Business Insights and Analysis

Amazon kept getting complaints – but the executive team didn’t know why, so Jeff Bezos called customer services on speaker in front of everyone… In the late 1990’s, Amazon was growing fast. Every week, Jeff Bezos gathered his leadership team for their most important ritual: the Weekly Business Review. One day, the head of customer service proudly presented a slide: “Average phone wait times: 59 seconds.” Tick - move on to the next item. Jeff paused. A number of Amazon’s customers were not happy. He knew this because he maintained and read a public email account. Customers would email him directly, Jeff would forward on to the appropriate executive with a simple “?” for follow up. But his customer service leader was saying everything was rosy. Something didn’t add up… So right there, in the middle of the meeting, Jeff did something radical: Placing the call on speaker, with the entire executive team watching, he picked up the phone and called Amazon’s customer support line… The room went silent. 60 seconds passed. Then 2 minutes. Then 5 minutes. Still no answer. After 10 minutes of hold music - still nothing. “It was a really long time,” Jeff recalled. “More than 10 minutes.” In a flash, the metric they’d been using to reassure investors and guide operations collapsed. The problem? The data wasn’t wrong – but it was measuring the wrong thing. The metric measured average wait time for answered calls, ignoring the calls that never got picked up. That one moment rewired Amazon’s entire approach to measurement, feedback, and truth. Jeff didn’t just want favorable data, he wanted reality. The result? Amazon rebuilt its customer service from the ground up - and made customer service a core part of its moat. Reflecting on that meeting, Jeff said: “When the data and the anecdotes disagree, the anecdotes are usually right.” 👉 Enjoyed this story? Subscribe for one great real life finance story a week: BizStory.co

Yesterday was one of those market days that reminds you capitalism has range. ➰ Fed cut rates by 25bps but with a caveat: no December guarantee. ➰ NVIDIA crossed $5T ➰ Reports broke that OpenAI is eying a $1T IPO and will file paperwork early next year And all that before the Big Tech earnings bell - the market’s quarterly lie detector for the AI narrative. The surface read: beats all around, clouds still swelling, capex exploding. But lift the hood, and we’ve entered the high-stakes second act of the AI cycle. ▪️ Alphabet: Full-Stack, Full-Send Google hit a historic milestone - its first $100B quarter ($102.35B, +16% YoY). A key signal: GCP's $155B backlog (+70% YoY) shows enterprises locking in multi-year AI commits. Sundar flexed: "We’re now processing 1.3 quadrillion tokens per month" +20x YoY That scale is converting into real revenue: - AI products >200% YoY - $1B+ deals grew 2x in 2 years - Gemini app has >650M MAU, queries 3x QoQ Capex guide rose from $85B to $91-93B but investors deemed the juice worth the squeeze. Proof that capital spending, when paired with efficiency, still earns applause. Stock up 4% today. ▪️Microsoft: The House of Infinite Rent MSFT's $77.7B quarter (+18% YoY) was once again an Azure story. Commercial RPO hit $392B (+51% YoY), bookings +112%, and OpenAI contracted $250B of compute - yet to be recognized. Beneath the numbers was the real headline: “We now expect to be capacity constrained through at least the end of our fiscal year" Microsoft isn’t short on customers - it’s short on compute. They will boost AI capacity by 80% this year and double data-center footprint within 2 years, pushing capital intensity even higher. Cloud gross margins are guiding down (~66%) as spend eats profits. This explains investor caution despite topline beat. Stock down 3% today. ▪️Meta: Building God, Getting Punished for It Meta’s headline was strong - $51.2B revenue (+26%), record engagement, and 3.5B DAUs - but details turned sentiment sour. A $15.9B tax charge cratered GAAP EPS, and they warned of “notably larger” CapEx. Meta is transforming from a social-ad engine into an AI compute and product platform, front-loading billions to prepare for what Zuck calls “personal superintelligence.” Meta’s ad engine remains formidable (Advantage+ ad tools at $60B run-rate, conversions up double digits), but investors balked at rising costs and unclear payoff timing. Stock down 10.2% today. AI spending has officially reached the point where even Wall Street wants a spreadsheet. The first phase was euphoria: “Just say AI and we’ll double your multiple.” Now we’re in the second act - the capital cycle, where investors start counting GPUs and mapping depreciation schedules. All three raised capex - Google to scale, Microsoft to de-bottleneck, Meta to front-load for the rapture. The market rewards growth, not faith. AI enthusiasm isn’t fading, it’s maturing. This is progress: eventually every miracle becomes an accounting line.

Africa has too many small businesses, and too little business."  A few months ago, The Economist said this and it stuck with me.   #Africa is the only continent with no company on the Forbes Global 2000. We have just 60% of the large firms you’d expect, given the size of our economies. Why? Are we less talented? Less ambitious? No. But we do face structural roadblocks. Here’s what we hear all the time:  Lack of access to finance. Lack of access to markets. #Finance Want to start a business? Collateral + 20% interest rates. Manage to grow? You’re lucky if you get paid within 90 days. And yet, Kenyan banks are thriving.  NCBA Group just reported profits of KSh 61.8B (~$460M). So we have money — just not for businesses that create jobs and value? #Markets: -Flights within Africa cost $400–$1000. Cheaper to fly to Dubai or Europe. - It’s easier to ship goods from China to Kenya or Uganda than between our own countries. -54 countries = 54 licenses. One continent, but no real single market. -Even our trade payments go through the US dollar, costing us $5B every year. And yet, we have the tools: AfCFTA – continental free trade SAATM – single African air transport PAPSS – Pan-African payment system But we haven’t activated them at scale. So what now? Global systems are skewed. Capital is more expensive. Risk is exaggerated. But we’re not powerless. Individually, we are weak. But collectively, Africa is strong. Let’s stop waiting for  international governments or donors to save us.  Can we build trust, work as pan-Africa, drop the ego, and prove The Economist wrong again?

This is the most underrated way to use Claude: (and it has nothing to do with writing or coding) It's competitive intelligence. Using data that's free, public, and updated every single week. Here's my extract step by step guide: Step 1. Go to claude .ai. Step 2. Select the new Claude "Opus 4.6." Step 3. Turn on "Extended Thinking." Step 4. Pick a competitor. Go to their careers page. Step 5. Copy every open job listing into one doc. (Title. Team name. Location. Full description) Step 6. Save it as one .txt or .docx file. Step 7. Search the company at EDGAR (sec .gov) Step 8. Download its recent 10-K or 10-Q filing. (Official strategy, risks, and financials - all public.) Step 9. Upload both files to Claude Opus 4.6. Step 10. Paste this exact prompt: "You are a competitive intelligence analyst at a rival company. I've uploaded [Company]'s complete current job listings and their most recent SEC filing. Perform a strategic intelligence analysis: → Cluster these roles by what they suggest is being built. Don't use the team names they've listed. Infer the actual product initiatives from the skills, tools, and responsibilities described. → Identify capabilities or teams that appear entirely new — not mentioned anywhere in the SEC filing. These are unreleased bets. → Find roles where seniority is disproportionately high for a new team. This signals executive-level priority. → Cross-reference the SEC filing's Risk Factors and Strategy sections with hiring patterns. Where are they investing against a stated risk? Where did they flag a risk but have zero hiring to address it? → Predict 3 product launches or strategic moves this company will make in the next 6-12 months. State your confidence level and cite specific job titles and filing sections as evidence. Format this as a 1-page competitive intelligence briefing for a CMO." What you'll find: → Products that don't exist yet but will in 6 months. → Priorities that contradict what the CEO said. → Risks they told the SEC but aren't addressing. This is what consulting firms charge $200K for. It took me 10 minutes. I used the new Claude 'Opus 4.6' for a reason: ✦ It read 60 job listing & a 200-page filing together.  ✦ And connects dots across both. ✦ It is superior in thinking and context retrieval. That's why I didn't use ChatGPT for this.

Mike Maples, Jr is one of the most successful startup investors in history. He's worked with more early-stage startups than almost anyone alive, and with his fund, Floodgate, helped pioneer seed-stage investing as a category. He's been on the Forbes Midas List eight times and has made early bets on transformative companies like Twitter, Lyft, Twitch, and Okta. In his new book (coming out this Tuesday!), Pattern Breakers: Why Some Start-Ups Change the Future, he shares the three common elements he's uncovered that separate startups (and founders) that break through and change the world from those that don’t. This research is rooted in his decades of notes, decks, and founder relationships, and is unlike anything I've seen elsewhere. In our conversation, Mike shares: 🔸 The three elements of breakthrough startup ideas 🔸 The importance of founder disagreeableness 🔸 Why you need to both *think* and *act* differently 🔸 How to avoid the “comparison trap” and “conformity trap” 🔸 How to apply pattern-breaking principles within large companies 🔸 Mike’s one piece of advice for founders 🔸 Much more Listen now 👇 - YouTube: https://lnkd.in/gPjw8RXk - Spotify: https://lnkd.in/gAUbgGxz - Apple: https://lnkd.in/g6t367uY Some key takeaways: 1. Get out of the present: Instead of just thinking about solving current problems, you need to immerse yourself in the future. Look for emerging trends, technologies, or shifts in behavior that suggest where the world is heading. Great innovations often stem from individuals who immerse themselves deeply in a niche or cutting-edge area. 2. Great startup ideas share three elements: a. Inflections: External shifts that create potential for radical change in how people think, feel, and behave. b. Insights: A unique understanding of how to harness these inflections and enable a future that the company believes in. c. Founder-future fit: An alignment between the founders and the future they envision, including their skills, motivations, and network. 3. Three things that successful founders do differently: a. Movements: A movement aligns early believers around a higher purpose, leveraging their emotional commitment rather than just pragmatic benefits. b. Storytelling: Frame your startup’s story as a hero’s journey. Position yourself not as the hero but as the guide (like Obi-Wan Kenobi) who invites customers (the heroes) to embark on a transformative journey toward a better future. Tailor your narrative to resonate with different stakeholders—investors, customers, employees—by emphasizing how they can achieve their aspirations through your vision. c. Disagreeableness: Founders who challenge the status quo often appear disagreeable because they defy conventional norms. They drive change by questioning existing patterns and persuading others to embrace new ways of thinking and acting.

Reporting is NOT delivering insights. Unfortunately, many data & analytics professionals think it is. Reporting dashboards show WHAT's happening and enable basic slicing and dicing, but fail to deliver WHY. Example - "Performance is down 15% WoW" This is just stating the obvious. It's not a real insight. It's not actionable. This leaves many business leaders frustrated. When business stakeholders ask for more dashboards, what they are ultimately trying to achieve is "I need to know what's impacting my key business metrics and what I should do to improve it". Adding 15 more charts/views/slices won't help much to understand what's impacting the key business metrics and which actions should be taken. The key to REAL INSIGHTS that can move the needle? ROOT-CAUSE ANALYSIS to find the WHY (i.e., DIAGNOSTIC analytics) This is the most effective way to drive change with data & analytics. This can make the data & analytics team a TRUSTED ADVISOR and get a seat at the leadership and decision-making table. Insights need to be: 🟢SPEEDY: business stakeholders need quick insights into performance changes to make decisions before it's too late 🟢PROACTIVE: don't wait for business stakeholders to ask. Monitor key metrics and proactively share insights to become that trusted advisor 🟢IMPACT-ORIENTED: focus on the key drivers that drove most of the change and communicate accordingly 🟢EFFECTIVELY COMMUNICATED to drive the right action #data #analytics #impact #diagnosticanalytics

𝗛𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗶𝗻 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗖𝗮𝗻𝗻𝗮𝗯𝗶𝘀. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗕𝘂𝗿𝗻𝗶𝗻𝗴 𝗖𝗮𝘀𝗵 𝗼𝗿 𝗖𝗿𝗲𝗱𝗶𝗯𝗶𝗹𝗶𝘁𝘆 Start with the Patient, Not the Plant Medical cannabis is medicine, not wellness or lifestyle. Your product must serve a real need consistently & safely, backed by data. Understand patient journeys, work with clinics & doctors, & embed yourself in the healthcare system, not outside it. Build GACP First, Then EU GMP or Equivalent Too many try to chase EU GMP without mastering GACP. Good Agricultural & Collection Practices are about how you grow. EU GMP is for post-harvest processing & pharma-grade quality control. Get the basics right, document everything, & then scale. Make Regulation One of Your Strengths If you don’t understand the regulatory landscape, you don’t have a business. Know your country’s cannabis laws, narcotics classifications, export rules, & patient access pathways. Compliance is not a department, it’s part of your product. Never Outsource Your Integrity There will be pressure to cut corners, overpromise, or take shortcuts. Don’t. One contamination, one false claim, one deal with a bad distributor and your business collapses. In cannabis, reputation takes years to build and seconds to lose. Trust the Local Team If you operate in another country, listen to the people on the ground. Local growers, engineers, regulators, and logistics teams know more than a remote HQ ever will. Many failed projects stem from ignoring local intelligence. Control the Supply Chain Medical cannabis isn’t just about growing. It’s about controlling drying, processing, lab testing, packaging, export clearance, & more. Own your chain or verify every part of it. You cannot afford surprises with patient-use products. Avoid Chasing the “Next Big Thing” There’s always a new hype, CBD for pets, infused snacks, luxury creams. These trends rarely survive strict medical regulation. Stick to your core business. Deliver clean, consistent, compliant flower or extract. Then grow. Document Everything This industry runs on traceability. You need clean SOPs, batch logs, validated results, cultivation records, & patient outcomes. If it’s not documented, it didn’t happen. If it’s not auditable, it’s not exportable. Raise the Right Money Work with investors who understand the timelines and risks. You need partners who can handle a 3 to 5-year return horizon and still back compliance over short-term revenue. Misaligned finance will kill your project faster than pests. Know When to Say No Sometimes the smartest move is to walk away. If the laws are too grey, your partners untrustworthy, or the facility isn’t ready, pause. Medical cannabis must be built with discipline and maturity. Forced projects fail. Focused ones succeed. Please ask me how to build or fix your cannabis business if you are unsure, stuck, or scaling. I’ve worked in this space for 9+ years, and I have seen what works and what wrecks good ideas.

Most insurance companies don’t have a product problem. They have a 𝐬𝐢𝐠𝐧𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Trouble shows up early for customers… and late for leadership. McKinsey’s 2025 analysis shows that only a small fraction of insurers capture meaningful value from AI and the reason isn’t model quality. It’s because 𝐝𝐚𝐭𝐚 𝐬𝐢𝐭𝐬 𝐢𝐧 𝐬𝐢𝐥𝐨𝐬 across underwriting, claims, support, and policy servicing. Another study highlights that predictive analytics when actually integrated can reduce loss ratios, speed up claims, and improve risk accuracy. But most insurers never reach that stage because their systems can’t surface early patterns. So what happens? A spike in confusion calls. Customers misusing features. Renewal expectations not matching policy reality. Claim friction rising quietly for weeks. By the time these signals hit dashboards, the damage is already in motion: lower NPS, rising churn, operational load, regulatory exposure. This is why insurance needs an 𝐈𝐂𝐔 - 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐧𝐢𝐭. A team that: 1. Connects disparate data into a single, queryable layer. 2. Builds early-warning models for churn, fraud, sentiment, and claims delay. 3. Flags mismatches between expectation and experience in real time. 4. Routes insights directly into underwriting, ops, and customer teams. When insights arrive early, transformation doesn’t arrive late. And in insurance, 𝐭𝐡𝐞 𝐞𝐚𝐫𝐥𝐢𝐞𝐬𝐭 𝐬𝐢𝐠𝐧𝐚𝐥 𝐢𝐬 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐭𝐨 𝐰𝐢𝐧. #InsuranceIndustry #DataAnalytics #CustomerExperience #PredictiveAnalytics

After 25 years in the #BusinessAnalysis field practicing, consulting, teaching, writing, and basically devoting my career to the betterment of business analysis; I see some common things organizations do that severely compromise success. 📍 Using requirements documents as the "system documentation". ���� Requirements documents, user stories, and information should not become the system documentation. These are very different artifacts and very different purposes. It leads to poor requirements quality as BAs have the wrong focus on tech/system details. Requirements should be agnostic of the system details and implementation. Then requirements are durable and reusable. System documentation is created for many reasons and uses, and needs to be suited to those uses and audience. 📍 Locking the requirements documents and artifacts in a hidden folder no one can access. 💥 Requirements information should be reusable and an asset teams can and should look back on. If it is locked up for "compliance and audit" and no one has access to it, this severely compromises other teams work and pace when looking to identify what previous team worked on, the context, and user impact. 📍 Assigning BAs to a specific application and focusing their training on "how the system works". 💥 Business Analysis is about analyzing the business goals, user goals/actions/scenarios, not the system. Most user actions encompass many applications. Assigning BAs to an application compromises the analysis and focusing on system knowledge negates the goal of a BA overall. They will learn the system(s) naturally as they work to focus on the user's needs and processes. It is better to focus BA training on the analysis skills and techniques agnostic of the system and business process. Good BAs use the analysis techniques to learn what they need to learn quickly about the users, data, and systems. 📍 Assigning BAs to projects based on application knowledge. 💥 The more complex the project is, the more BA skills agnostic of the system knowledge are needed. Knowledge of the system or business operations cannot replace analysis skills and techniques to do god business analysis when complexity and risk are high. A experienced and well trained BA with strong BA skills will outperform a subject matter expert as a BA on any complex piece of work impacting many users. Best is when both can work together! 📍 Not guarding the intent of the BA role and allowing every other role to tell the BAs to produce artifacts that compromise quality, value, and time. 💥 Many BAs experience developers, business teams, PMs, and Testing teams giving them loads of tasks to do that compromise the intent of the BA role. Good BAs know how to create far less artifacts and meetings that serve all these audiences well. Untrained BAs and BA Teams fall victim to creating artifacts for everyone. What are you seeing? Comment below 👇

Markets aren't always rational, particularly in the short term, but market reactions to last week’s earnings announcements from some of the most scrutinized companies on earth — Meta, Google and Microsoft — caught my attention as an important signal. My interpretation is that while all three companies are pouring billions into AI-related capex, Wall Street is increasingly skeptical about whether consumer-facing AI (like Meta’s “personal superintelligence”) can justify the massive capex and deliver sufficient TAM. Meanwhile, Google and Microsoft are being given much more license to invest ahead of revenue and build capacity to meet existing and projected demand for enterprise applications, even if the ROI isn’t yet fully visible. What strikes me is how AI investment is mirroring to some extent the “growth at all costs” playbook — but with capacity spending. Meta's decline suggests to me that investor confidence is wearing thin for consumer-facing AI, while the market seems to be rewarding enterprise software that creates business value with AI. And that seems rational for the longer term given how enterprise software that incorporates AI can transform end-to-end business systems for customers.