Overcoming Challenges With Real Data

I see it in almost every leader I work with: Brilliant instincts. Game-changing ideas. Unmatched drive. But they're trying to scale their business on gut feel alone. It works... at first. You know your market cold. You read people like a book. Your instincts for opportunity are razor-sharp. Then growth hits. Complexity multiplies. Your team expands. The stakes shoot up. And suddenly... gut feel isn't enough anymore. That's when the questions start haunting you: ➔ "Why are we missing our targets?" ➔ "How did I not catch that problem earlier?" ➔ "Why does everything feel like it's slipping away?" Here's the reality check you need: Instinct is powerful, but data is transformational. And truly great leaders build systems that scale beyond their personal genius. Every leadership team needs 5-15 critical numbers, tracked weekly, that tell the real story of your business at a glance. Running on data means: ✅ Catching issues when they're speed bumps, not roadblocks ✅ Leading with facts, not emotions ✅ Empowering your team to solve problems because everyone sees the same truth ✅ Creating real accountability without the drama Data doesn't restrict your creativity — it amplifies it. It turns your instincts from a solo act into a powerful duet with reality. If you're still running purely on gut, it's time to put some structure behind your genius. Start by building your Scorecard — one critical number at a time: https://lnkd.in/gkbkMY5S Trust your gut. Verify with data. That's how you build a business that truly scales. ➕ Follow me, Mark O'Donnell, for more insights that turn entrepreneurial challenges into opportunities ♻️ Share this post if you know a leader who needs this message ✉️ Get weekly leadership insights in my newsletter: www.markodonnell.me

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.

This week, I want to talk about something that might not be the most exciting or sexy topic—it might even seem plain boring to some of you. Very impactful, yet even in many large and complex organizations with tons of data challenges this foundational data process simply doesn’t exist: the Data Issue Management Process. Why is this so critical? Because #data issues, such as data quality problems, pipeline breakdowns, or process inefficiencies, can have real business consequences. They cause manual rework, compliance risks, and failed analytical initiatives. Without a structured way to identify, analyze, and resolve these issues, organizations waste time duplicating efforts, firefighting, and dealing with costly disruptions. The image I’ve attached outlines my take on a standard end-to-end data issue management process, broken down below: 📝 Logging the Issue – Make it simple and accessible for anyone in the organization to log an issue. If the process is too complicated, people will bypass it, leaving problems unresolved. ⚖️ Assessing the Impact – Understand the severity and business implications of the issue. This helps prioritize what truly matters and builds a case for fixing the problem. 👤 Assigning Ownership – Ensure clear accountability. Ownership doesn’t mean fixing the issue alone—it means driving it toward resolution with the right support and resources. 🕵️♂️ Analyzing the Root Cause – Trace the problem back to its origin. Most issues aren’t caused by systems, but by process gaps, manual errors, or missing controls. 🛠️ Resolving the Issue – Fix the data AND the root cause. This could mean improving data quality controls, updating business processes, or implementing technical fixes. 👀 Tracking and Monitoring – Keep an eye on open issues to ensure they don’t get stuck in limbo. Transparency is key to driving resolution. 🏁 Closing the Issue and Documenting the Resolution – Ensure the fix is verified, documented, and lessons are captured to prevent recurrence. Data issue management might not be flashy, but it can be very impactful. Giving business teams a place to flag issues and actually be heard, transforms endless complaints (because yes, they do love to complain about “the data”) into real solutions. And when organizations step back to identify and fix thematic patterns instead of just one-off issues, the impact can go from incremental to game-changing. For the full article ➡️ https://lnkd.in/eWBaWjbX #DataGovernance #DataManagement #DataQuality #BusinessEfficiency

Ever wonder what's on your CEO's mind — or what should be? And how you on the data side can make a significant impact? Check out "Success, Failure & Numbers" by David Weiden, managing director at Khosla Ventures and early investor in $OKTA, $RNG, and $UPST. I had the privilege of attending the CEO Summit last May where Weiden presented. Since then, I've shared this video with folks at Metaplane, founder friends, and several data folks. David starts with an intriguing premise: most venture-backed startups have a product with high NPS and good margins. Yet, most startups fail. Why? His answer is that founders are generally weak at numbers, quantitative analysis, and financials. And there are ways to overcome that weakness by instrumenting the business to increase odds of success through: 1. 𝗧𝗮𝗿𝗴𝗲𝘁𝗶𝗻𝗴. Instead of relying only on intuition, how can we overlay quantitative analysis to focus on the right markets, channels, and customers? 2. 𝗞𝗻𝗼𝘄𝗶𝗻𝗴 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀. How do we avoid the “WeWork express train” to be as close to the source of truth as possible? 3. 𝗙𝘂𝗻𝗱𝗿𝗮𝗶𝘀𝗶𝗻𝗴. What’s the best way to avoid overfocusing on revenue and, instead, focusing on accelerating into a company that makes sense? Instrumenting the business is not possible without data. Specifically, a rigorous quantitative analysis of the business requires centralizing data from across teams, presenting it in a way that aids the right decisions, then aligning the business behind those decisions. So if you’re a CEO: hire a data team to help. If you’re a data person, try to help along these dimensions. We all want to increase the odds of success, and this talk provides a sketch for how to do that: https://lnkd.in/ddpFVb7Y #data #dataengineering #analytics #startups

Data is only as valuable as your ability to understand it. Let’s say you conduct an Employee Engagement Survey. You ask a simple question: "How can we make this company a better place to work?" Responses come in: ➡️ “Increase my salary.” ➡️ “Better pay would help.” ➡️ “We’re underpaid.” Different wording. Same message. But here’s where most companies struggle: Traditional data tools can’t recognize patterns in unstructured responses. You can’t run an SQL query on free-text feedback. And that’s a problem. Because without structure, insights remain hidden. 💡 Enter Natural Language Processing or NLP. With NLP tools, we can read, categorize, and transform messy, unstructured data into clear, actionable insights. Now, instead of drowning in a sea of random responses, you get: 🔍 52% of employees want higher pay. 🔍 24% need career growth opportunities. 🔍 13% seek more flexibility. Suddenly, you’re not guessing. You’re making data-driven decisions with confidence. This is how AI is reshaping business strategy today. It’s eliminating blind spots. It’s making organizations smarter.  It’s bridging the gap between intuition and intelligence. Companies that fail to leverage AI in data analysis aren’t just missing insights. They’re missing opportunities. Are you making decisions based on assumptions or real data?