to their certainty level When confidence intervals overlap, effective leaders either: - Declare the options equivalent and move forward - Quickly gather more data if the decision is critical - Look beyond primary metrics for differentiation True data-driven decision making isn't about precision—it's about understanding what your data can actually support while maintaining velocity. How does your organization handle uncertainty in metrics while still moving quickly? What practices have you found most effective?

When your numbers and dates dress well, they impress well. Ever struggled with formatting currency, percentages, decimals, or date/time in T-SQL? This SQL Server Formatting Cheat Sheet is your go-to guide to make your data not only accurate, but presentable whether you're building reports, dashboards, or queries. 📌 Highlights you shouldn’t miss: Standard Numeric Formats → FORMAT(1234.9, 'C') → 💰 Currency → 'N' for number, 'P' for percent, 'X' for hex Custom Numeric Patterns → Add decimal precision, group separators, or scientific notation → "degrees" and other literals now live inside your format strings Date & Time Formats → Short dates, long dates, round-trip timestamps, and RFC 1123 formatting → Even era-based (A.D.) and AM/PM controls Whether you're an analyst, engineer, or BI developer beautifully formatted output makes all the difference when sharing results with clients or stakeholders. Your SQL query is great, but how you format your results? That’s the real first impression #SQLServer #TSQL #DataFormatting #CheatSheet #DateTime #DataAnalytics #DataPresentation #BusinessIntelligence #SQLTips #CleanCode #FormattedData

Top 6 things about daily market data that even institutional traders get wrong. 1️⃣ Official is not always better: An exchange's official daily statistics (like high, low, settlement, open interest, volume) don't necessarily match actual numbers you could derive from their electronic trading sessions. 2️⃣ Why: Many trading venues have semi-discretionary steps involved in computing official statistics. Or they may have a different methodology—do they double count? Include legs of related spreads? Include OTC or block trades? Things that are hard to replicate in a backtest. 3️⃣ Consistency: Most data vendors only give you the official daily volumes and there's no transparency in how these are derived. In most cases if you take the same vendor's tick history and sum up the volume, it doesn't match. You can see how this creates a problem. e.g It's common to build execution algos that use signals that are sampled on some % ADV interval, but ADV could differ wildly if it's based on intraday data from the electronic session vs. if you're just getting it from the official statistics. This is why Databento separately provides official exchange volume and volume derived from tick data. 4️⃣ Data integrity: There's value in computing your own summary statistics from intraday data and comparing them to the official numbers as an integrity check. 5️⃣  Point-in-time: Timestamping precision matters even for daily data. Even statistics that are computed automatically may be published with a large degree of time variation. e.g. Closing or settlement prices aren't guaranteed to be published soon after the close. We've seen settlement prices get published variably between a minute to 30+ minutes after when they are supposed to be published according to platform docs. Nick Macholl at Databento made a useful visualization of how #CME statistics can get published with significant time variation, using nanosecond timestamps included in Databento's daily settlement data (diagram below). 6️⃣ Lookahead effects: If you're backtesting on daily settlement prices, having insufficient timestamping precision on your daily data could create unexpected lookahead effects, e.g. assuming that you'll get the close price immediately at the close. #marketdata #api #apis #futures #algotrading #trading

Use these 3 phrases to improve your data presentations instantly: 1. "The main point is..." Use this before showing any complex visual. It focuses attention on what matters. 2. "This matters because..." Connect every insight directly to outcomes people care about. 3. "What this means for you is..." Make the relevance personal and actionable for your specific audience. I used to believe good data would speak for itself. I'd present findings and wait for others to see their significance. But insights don't speak. People do. These simple phrases create a frame that helps others see what you see. They transform data from interesting information into something clear and actionable. Try adding these phrases to your next presentation and watch engagement transform. What phrases have you found that make your data work more compelling?