Data-Driven Decision Making

She started invoicing her company for data requests. $200 per PowerPoint. $500 per dashboard. What happened next: It began as a joke during her performance review. "You say I'm not strategic enough," she told her manager. "But I spend 60% of my time on executive data requests." "That's part of the job," he replied. That night, she built a simple system. Every data request generated an internal invoice: - Time required - Hourly rate - Opportunity cost - Total "charge" She didn't send them. Just tracked them. Month 1 total: $18,400 Month 2 total: $22,100 Month 3 total: $19,750 During her next one-on-one, she presented the receipts. "I've generated $60,250 in data services this quarter. My actual job contributed $0. Which one should I prioritize?" Her manager went pale. She continued: "If we outsourced this to a data analyst at $50/hour, it would cost the company 75% less. And I could do my actual job." Word spread. Other employees started tracking their "invoices." The numbers were staggering: Engineering: $147,000/month in data services Product: $89,000/month in reporting Design: $34,000/month in presentations Someone built a company-wide dashboard: "Internal Data Services Inc." Running total: $4.2M annually The CFO called an emergency meeting. "This is ridiculous. You don't actually invoice internally." Someone responded: "Why not? Every external agency does. We're just the agency that also tries to do our real jobs." That's when it clicked. They were running two companies: 1. The actual business 2. An internal data agency with no billing department The CFO did what CFOs do. Ran an ROI analysis. Option A: Keep status quo ($4.2M hidden cost) Option B: Hire 3 dedicated analysts ($350K) Option C: Buy proper tools and train execs ($100K) The decision took five minutes. Within 30 days: - Executives learned self-service dashboards - Three analysts hired for complex requests - "Invoice system" retired The woman who started it all? Got promoted to Chief of Staff. First initiative: "Time is Money" visibility program. Now every team tracks the true cost of interruptions. Not to invoice. To inform. Because when you make invisible costs visible, behavior changes instantly. The company motto became: "Would you pay $500 for that PowerPoint? Then don't ask someone else to." Revenue grew 40% the next year. Not from new features. From people actually building them. Try it at your company. Track the invoice you'll never send. Watch how fast things change. Because nothing shifts behavior like a price tag.

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

One of the biggest threats to data-driven leadership isn’t technology-related—it’s overconfidence. That’s why the 🚨 𝐃𝐮𝐧𝐧𝐢𝐧𝐠-𝐊𝐫𝐮𝐠𝐞𝐫 𝐄𝐟𝐟𝐞𝐜𝐭 🚨 is so dangerous: Those with limited knowledge think they know it all, while experts second-guess themselves. William Shakespeare summarized this bias more than 400 years ago when he said, “The fool thinks himself to be wise, while a wise man knows himself to be a fool.” 𝐇𝐨𝐰 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐟𝐚𝐥𝐥 𝐢𝐧𝐭𝐨 𝐭𝐡𝐢𝐬 𝐭𝐫𝐚𝐩 (𝐥𝐢𝐦𝐢𝐭𝐞𝐝 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 + 𝐨𝐯𝐞𝐫𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞) ❌ Trust their gut over data instead of questioning assumptions ❌ Make decisive decisions based on misinterpretations ❌ Dismiss expert advice and oversimplify complex issues ❌ Overestimate the data maturity of their teams ❌ Resist upskilling efforts, assuming they already “get” data 𝐖𝐡𝐲 𝐞𝐱𝐩𝐞𝐫𝐭𝐬 𝐬𝐭𝐮𝐦𝐛𝐥𝐞 (𝐝𝐞𝐞𝐩 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 + 𝐥𝐞𝐬𝐬 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐭) ❌ Undervalue their contributions to informing decisions ❌ Hesitate to challenge flawed interpretations or decisions ❌ Overcomplicate explanations, making insights harder to follow and act on ❌ Assume the data speaks for itself and the right course of action is obvious ❌ Struggle to communicate insights effectively (data storytelling!) You won’t be able to fix this problem with more AI, analytics, or dashboards. To overcome this trap, you need a cultural shift. It starts with humble leaders who know they don't have all the answers and empowered experts who trust their knowledge enough to speak up. Here are some other steps you should consider: ✅ 𝐏𝐫𝐨𝐦𝐨𝐭𝐞 𝐝𝐚𝐭𝐚 𝐥𝐢𝐭𝐞𝐫𝐚𝐜𝐲: Make it a priority for all decision-makers. ✅ 𝐄𝐥𝐞𝐯𝐚𝐭𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐯𝐨𝐢𝐜𝐞𝐬: Give data teams a seat at the table. ✅ 𝐅𝐨𝐬𝐭𝐞𝐫 𝐚 𝐭𝐞𝐬𝐭-𝐚𝐧𝐝-𝐥𝐞𝐚𝐫𝐧 𝐜𝐮𝐥𝐭𝐮𝐫𝐞: Encourage leaders to test assumptions with data. ✅ 𝐂𝐫𝐞𝐚𝐭𝐞 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐥𝐨𝐨𝐩𝐬: Evaluate decisions against real-world outcomes. What else would you add to this list to overcome this trap and help foster healthy data-driven leadership? 🔽 🔽 🔽 🔽 🔽 📬 Craving more of my data storytelling, analytics, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7 📚Check out my new data storytelling masterclass: https://lnkd.in/gy5Mr5ky 🛠️ Need a virtual or onsite data storytelling workshop or speaker? Let's talk. https://lnkd.in/gNpR9g_K

Choosing the right chart is half the battle in 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠. This one visual helped me go from “𝐖𝐡𝐢𝐜𝐡 𝐜𝐡𝐚𝐫𝐭 𝐝𝐨 𝐈 𝐮𝐬𝐞?” → “𝐆𝐨𝐭 𝐢𝐭 𝐢𝐧 10 𝐬𝐞𝐜𝐨𝐧𝐝𝐬.”👇 The right chart makes insights stick. The wrong one? Confusion. 𝐇𝐞𝐫𝐞'𝐬 𝐦𝐲 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭 – which chart to use, when, and why: 𝟏. 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 – Compare values across categories • When: Sales by region, product performance • Why: Our brains process length differences instantly 𝟐. 𝐋𝐢𝐧𝐞 𝐂𝐡𝐚𝐫𝐭 – Show trends over time • When: Revenue growth, user adoption curves • Why: Makes patterns and changes obvious 𝟑. 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭 – Display parts of a whole • When: Market share, budget allocation • Why: Works when you have 5 or fewer segments 𝟒. 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭 – Find relationships between variables • When: Price vs. demand, experience vs. salary • Why: Reveals correlations and outliers 𝟓. 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦 – Show frequency distribution • When: Customer age ranges, response times • Why: Spots normal vs. skewed distributions 𝟔. 𝐑𝐚𝐝𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 – Compare multi-dimensional data • When: Employee skills assessment, product features • Why: Shows strengths and gaps at a glance 𝟕. 𝐌𝐚𝐩 – Visualize geographic data • When: Sales by state, store locations • Why: Location patterns jump out immediately 𝟖. 𝐇𝐞𝐚𝐭𝐦𝐚𝐩 – Highlight intensity patterns • When: Website clicks, correlation matrices • Why: Color gradients reveal hot spots 𝟗. 𝐁𝐮𝐛𝐛𝐥𝐞 𝐂𝐡𝐚𝐫𝐭 – Display three variables • When: Market cap vs. growth vs. profit margin • Why: Adds a third dimension through size 𝟏𝟎. 𝐃𝐨𝐧𝐮𝐭 𝐂𝐡𝐚𝐫𝐭 – Modern take on pie charts • When: KPI progress, category breakdown • Why: Center space for key metrics 𝐏𝐫𝐨 𝐭𝐢𝐩: Match your chart to your audience's decision. Executives need trends? Line chart. Team needs to compare options? Bar chart. The right visualization = clearer insights, faster decisions, stronger impact. ♻️ Save this guide for your next presentation! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 16,000+ readers here → https://lnkd.in/dUfe4Ac6

Cloud computing infrastructure costs represent a significant portion of expenditure for many tech companies, making it crucial to optimize efficiency to enhance the bottom line. This blog, written by the Data Team from HelloFresh, shares their journey toward optimizing their cloud computing services through a data-driven approach. The journey can be broken down into the following steps: -- Problem Identification: The team noticed a significant cost disparity, with one cluster incurring more than five times the expenses compared to the second-largest cost contributor. This discrepancy raised concerns about cost efficiency. -- In-Depth Analysis: The team delved deeper and pinpointed a specific service in Grafana (an operational dashboard) as the primary culprit. This service required frequent refreshes around the clock to support operational needs. Upon closer inspection, it became apparent that most of these queries were relatively small in size. -- Proposed Resolution: Recognizing the need to strike a balance between reducing warehouse size and minimizing the impact on business operations, the team developed a testing package in Python to simulate real-world scenarios to evaluate the business impact of varying warehouse sizes -- Outcome: Ultimately, insights suggested a clear action: downsizing the warehouse from "medium" to "small." This led to a 30% reduction in costs for the outlier warehouse, with minimal disruption to business operations. Quick Takeaway: In today's business landscape, decision-making often involves trade-offs.  By embracing a data-driven approach, organizations can navigate these trade-offs with greater efficiency and efficacy, ultimately fostering improved business outcomes. #analytics #insights #datadriven #decisionmaking #datascience #infrastructure #optimization https://lnkd.in/gubswv8k

Five years ago, Warburg Pincus LLC invested in BetterCloud and urged us to work on a project to narrow our ideal customer profile (ICP). It's the most impactful thing I've ever done to improve conversion rates, shorten sales cycles, increase deal size and ultimately transform the company. A big mistake many CEOs make is believing their product is for everyone. It’s tempting. More potential customers should mean more sales, right? But in reality, chasing too broad a market drains resources, distracts your team, muddles messaging, confuses your product roadmap, and kills go-to-market efficiency. Being laser-focused on your ICP drives alignment across product, messaging, and the go-to-market motion. When the right prospect engages, they’ll feel like you built it just for them. Anyone who has built a product or service knows that the things a small business needs are very different than what a huge enterprise needs. A company is different from a school. An IT buyer is different from a security buyer, a sales buyer is different from a marketing buyer, a director level decision maker is different than a C level decision maker… but we still believe we can sell to different segments and personas as the same time. The process to define and use your ICP is relatively straightforward but does take time. The larger your business, the more data you have, the more resources you have to crunch that data the more time you should spend to do it as scientifically as possible. The high level steps are: 1. Build a Customer Dataset: Gather all your customer data. Current and churned customers, won and lost opportunities. Enrich it with firmographic, business-specific, and buyer demographic data. 2. Engage Your Team: Your best sales and customer success people hold invaluable insights about your most successful (and worst) customers. 3. Analyze & Identify Pockets of Gold: Identify common attributes of high-performing accounts and avoid the traps of poor-fit customers. 4. Communicate the ICP to the entire company with the “why” behind the attributes that make up an ideal customer.  5. Rework your messaging to appeal to your newly defined ICP and narrow your growth initiatives to be focused only on the accounts that matter.  6. Assign the right ICP accounts to your reps and ensure they’re focused on the right buyer personas. 7. Product Development: Reassess your roadmap to align with the needs of your ICP. You should see impact fast. GTM funnel metrics will improve. Conversion rates should rise, with better leads turning into stronger opportunities. You may not get more leads, but their quality will increase. I’ve been discussing this with many Not Another CEO Podcast guests, so don’t just take my word for it. I wrote a deep dive on how to “Narrow Your ICP and Transform your Company”, with real examples from other companies. You can read the full article here https://lnkd.in/e5EN3XSR

I built the data and AI strategies for some of the world’s most successful businesses. One word helped V Squared beat our Big Consulting competitors to land those clients. Can you guess what it is? Actionable. Strategy must clear the lane for execution and empower decisions. It must serve people who get the job done and deliver results. Most strategies, especially data and AI strategies, create bureaucracy and barriers that slow execution. They paralyze the business, waiting for the perfect conditions and easy opportunities to materialize. CEOs don’t want another slide deck and a confident-sounding presentation about “The AI Opportunity.” They want a pragmatic action plan detailing strategy implementation, execution, delivery, and ROI. They need a framework for budgeting based on multiple versions of the AI product roadmap that quantifies returns at different spending levels. They need frameworks to decide which risks to take. Business units don’t want another lecture about AI literacy. They need a transformation roadmap, a structured learning path, and training resources. They need to know who to bring opportunities to, how to make buying decisions, and when to kick off AI initiatives. Most of all, data and AI strategy must address the messy reality of markets, customers, technical debt, resource constraints, imperfect conditions, and business necessity. Technical strategy is only valuable if it informs decision-making and optimizes actions to achieve the business’s goals.

Week-by-week, here’s how I’d turn raw data into results in just 90 days. Weeks 1-2: List the 10-20 decisions that control margin, risk, or churn. Pick one flagship. Name an owner and write a one-page brief that states the question, the KPI, and what “good” looks like. Weeks 3-4: Trace the minimum data path for that decision. No extra dashboards. Just the signals, context, and feedback you need. Ship a simple view that everyone can read in under 30 seconds. Weeks 5-8: Automate the pipe if you can, but definitely run and track the first full loop. Measure time from event to action and the lift you got. Post a short retro so the team learns in public. Weeks 9-12: Expand to two more decisions, wire automated telemetry if possible, and celebrate the win company-wide. Nothing changes culture faster than a closed-loop pilot that moves a number people care about. PS - no AI needed. Just using people in seats and data that already exists. How’s that for a way to start your workday tomorrow!? #datadriven #customerexperience #analytics

Gain a data-driven understanding of your customer through Importance-Performance Maps. In today's competitive business world, differentiating your brand by understanding and delivering what truly matters to your customers is crucial. That’s where Importance-Performance Maps (I-P Maps) come in, providing a powerful visual tool to drive strategic decisions. What exactly is an I-P Map? It's a two-by-two grid that allows you to evaluate how well your brand performs in the areas that are important (as well as *not* important) to consumers. The vertical axis represents the importance of various attributes in consumers' eyes, while the horizontal axis shows your brand's performance in those areas. You can include other brands in your market, too, in order to see how your brand stacks up against the competition along those. When done correctly, every critical attribute of your offering -- whether it's product quality, customer service, or pricing -- is plotted on the I-P Map based on these two dimensions. Why does it matter? I-P Maps reveal your brand's strengths and areas where improvement is needed. Here's a breakdown of the quadrants: - Keep It Up (High Importance, High Performance): These are your strengths—attributes that are both highly important to customers and where your brand performs well. Maintain focus here to keep your competitive edge. - Concentrate Here (High Importance, Low Performance): These are critical areas where your brand is underperforming, despite their high importance to customers. Improving performance here can significantly boost customer satisfaction. - Low Priority (Low Importance, Low Performance): Attributes that are less important and where performance is lower. These areas may not require immediate attention but should be monitored for any shifts in customer priorities. - Possible Overkill (Low Importance, High Performance): Here, your brand may be over-delivering in areas that are not as important to customers. Resources invested here might be better allocated to areas of higher impact. How do I use I-P Maps? Use I-P Maps to make informed decisions backed by data that align with customer expectations. Fix those areas of underperformance that are important to consumers. Stop investing in attributes of your product or service that consumers just don't care about. Prioritize investment in product offerings, elevate aspects of customer service, or reallocate resources to close competitive gaps or strengthen your advantages. Use I-P Maps to make informed choices that improve your business performance in impactful and efficient ways. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.