Balancing Speed and Accuracy

How I Balance Speed and Quality as a Program Manager at Amazon Speed and quality aren’t opposites—they’re complements. Early in my career, I thought moving fast meant sacrificing quality. Then I noticed how a senior PM delivered projects quickly without compromising on standards by using clear frameworks and decision-making principles. That realization changed my approach entirely. Here’s how I balance speed and quality effectively: 1️⃣ Define ‘Good Enough’ Early I set clear quality thresholds before starting a project—what ‘good enough’ looks like and what we’re willing to trade off to meet deadlines. This clarity prevents scope creep and maintains quality standards. 2️⃣ Build in Quality Gates I establish quality checkpoints at critical milestones, not just at the end of the project. These gates allow us to catch issues early and course-correct without impacting the timeline significantly. 3️⃣ Iterate, Don’t Perfect I focus on delivering MVPs (Minimum Viable Products) and iterating based on feedback rather than aiming for perfection from the start. This approach has cut delivery times by 20% on average while still meeting quality benchmarks. Balancing speed and quality isn’t about choosing one over the other—it’s about finding the right blend. If you’re struggling to balance both, try focusing less on perfection and more on progress. How do you balance speed and quality? #ProjectManagement #SpeedVsQuality #Leadership #Amazon

The interview is for a Gen AI Engineer role at Microsoft. Interviewer: "Your RAG pipeline produces accurate answers, but users complain it’s too slow. What do you do?" You: "That's the classic trade-off between retrieval quality and latency. Most teams over-optimize for accuracy early on, then realize response time kills adoption." Interviewer: "So where do you start optimizing?" You: "First, I look at where the delay actually comes from. Usually, it's one of three things: 1. Vector search latency - embedding model or ANN index not optimized. 2. Document chunking - too many chukns retrieved = more context tokens = higher inference time. 3. LLM context length - longer context means higher cost and slower generation." Interviewer: "And your quick wins?" You: - Switch to dense retrieval caching for repeated queries. - Use re-ranking only for complex questions, not every call. - Compress context with summary embeddings or hierarchical retrieval. - Use function calling or structured context instead of dumping long passages. Interviewer: "So you trade off depth for speed?" You: "Not always. I trade redundant context for useful context. Most teams think 'more retrieval = better grounding'. But in practice, fewer, higher-quality chunks -> same accuracy, 3x faster responses." Interviewer: "How do you know when you've hit the sweet spot?" You: "When latency < 3s and user trust doesn't drop. That’s when the system feels 'instant' and grounded." Interviewer: "So RAG optimization is less about models and more about experience design?" You: "Yep. The best GenAI systems balance precision, performance, and patience. Users forgive slight inaccuracy - they never forgive waiting." #AI #RAG #LLM

I violated data best practices to deliver a $40K ROI. (The client renewed. Here's why.) For 4 years, I've preached data best practices: Build proper data models. Minimize tech debt. Do it right the first time. Then reality hits. A mid-sized healthcare company hires us. They need a manual report automated. Fast. Your offer as a consultant is speed-centric. Their "source of truth" is 400 stored procedures written by a DBA who left 2 years ago. Zero documentation. Spaghetti SQL everywhere. 30+ Power BI reports querying directly off the transactional database. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝘄𝗮𝗻𝘁𝗲𝗱 𝘁𝗼 𝗱𝗼: Build a clean data warehouse from scratch. Proper dimensional modeling. Governed metrics. Best practices. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗶𝗱: Replicated their messy legacy logic in the cloud. Matched their numbers exactly—even the parts I knew were questionable. Automated the manual report in 6 weeks. Delivered the $40K ROI we guaranteed. 𝗪𝗵𝘆? Because many executives don't care about best practices. They care about results. Now. You don't get 3-6 months to "do it right." You get 6 weeks to prove you're worth keeping. 𝗧𝗵𝗲 𝘁𝗿𝘂𝘀𝘁-𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗮𝗿𝗮𝗱𝗼𝘅: If you show up and tell them their legacy logic is wrong, they won't trust you. If you replicate it perfectly first, they do. Once trust is built? Then you can challenge the legacy logic. Then you can propose the proper data model. Then you can start fixing the mess. But not before. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗾𝘂𝗶𝗰𝗸 𝘄𝗶𝗻𝘀 𝘁𝗵𝗮𝘁 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝘁𝗿𝘂𝘀𝘁 Automate one critical report. Match legacy numbers. Show ROI fast. 𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝘁𝗵𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀 "This works, but it creates tech debt. Here's the plan to fix it long-term." 𝗖𝗮𝗿𝘃𝗲 𝗼𝘂𝘁 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗲𝗯𝘂𝗶𝗹𝗱 Once trust is established, allocate hours to build the proper foundation. 𝗞𝗲𝗲𝗽 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 𝘄𝗵𝗶𝗹𝗲 𝘆𝗼𝘂 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 Don't stop showing ROI while you refactor. Balance both. 𝗧𝗟;𝗗𝗥: Best practices are the North Star. But speed to value is survival. Deliver quick wins. Build trust. Then improve the foundation. Perfection kills consulting businesses. Progress builds them. Agree or Disagree? P.S. - Full breakdown of how to balance speed vs. best practices in this week's newsletter. Link in comments. 👇 ♻️ Share this if you've ever had to choose between doing it "right" and doing it "fast." Follow me for real talk on what data consulting actually looks like in the wild.

You’ll never get 100%. Make the call at 80%. You’re not hired to get it right every time. You’re hired to move when no one else wants to. To decide before it’s obvious. To commit before it’s confirmed. That’s the real job. Not managing certainty, but leading through ambiguity, with speed, judgment, and a stomach for risk. 🧠 The myth of “just one more report” CEOs don’t choke on bad data. We choke on too much of it. The trap is seductive: “If we could just get one more forecast, one more model, one more dashboard, then we’ll know.” No, you won’t. Most executive decisions aren’t waiting for new facts. They’re waiting for someone brave enough to make the call. And the longer you wait for perfect information, the more expensive everything becomes: • Opportunity cost balloons • Morale drifts • Competitors move • The team loses belief • Paralysis spreads from the top down Perfect data doesn’t come. Delay does. 🧪 The research: action beats accuracy A Harvard Business Review study of executive decision-making found that leaders who made fast, high-velocity decisions, even with incomplete information, outperformed “perfect planners” by 12% on long-term outcomes. Why? Because movement creates momentum. And most decisions can be corrected. Indecision can’t. McKinsey calls it the “bias for action.” Top teams don’t wait to be certain. They move with 70 to 80% confidence, then course-correct aggressively. 🔍 What I do when the data is blurry I ask two questions: 1. What’s the cost of waiting? Not just in money, in team energy, narrative control, and strategic tempo. 2. Can this decision be reversed? If yes, move fast. If no, move clear. But still move. Speed doesn’t mean recklessness. It means deciding at the speed your context demands, not the speed that keeps you comfortable. 🛠 Leading when others are frozen When I feel the room hesitate, I name it. “We’re not going to get 100% certainty. So, let’s align on what we know, decide, and own the adjustment if we miss.” You’d be surprised how fast smart people move once someone gives them permission to stop overthinking. And if I can’t get the team to 100% agreement, I don’t wait. I tell them the truth: “This is my call. I’ll take the weight for it.” Because that’s what the seat is for. ⚔️ Your decision sets the pace As CEO, your decision velocity becomes company velocity. If you hesitate, they stall. If you second-guess, they get nervous. If you wait for certainty, your competitors take the shot first. 80% confidence is usually all you’ll get. Take it. Make the call. Then lead through the impact. Because companies don’t die from the wrong decision. They die from no decision. #Leadership #CEO #Business #Entrepreneurship #Startups #DecisionMaking #Management #Business #CEOInsights

Speed and Accuracy Don’t Have to Be Opposites. People think they must choose between speed and accuracy, but that’s not true. Let me show you a simple example. The Problem: Imagine a system handling millions of sign-ups. Before adding a new user, you need to check if their email already exists. Querying the database for every email can be slow and costly under high traffic. Here is one solution that improves both Speed and Accuracy: 1. Bloom Filter for Speed A Bloom filter is a space-efficient, probabilistic data structure used to test whether an element might exist in a set. When a new email arrives (e.g., john@example.com), check the filter: • If it says the email doesn’t exist, proceed with confidence. • If it says the email might exist, move to the next step. 2. Database for Accuracy If the email passes the Bloom filter, attempt to insert it into the database. The database’s unique constraint ensures no duplicates are ever stored. 3. Update the Bloom Filter If the database accepts the email, add it to the Bloom filter for future checks. Why It Works: • The Bloom filter provides speed by reducing unnecessary database queries. • The database ensures accuracy through its unique constraint. • Together, they create a system that is both fast and accurate. Great developers don't just talk trade-offs; they combine them to build better systems. P.S. Bloom filters are probabilistic data structures, so you must deal with FALSE POSITIVES.

Speed meets strategy.  Last year, I was part of a project that had a tight deadline and high stakes. The team was skilled, but the challenge was how to move fast without losing focus.  We started with a simple rule: move quickly, but think twice. Every decision had to be fast, yet smart. We broke big tasks into small steps. Each step was clear and planned. No rush without reason.  One day, a problem hit hard. We could have rushed to fix it. Instead, we paused. We asked, what’s the best move now? That pause saved hours later.  The team learned that speed alone can cause mistakes. Strategy alone can slow progress. But together, they create power.  By balancing quick action with careful thought, we finished on time and with quality.  Speed gave us momentum. Strategy kept us on track.  This experience taught me: moving fast is good. Moving fast with a plan is better.  How do you balance speed and strategy in your work?

Auditing fast doesn’t mean sacrificing quality. I get it. As a CAE or Head of Internal Audit, the pressure is real: risks are accelerating and becoming more complex and interconnected. That means delivering more, faster. But speed without quality? That’s a zero‑sum game. So how do you keep audit quality high when operating at pace? Risks aren’t just multiplying; they’re moving faster, with shorter tails and less lead time from identification to impact. At the same time, stakeholders want answers yesterday. Boards want faster insights, and that’s where internal audit can really add value. But if we’re cutting corners, what’s the point of doing the audit at all? The challenge is clear: we’re being asked to do more with the same resources (sometimes less). That means being smarter in how we deliver. Here are a few practical ways to keep pace without losing quality: 🔹Apply the 80/20 principle: Focus on high‑impact areas that truly drive strategy. 🔹Gather enough evidence to support informed opinions: Avoid doing more simply to tick a box. If an audit activity or procedure doesn’t add value or have an ROI, consider if it’s worthwhile doing. 🔹Leverage work already performed by others: Make good friends with your second‑line peers. 🔹Accelerate insights through AI and analytics: Consider how GenAI can handle some back office tasks for you, allowing you more time to do what you do best. 🔹Choose the right audit type for the situation: Is an end‑to‑end process audit really necessary, or could a quick pulse check on key controls achieve the same result? 🔹Rethink reporting: One‑page reports, short emails, even quick videos can get the message across faster. Audit standards don’t require lengthy reports. Our real product is insight, not reports. And when cycle times drag, relevance suffers. Auditing at speed can still deliver strong outcomes if we design for it. The goal isn’t just speed; it’s delivering insight that matters, when it matters, without compromise. 👉 How are you finding ways to balance speed, agility, and quality in your audits?

"The data isn't conclusive yet. Let's wait." I watched a $50M opportunity slip through our fingers because of that sentence. The painful truth? Technical and business leaders often get trapped between two extremes: analysis paralysis or reckless speed. That $50M opportunity? It went to a competitor who had an imperfect solution but was willing to learn and adapt in the market. Sometimes the riskiest decision is waiting for perfect data. After guiding engineering, operations and manufacturing teams through transformation for over 20 years, I've discovered something counterintuitive: the most innovative organizations don't choose between rigor and speed. They embrace the paradox and design systems that deliver both. Beyond the classic frameworks like SWOT or RACI Matrix, what matters most is how we approach decisions, not just which tools we use. Here are four principles that consistently drive results, regardless of your chosen framework: 1️⃣ Classify by consequence, not complexity Map decisions on two axes: reversibility and impact. When a decision can be easily reversed and has modest impact, move quickly. Save the deep analysis for truly consequential choices. 2️⃣ Create "decision boundaries" instead of "decision points" For each initiative, establish clear parameters where teams have autonomy to experiment without additional approvals. This accelerates learning while maintaining control. 3️⃣ Separate learning decisions from scaling decisions Use small, rapid tests to generate evidence before making larger commitments. This approach lets you fail fast and adapt quickly. 4️⃣ Build feedback loops into every decision Make data collection automatic and continuous. The goal isn't perfect information - it's learning faster than your competition. This framework works because it: ☑️ Creates safe spaces for experimentation ☑️ Builds a culture of continuous learning ☑️ Enables incremental improvement over either/or thinking ☑️ Makes decision ownership clear and visible What decision-making approaches have helped your team balance speed with rigor? Share your experience below. --------------- #DecisionMaking #Leadership #Innovation #StrategicThinking

AI & Innovation Thursday: Speed vs Accuracy in Radiology AI In radiology, we often hear two promises of #AI: Speed: faster image reads, quicker workflows, reduced backlogs. Accuracy: sharper detection, more consistent measurements, earlier diagnosis. Both are important. But if you ask me which one matters most, the answer is clear: accuracy always comes first. A fast but unreliable system doesn’t save time, it creates confusion, extra work, and potentially risks patient safety. On the other hand, an accurate system, even if slightly slower, builds trust and creates real value. That said, the magic of AI is when we don’t have to choose: AI triage tools can prioritize urgent cases without sacrificing accuracy. Standardized measurements can save time while improving reproducibility. Automated reporting can accelerate workflows and reduce errors. As Chief Medical Officer at GE HealthCare, I believe the true power of AI is not speed versus accuracy but speed with accuracy. Because in healthcare, time matters, but trust matters more. For my colleagues: In your clinical experience, where has AI (or any new technology) saved you more in time or in accuracy? #Radiology #ArtificialIntelligence #Leadership #GEHealthcare

Speed Without Chaos, Governance Without Friction "Move fast and break things" sounds great... until the thing you break is your entire data program's credibility. Many companies fall into this trap: rushing to deliver data insights without governance or over-governing to the point of stifling agility. The result? Mismanaged expectations, frustrated stakeholders, and initiatives that fail to deliver sustained value. The tension between speed and governance isn’t new, but it often feels paradoxical: Move too fast, and you risk bad data quality, compliance issues, or decisions business teams can’t trust. Govern too tightly, and we slow down delivery, creating bottlenecks that frustrate business teams. So here's how, I think, we can strike the right(ish) balance Embed Governance Early Treat governance as part of the process, not an afterthought. Speed and control can coexist when data quality, lineage, and security are baked in. Leverage Technology Modern tools—like data catalogs, automated checks, and self-service platforms—can accelerate delivery while ensuring governance. (Most Importantly) Culture Matters Governance works best when it’s everyone’s responsibility. We should help teams understand that good governance isn’t a blocker; it’s a confidence booster. Balancing speed and governance isn’t easy, but the organizations that get it right move fast, break nothing (hopefully), and win.