How to Improve Decision Making With Data Connections

Most professionals get stuck in reporting mode. You know, endless charts, dashboards, and status updates. But real impact happens when you show: Why it happened. What’s next. ...not just what happened last week/month/quarter. Here’s the ladder to level up your data skills: Level 1: Reporting You build dashboards, clean data, make charts. Tools: Excel, Sheets, Power BI. Make no mistake. This is foundational. This is called "Descriptive Analytics," and your leaders must have it. However, think of it like electricity. They'll only appreciate it when it's gone. Level 2: Exploratory Analysis Now you're asking: • What patterns are in the data? • What metrics truly matter? • Where are the outliers? This is where you get to why something happened. Tools: Excel, SQL, Python. Leaders value explanations - especially when things aren't going well. Level 3: Pattern Discovery (Unsupervised ML) You start finding structure in messy data. No labels. Just hidden groupings. Examples: • Customer segments • Product groupings Tools: K-means & DBSCAN. Start delighting leaders with your new insights. Use Python in Excel to get started. Level 4: Predictive Modeling (Supervised ML) Now you’re using data like a crystal ball: • Will a customer cancel? • Will a loan default? • Will a deal close? Tools: Decision trees & Random Forests. Successful predictions provide the "why." It's magical. Use Python in Excel to get started. Level 5: Mindset Are you already good at Excel?  You’re closer than you think. Steps 1 & 2?  You’ve probably got that down. Time to step up into 3 & 4. Remember - it isn't a leap. It's just the next rung on the ladder.

Last week, I posted about data strategies’ tendency to focus on the data itself, overlooking the (data-driven) decisioning process itself. All it not lost. First, it is appropriate that the majority of the focus remains on the supply of high-quality #data relative to the perceived demand for it through the lenses of specific use cases. But there is an opportunity to complement this by addressing the decisioning process itself. 7 initiatives you can consider: 1) Create a structured decision-making framework that integrates data into the strategic decision-making process. This is a reusable framework that can be used to explain in a variety of scenarios how decisions can be made. Intuition is not immediately a bad thing, but the framework raises awareness about its limitations, and the role of data to overcome them. 2) Equip leaders with the skills to interpret and use data effectively in strategic contexts. This can include offering training programs focusing on data literacy, decision-making biases, hypothesis development, and data #analytics techniques tailored for strategic planning. A light version could be an on-demand training. 3) Improve your #MI systems and dashboards to provide real-time, relevant, and easily interpretable data for strategic decision-makers. If data is to play a supporting role to intuition in a number of important scenarios, then at least that data should be available and reliable. 4) Encourage a #dataculture, including in the top executive tier. This is the most important and all-encompassing recommendation, but at the same time the least tactical and tangible. Promote the use of data in strategic discussions, celebrate data-driven successes, and create forums for sharing best practices. 5) Integrate #datascientists within strategic planning teams. Explore options to assign them to work directly with executives on strategic initiatives, providing data analysis, modeling, and interpretation services as part of the decision-making process. 6) Make decisioning a formal pillar of your #datastrategy alongside common existing ones like data architecture, data quality, and metadata management. Develop initiatives and goals focused on improving decision-making processes, including training, tools, and metrics. 7) Conduct strategic data reviews to evaluate how effectively data was used. Avoid being overly critical of the decision-makers; the goal is to refine the process, not question the decisions themselves. Consider what data could have been sought at the time to validate or challenge the decision. Both data and intuition have roles to play in strategic decision-making. No leap in data or #AI will change that. The goal is to balance the two, which requires investment in the decision-making process to complement the existing focus on the data itself. Full POV ➡️ https://lnkd.in/e3F-R6V7

Data is only powerful if people understand and act on it That’s why just pulling numbers isn’t enough. A good report tells a story, answers key business questions, and helps decision-makers take action. To ensure your analysis actually gets used: ✅ Start with the right question – If you don’t understand what stakeholders really need, you’ll spend hours on the wrong metrics. It’s okay to ask clarifying questions. ✅ Make it simple, not just accurate – Clean tables, clear charts, and insights that anyone (not just data people) can understand. ✅ Provide context, not just numbers – A 20% drop in sales is scary… unless you also show seasonality trends and explain why it’s normal. ✅ Anticipate follow-up questions – The best reports answer the next question before it's asked. ✅ Know your audience – A C-suite executive and a product manager don’t need the same level of detail. Tailor accordingly. Your work should make decision-making easier. If stakeholders are confused, they won’t use your report No matter how technically correct it is. The best data professionals don’t just crunch numbers. They translate data into impact. Have you ever spent hours on an analysis only for no one to use it?

A man with a watch knows what time it is. A man with two watches is never sure. ~ Segal’s Law More data doesn’t mean better decisions. In fact, it often leads to paralysis, over-analysis, and slower execution. So ... how do you filter out the signal from the noise? While AI cannot replace your instincts and judgment, nor make a high-stakes leadership call on your behalf, it can be a valuable thought partner in decision-making. Here are AI prompts to challenge your own thinking: CLARIFY THE CONTEXT 💭 What is the core problem we’re solving, and how has it evolved over time? 💭 What data or evidence suggests this is the right priority right now? 💭 What are the second- and third-order consequences of this decision? 💭 What does success look like in 12 months? What about failure? 💭 If we had to explain this decision in one sentence, what would it be? MODEL SCENARIOS 💭 What are the best-case, worst-case, and most likely scenarios if we move forward? 💭 How would this decision play out in different competitive conditions? 💭 What factors would make this decision a game-changer or a massive failure? 💭 What are the opportunity costs of choosing this path over others? 💭 If we succeed beyond expectations, what new risks or constraints will emerge? STRESS TEST ASSUMPTIONS 💭 What assumptions are we making that could be flawed or outdated? 💭 What evidence would immediately prove this decision wrong? 💭 What are the hidden risks or unintended consequences we aren’t considering? 💭 Are we making this decision based on past success, or future relevance? 💭 What is the hidden downside of being right? PRIORITIZE SPEED 💭 What is the ONE critical insight that makes this decision 80% clear right now? 💭 If we had to make this decision within 24 hours, what would we prioritize? 💭 Are we optimizing for certainty, or are we delaying out of fear? 💭 If we delay this decision by 6 months, what are the risks and missed opportunities? 💭 What’s the smallest action we can take to test this decision before fully committing? BUILD FEEDBACK LOOPS 💭 What are the top 3 leading indicators that will signal whether this decision is working? 💭 What biases might cause us to ignore early warning signs of failure? 💭 If this decision needs to be reversed, what’s the fastest and least costly way to do it? 💭 How will we ensure that feedback is acted upon, not just collected? 💭 What questions should we be asking 6 months from now to reassess this decision? #leadership #AI #innovation