How to Use Analytics for Informed Decision Making

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.

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

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

Decision-making is a necessity in almost every aspect of daily life. However, making sound decisions becomes particularly challenging when the stakes are high and numerous complex factors need to be considered. In this blog post, written by The New York Times (NYT) team, they share insights on leveraging the Analytic Hierarchy Process (AHP) to enhance decision-making. At its core, AHP is a decision-making tool that simplifies complex problems by breaking them down into smaller, more manageable components. For instance, the team faced the task of selecting a privacy-friendly canonical ID to represent users. Let's delve into how AHP was applied in this scenario: -- The initial step involves decomposing the decision problem into a hierarchy of more easily comprehensible sub-problems, each of which can be independently analyzed. The team identified criteria impacting the choice of the canonical ID, such as Database Support and Developer User Experience. Each alternative canonical ID choice was assessed based on its performance against these criteria. -- Once the hierarchy is established, decision-makers evaluate its various elements by comparing them pairwise. For instance, the team found a consensus that "Developer UX is moderately more important than database support." AHP translates these evaluations into numerical values, enabling comprehensive processing and comparison across the entire problem domain. -- In the final phase, numerical priorities are computed for each decision alternative, representing their relative ability to achieve the decision goal. This allows for a straightforward assessment of the available courses of action. The team found leveraging AHP proved to be highly successful: the process provided an opportunity to meticulously examine criteria and options, and gain deeper insights into the features and trade-offs of each option. This framework can serve as a valuable toolkit for those facing similar decision-making challenges. #analytics #datascience #algorithm #insight #decisionmaking #ahp – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Spotify: https://lnkd.in/gKgaMvbh https://lnkd.in/gzaZjYi7

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?

Let me share a personal story that changed my perspective on data's role in decision-making. Picture this: I'm on the New York subway platform, staring at the digital display. "Next train: 6 minutes." Useful? A bit. But I've already swiped my card and committed to this train line. All I can do is figure out how to best use the wait time. This is classic Business Intelligence (BI) - information that's useful but not action-oriented. Now, fast forward a few years. The MTA installs displays outside the stations. Seeing a 6-minute wait for the local train, I now have a choice. It's a 4-minute walk to the express station. Stay or go? This is Decision Intelligence (DI) - the power of right place, right time delivery. The same principle applies to our role as CDOs. We often pour resources into creating insights, reports, and metrics, but then neglect that crucial last mile - getting the right information to the right person at the right time. Here's how we can shift from BI to DI in our organizations: 1. Identify Key Decision Points Where in the business cycle are your stakeholders making critical decisions? That's where your data products need to be integrated and ready to use. 2. Focus on Actionable Insights Don't just report what happened. What's relevant to the decision-maker? Is your insight in the "good to know" category or the "option A is vastly better" category? 3. Optimize the Last Mile Think about how you're delivering insights. Are they embedded in the decision-making process or sitting in a separate report? This shift isn't just about technology - it's about positioning data as a profit enabler, not a support function - from data aware to data driven. This is how we move from being seen as a cost centre to becoming a strategic partner directly contributing to the core objectives of the business. *** 2500+ data executives are subscribed to the 'Leading with Data' newsletter. Every Friday morning, I'll email you 1 actionable tip to accelerate the business potential of your data & make it an organisational priority. Would you like to subscribe? Click on ‘View My Blog’ right below my name at the start of this post.

The unprecedented proliferation of data stands as a testament to human ingenuity and technological advancement. Every digital interaction, every transaction, and every online footprint contributes to this ever-growing ocean of data. The value embedded within this data is immense, capable of transforming industries, optimizing operations, and unlocking new avenues for growth. However, the true potential of data lies not just in its accumulation but in our ability to convert it into meaningful information and, subsequently, actionable insights. The challenge, therefore, is not in collecting more data but in understanding and interacting with it effectively. For companies looking to harness this potential, the key lies in asking the right questions. Here are three pieces of advice to guide your journey in leveraging data effectively: 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟏: 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐆𝐨𝐚𝐥-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 • Tactic 1: Define specific, measurable objectives for each data analysis project. For instance, rather than a broad goal like "increase sales," aim for "identify factors that can increase sales in the 18-25 age group by 10% in the next quarter." • Tactic 2: Regularly review and adjust these objectives based on changing business needs and market trends to ensure your data queries remain relevant and targeted. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟐: 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐂𝐫𝐨𝐬𝐬-𝐃𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭𝐚𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 • Tactic 1: Conduct regular interdepartmental meetings where different teams can present their data findings and insights. This practice encourages a holistic view of data and generates multifaceted questions. • Tactic 2: Implement a shared analytics platform where data from various departments can be accessed and analyzed collectively, facilitating a more comprehensive understanding of the business. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟑: 𝐀𝐩𝐩𝐥𝐲 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 • Tactic 1: Utilize machine learning models to analyze current and historical data to predict future trends and behaviors. For example, use customer purchase history to forecast future buying patterns. • Tactic 2: Regularly update and refine your predictive models with new data, and use these models to generate specific, forward-looking questions that can guide business strategy. By adopting these strategies and tactics, companies can move beyond the surface level of data interpretation and dive into deeper, more meaningful analytics. It's about transforming data from a static resource into a dynamic tool for future growth and innovation. ******************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!