Data-Driven Decision Making

A market map with 10,000 companies is impossible to prioritize. These are the 300 to know. I was a VP of Product in sales tech. And I was frustrated with the maps I found. So I've been studying the space and speaking with experts. Here's the players you need to know: — ONE - Core: Revenue Operating System This is your CRM, your system of record - where your sales operation begins. I break this into 3 segments: Enterprise Platforms → Built for large organizations with complex workflows and high-volume deals → Salesforce, Oracle, Microsoft Dynamics 365, SAP Growth-Stage Solutions → Designed for growing businesses that need scalable tools but with flexibility to adapt → HubSpot, Pipedrive, Zoho CRM, SugarCRM Modern CRMs → Startups and fast-scaling companies looking to move fast without rigid systems rely on modern CRMs. → Attio, Affinity, Close.io, Copper, Freshsales. — LAYER TWO - Engagement & Intelligence These tools power outbound outreach, automate sequences, and provide real-time data on prospects: → Outreach, Salesloft, VanillaSoft, Groove Engagement tools ensure your team hits the right prospect at the right time. — LAYER THREE - Revenue Acceleration These platforms shorten deal cycles: → Gong, Salesloft, Chorus.ai, Ebsta With real-time feedback and actionable insights... — LAYER FOUR - Data & Enrichment Your outreach is only as good as the data backing it. These platforms ensure you’re reaching out to right prospects. → ZoomInfo, Apollo.io, Clearbit, Lusha, Hunter io, Cognism — SATELLITE CLUSTERS - Modern GTM Stack These tools enhance parts of the GTM journey. AI-Enhanced Tools → Automate and personalize content creation at scale. → Writer, Grammarly, CopyAI, Jasper Product-Led Motion → Identify sales-ready leads through product engagement. → Pocus, Intercom, Breyta Sales Enablement → Equip sales teams with training, resources, and playbooks to perform at their best. → Seismic, Spekit, Allego Conversational GTM → Convert prospects directly through real-time chat. → Drift (now part of Salesloft) — SATELLITE CLUSTERS- Emerging Categories These are adjacent categories sales teams often still use. Product Analytics → Track user behaviors post-sale for better upsell and retention opportunities. → Amplitude, Mixpanel Customer Success → Ensure long-term customer retention and success beyond the initial sale. → Gainsight, Catalyst, Totango Workspace Integration → Enable seamless collaboration across sales and operations. → Notion, Slack, Airtable, monday.com Revenue Orchestration → Connect workflows across different systems to streamline revenue operations. → NektarAI, Tray.io, Workato, Boomi — This took a lot of time. Reshare ♻️ if you loved this post. What tools would you add?

GOODBYE SKYSCANNER. GOODBYE GOOGLE FLIGHTS. I booked a $1,120 flight for just $142. No miles. No gimmicks. Just 7 sharp Claude prompts that consistently find cheap flights: 1. Find Hidden Flight Deals Prompt: "Search the web right now for the 5 cheapest international flights from [Your City] departing in the next 60 days under $300. Check airline deal pages, Scott's Cheap Flights, Secret Flying, and fare aggregators. Give me actual prices, dates, and booking links." 2. Trigger Price Drops Prompt: "Research current fare tracking tools and build me a step-by-step system to monitor price drops for flights to [Destination]. Search for which free alert services actually work right now, then set up a tracking spreadsheet I can download and use today." 3. Bypass Expensive Dates Prompt: "Search for round-trip fares from [City] to [City] across every date combination in [Month]. Pull real pricing data from the web, then build me a visual calendar heatmap showing the cheapest travel windows. Flag any weekday combos under $[Budget]." 4. Beat the System Prompt: "Deep-dive into travel hacking forums, FlyerTalk, and airline blogs right now. Find 5 concrete, currently-working booking tricks that lower airfare, not generic advice. Cite your sources so I can verify each one." 5. Optimize Airport Choices Prompt: "Search for every airport within 150 miles of [City]. Compare current average fares to [Destination] from each one, pull drive times and transit costs, then build me a comparison spreadsheet ranking them by total trip cost." 6. Leverage Stopovers Prompt: "Search airline stopover programs and hidden-city ticketing options right now. Find 3 international routes with 20+ hour layovers I can turn into bonus trips. Focus on Europe or Asia. Give me real itineraries with current prices and booking links." 7. Build My Travel Bot Prompt: "Write me a working Python script that monitors flight prices to 3 destinations I choose, scrapes fare data on a schedule, and emails me when prices drop below my threshold. Create the file so I can download and deploy it today." The difference? Claude doesn't just brainstorm. It searches the web live, writes real code, builds downloadable files, and hands you actual tools. Stop getting advice. Start getting results.

You can't fix what you won't measure. Most gender equity conversations stop at headcount. "We have X% women on staff." Great. Now what? Because headcount doesn't tell you why she earns less for the same role. Here's what the organisations serious about change are tracking instead: ✅ Who actually gets to participate (and who gets overlooked). It's not enough to open the door. Are women enrolling in leadership programs at the same rate? Are they dropping out halfway through? Representation at the top starts with access at the bottom. ✅Health outcomes by gender.. Not just headcount in the wellness program. Maternal mortality. Access to reproductive health. Rates of gender-based violence. These aren't just societal statistics. They show up in absenteeism, attrition, and performance. They're your problem too. ✅Who's actually learning (and who's being left behind). Literacy gaps. Dropout rates by gender in your training programs. Enrollment in continuing education. ✅Who's in the room when it matters. Voter turnout is a civic metric. Boardroom turnout is yours. What percentage of women are in your decision-making meetings? What percentage are leading them? ✅What people actually believe about gender, including your managers. Attitudes don't announce themselves. They show up in performance reviews, in who gets the benefit of the doubt, in whose ideas get credited. Culture surveys that skip this question are measuring the furniture, not the house. ✅Who's doing the unpaid work (at home and at work). Who takes notes in meetings nobody asked them to take? Who schedules the team lunch? Who goes part-time after a baby and never quite comes back from it? Time use data makes the invisible visible. Most organisations measure what's comfortable. Gender equity data is only uncomfortable until you look at it long enough to do something about it. The question isn't whether these gaps exist in your organisation and programmes. They do. The question is whether you're willing to find out exactly where. ---- Want insights like this directly in your inbox? Sign up for my mailing list. It's FREE! 👉 https://lnkd.in/ec8mqV2M

A Practical Guide to 1.5 C Scenarios for Financial Users I'm incredibly proud of this comprehensive UN Environment Programme report and resource on climate scenarios! It was my final piece of work with United Nations Environment Programme Finance Initiative (UNEP FI) and one that was a major team effort and a multiyear process! We developed it to help financial users to understand the assumptions behind these critical scenarios and how they can be applied in financial decision-making from net-zero target-setting to risk management. It is full of analyses of different scenarios in comparison to each other, explorations of sector decarbonization pathways, and practical applications of scenario data and insights. It covers IPCC, NGFS, and International Energy Agency (IEA) scenarios and brings in data from a variety of sectors in order to show the changes needed to deliver a sustainable future. Have a look through it here: https://lnkd.in/d8G5eSae There really is something in here for everyone. We hope it becomes a valuable desk reference for you and your teams! #climate #netzero #decarbonization #climatescenarios #climatescience #IEA #NGFS #UN #IPCC #climatefinance #climaterisk

Are you solving the right problem? Now that probability and uncertainty is creeping into previously deterministic systems, it's time to talk about errors -- those bad conclusions you're about to jump to. Everyone in data science knows about Type I and Type II errors: 1️⃣ Type I Error = False positive. You thought you found something actionable, but it was noise. 2️⃣ Type II Error = False negative. You missed a real signal and failed to change course. But the one that should really keep you up at night is the Type III Error: ✔️ All the right math, beautiful dashboards, flawless execution… ❌ Solving the wrong problem. 3️⃣ Type III Error = Wrong positive. It's... The boardroom high-five that shouldn’t have happened. The KPI that looks impressive, but delivers no actual value. Organizations love to ask: “What does the data say?” But often they're skipping the more important question: “Are we asking the right question?” The most dangerous AI/ML system isn’t the one that breaks. It’s the one that works perfectly—on a goal that shouldn't exist in the first place. That’s why I keep saying: “Skilled decision-making is a must-have for effective AI and data science.” Decision intelligence is how you elevate the judgment and framing skills required to turn information into better action. And that’s where most organizations are weakest. They hire technical folk before the leaders have done their homework and properly clarified the decisions worth making. And the more your systems scale, the more dangerous this becomes. Want to reduce Type III errors? Here’s what that takes: ✅ Start with the decision/action/vision, not the data. ✅ Define what “better” means before you look for insights. ✅ Think through the alternatives before automating anything. ✅ Bring in decision scientists—don’t expect everyone to be one without training. ✅ Watch out for technically flawless projects that deliver suspiciously little impact. Data-driven decisions aren’t the same as data-decorated decisions. Your turn: Have you ever seen a Type III error in the wild? What helped you catch it? If you found this useful, a repost ♻️ makes my heart happy. And a subscription to my newsletter makes my day. decision.substack.com #DecisionIntelligence #DataScience #Leadership #AI #DecisionMaking *Footnote for my fellow statisticians in the room: We statisticians shudder unless the meaning is exactly right, so here's the more proper set of definitions: Type I Error: Incorrectly rejecting the null hypothesis. Leaving a good default action. Type II Error: Incorrectly failing to reject the null hypothesis. Staying with a bad default action. Type III Error: Correctly rejecting the wrong null hypothesis. Wasting your life. If you read this far and were cheered by that footnote, you're the best kind of nerd -- definitely repost ♻️ keep the good stuff alive. Join my newsletter where sensible leaders go for AI and decision science: decision.substack.com

Today we release the Champions of Change Coalition “2025 Impact Report”. This is the largest voluntary disclosure of progress towards workplace gender equality in the world, sharing data, actions and insights from our 200+ Members, representing organisations across every major sector. It reflects both year-on-year improvement and the long-term impact of a collective leadership strategy that has been sustained and expanded since 2010. This year’s report confirms that when leadership is visible, action-oriented and accountable, progress follows. Members are strengthening the systems, policies and cultural settings that enable equality to take hold. For example: • 46% of ASX 200-listed Member organisations have achieved gender-balanced executive leadership teams, compared with 31% across the broader ASX 200. • Gender balance in overall representation and Board positions has reached our 40:40:20 target. • 97% of Member organisations are mainstreaming flexible work with policy, tools, technology and leadership support, while 78% are taking action to enable flexible work for frontline and operational workers. • 79% of Members have a strategy and policy in place to improve men’s uptake of parental leave. • Continued leadership on domestic and family violence prevention and response, including employee supports, approaches for responding to employees who may be using violence, and initiatives that deliver positive community impact. It’s also inspiring to see how the Coalition continues to evolve. Around 30% of our Members are now women – a powerful reflection of the gender-equal and inclusive leadership we seek to model and embed across every sector. At the same time, national and Coalition data shows progress is slowing in some areas: • Persistent over-representation of men in leadership and pipeline roles continues, reinforced by outdated models of what it takes to succeed as a CEO. • Built-in barriers continue to channel women and men into different types of jobs, driven by entrenched norms, inflexible work arrangements and work/pay systems that disadvantage people with caring responsibilities. • Progress on gender equality remains slow or stalled in key industries, particularly those facing labour shortages. Societal shifts, fatigue and competing priorities can easily erode hard-won gains. This is a moment that demands courage and conviction to ensure equality and inclusion are built into the design and leadership of every organisation. I am grateful to every Member, Implementation Leader, Convenor and partner who continues to invest their influence, time and resources in this shared effort. Progress in this report belongs to you. Together, we are building stronger organisations, fairer and more inclusive workplaces and shaping a more equal and prosperous society for all. Explore the “2025 Impact Report": https://lnkd.in/gaRrzn-q #ChampionsOfChangeCoalition #ImpactReport #GenderEquality

Ukraine has just redefined the meaning of combat effectiveness. The Armed Forces of Ukraine (AFU) have operationally implemented an e‑points system – an innovative mechanism for evaluating drone unit performance that is already reshaping modern warfare in real time. Each drone operator receives points for confirmed hits: 12 points for killing an enemy soldier, 6.4 points for destroying a TOR, Buk, or Pantsir system, 8 points for an S-300 or S-400 system, 40 points for a tank, 50 points for a Grad launcher. These points can be exchanged directly for battlefield assets – new FPV drones, Starlink terminals, ground control stations, FPV cameras, tactical gear – through the Brave1 platform. This is not about “kill scores” – it's a carefully designed, data-driven combat logistics model in which each team directly influences its own operational capabilities. The system requires full mission documentation – DVR footage, FPV recordings, GCS screen captures – eliminating randomness and reinforcing accountability. Tactical priorities have shifted: the enemy's infantry, electronic warfare operators, and artillery observers are now primary targets. The number of precision strikes on enemy personnel in frontline trenches has increased by over 40% in areas covered by the new scoring procedures. Equipment rotation has dropped from days to just hours – efficiency now grants immediate access to reinforcements. Russian forces are responding with improvised countermeasures: deeper trenches, overhead cover, thermal decoys, and “silent positions” with no movement or emissions. Their concern is growing, as Ukraine decentralizes its strike capabilities and shifts decision-making power directly to the operator level. That said, the e‑points system brings critical risks: – heightened pressure on operators – potential for falsifying mission data – resource inequality between units – overreliance on the Brave1 digital infrastructure – tension within traditional command structures Still, this marks the first known case where real-time battlefield footage and hit confirmation are directly converted into logistical decisions. In this war, the operator is not just the trigger – they manage their own arsenal. The era of low-cost, high-precision warfare has begun. The only question is – who will keep up?

Underwriting is about to experience the same disruption payments saw with UPI silent, intelligent, and hyper-personalized. Traditional actuarial models, largely built on age, gender, and medical history, are no longer enough to accurately price risk. The future of underwriting is about 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞, 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐫𝐢𝐬𝐤 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. A McKinsey study estimates that 𝐀𝐈-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐮𝐧𝐝𝐞𝐫𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐜𝐚𝐧 𝐫𝐞𝐝𝐮𝐜𝐞 𝐥𝐨𝐬𝐬 𝐫𝐚𝐭𝐢𝐨𝐬 𝐛𝐲 𝐮𝐩 𝐭𝐨 𝟐𝟎% through more accurate segmentation and predictive modeling. Insurers are already leveraging geolocation, wearable data, and transaction behavior to assess actual lifestyle risk, not just what’s declared on a form. Instead of pricing a policy once at issuance, underwriting will become continuous. Transactional data from IoT, telematics, and payments will enable dynamic risk tiers such as auto premiums recalibrating monthly based on real driving behavior. With explainability frameworks (like XAI), underwriters can ensure AI doesn’t become a black box. This is critical as 𝟖𝟐% 𝐨𝐟 𝐠𝐥𝐨𝐛𝐚𝐥 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐬 𝐞𝐱𝐩𝐞𝐜𝐭 𝐬𝐭𝐫𝐨𝐧𝐠𝐞𝐫 𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐧 𝐢𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞 over the next 3 years The top insurers are building ecosystems. Partnerships with mobility, fintech, and health platforms will give them richer, more reliable signals, transforming underwriting from risk prediction to risk prevention. The underwriting engine will sense, learn, and adapt in real time, turning insurance from reactive protection to proactive resilience. #DigitalIndia #Fintech #AI #technology #Fintech #technology

Some of the most important shifts in healthcare begin as observations on the ground long before they are reflected in global data. The World Economic Forum Women’s Health Investment Outlook 2026 is a timely validation of what many of us have been advocating for: a fundamental rethinking of women’s health. Women today live longer than men yet spend 25% more of their lives in poor health or with disabilities. That is 75 million years of life lost globally. To me, this is not just a statistic. It is a call to action. In my experience, one of the most persistent gaps in healthcare is not capability but prioritisation. For far too long, male biology has been the default in research, clinical trials, and product development. This has systematically limited our understanding of conditions that affect women differently or disproportionately. The consequences are visible: Women receive just 6% of private healthcare investment Nearly 90% of that is concentrated in reproductive, maternal, and cancer care. Conditions like menopause, PCOS, and endometriosis remain underfunded Equally concerning is the data gap: Only ~5% of clinical trials report sex-disaggregated data Women are diagnosed later than men for 700+ diseases, often by several years From where I stand, this is not just a healthcare disparityit is a systemic inefficiency. Because when women’s health is not adequately addressed, the impact extends beyond individuals to families, communities, and economies. What is equally clear to me is the scale of opportunity. Addressing this gap could unlock over $100 billion in new market opportunities by 2030 and add at least $1 trillion annually to the global economy by 2040. But more importantly, it gives us the opportunity to fundamentally improve quality of life. In my view, there are three priorities we must act on: First, we must move from a fragmented view of women’s health to a lifelong, continuum-based approach—from adolescence to healthy ageing. Second, we must invest in data, research, and innovation that are gender-intentional, ensuring that women are no longer an afterthought in clinical science. Third, we must build care models that are preventive, accessible, and personalised, particularly for conditions that have long been overlooked. Encouragingly, initiatives like the World Economic Forum’s Global Alliance for Women’s Health are beginning to create momentum. But meaningful change will come only when we collectively choose to prioritise women’s health not as a niche, but as a central pillar of healthcare transformation. For me, this is not just an area of focusit is a responsibility. And the time to act is now. https://lnkd.in/gqFbaURK #WomensHealth #HealthcareLeadership #HealthEquity #PreventiveCare #FutureOfHealthcare #WomenInHealthcare

Most companies are using AI for efficiency. Some are accelerating value creation. A great case study is how Colgate-Palmolive is driving innovation. Here are specific ways they are embedding GenAI across innovation processes to substantlly improve research and product development. These come from an excellent article in MIT Sloan Management Review by Tom Davenport and Randy Bean (link in comments). 💡 AI-Driven Product Concept Generation Accelerates Ideation By linking one AI system that surfaces consumer needs with another that crafts product concepts, Colgate-Palmolive can swiftly generate creative ideas like novel toothpaste flavors. This AI-augmented workflow produces a broader product funnel and allows rapid iteration, enabling more employees to participate in the innovation process under guided human oversight. 🔍 Retrieval-Augmented Generation Enhances Data Reliability The firm’s use of retrieval-augmented generation (RAG) integrates company-specific research, syndicated data, and real-time trends from sources like Google search data. This approach minimizes the risk of hallucinations and ensures that responses are deeply grounded in verified, internal content—delivering more accurate market analysis and trend detection. 🤖 Digital Consumer Twins Validate and Refine Concepts Moving beyond traditional focus groups, the company has developed “digital consumer twins”—virtual representations of real consumer behavior. These digital twins rapidly test hundreds of AI-generated product ideas. Early evaluations show a high level of agreement between virtual feedback and actual consumer responses. This innovation speeds up early-stage concept validation and reduces reliance on slower, more limited human panels. 🔐 Democratizing AI Through a Secure Internal AI Hub Colgate-Palmolive’s AI Hub provides employees with controlled access to advanced AI tools (including models from OpenAI and Google) behind corporate firewalls. Mandatory training on responsible AI use, including guardrails and prompt engineering best practices, ensures that employees harness these tools safely and effectively. Built-in surveys and KPI tracking further enable the company to measure improvements in creativity, productivity, and overall work quality. 🌐 Bridging Traditional Analytics with Next-Gen AI for Measurable Impact By integrating traditional machine learning with cutting-edge generative AI, Colgate-Palmolive is not only boosting operational efficiencies but also driving strategic growth. This seamless blend supports tasks ranging from market research and innovation to marketing content creation—demonstrating a holistic, value-driven approach to adopting AI that is a model for other organizations.