Developing a Culture of Metrics in Business Strategy

Most businesses drown in metrics. Too many KPIs. Too many dashboards. Too much noise. The result? • Teams lose focus • Leaders chase symptoms, not signals • Time is spent updating charts, not solving problems Here’s the truth: You don’t need more data. You need the right few metrics that actually drive performance. Here’s a simple 5-step approach I use to help teams cut through the clutter: 1. Inventory everything – List all the metrics, who uses them, and why. 2. Map to purpose – If it doesn’t support a decision or priority, kill it. 3. Identify the vital few – Pick 3–5 metrics per function that truly move the needle. 4. Build a tiered system – Align top-level KPIs to functional and front-line measures. 5. Eliminate, consolidate, automate – Make room for insight, not reporting theater. Bonus Tip: Run a quarterly “Metric Clean-Up” session—if a metric doesn’t drive action or decision-making, it’s a candidate for retirement. Leading vs. Lagging Check: Ask yourself: Does this metric help us influence the future (leading)? Or just tell us what already happened (lagging)? If your dashboard is 90% rearview mirror, it’s time for a redesign. More focus = better execution. Want help finding your “critical few”? Let’s talk. #BusinessOperatingSystem #KPIs #ContinuousImprovement #Leadership #LeanThinking #Execution #SimplifyToScale #OperationalExcellence #DataDrivenDecisions #BOS #LeadWithMetrics

One of the most common mistakes that companies make is to focus too narrowly on a small set of metrics while overlooking the broader ecosystem of inputs that drive results. At Amazon, we rejected the conventional wisdom that Executives should focus on just a few high-level metrics. Instead, we spent years developing mechanisms to measure, analyze, and improve thousands of input metrics (actions) based on the impact they have on output metrics (business results). The key lessons from this approach are: 1. Do not limit the number of metrics you monitor – Track a broad set of metrics, add, delete, and edit them over time based on observed results. 2. Review both controllable inputs (leading indicators) and output metrics (results) – They must be reviewed in tandem to understand cause-and-effect relationships. 3. Regularly review, analyze, and adjust metrics – The metrics that Amazon tracks are continuously improved to more accurately represent the speed, quality, and cost of every customer-facing process. 4. Implement new product and process improvements designed to deliver improvements for your input metrics. If you have selected the right inputs, then improvements to your outputs will follow. 5. Control your processes by continuously reviewing all relevant input metrics to ensure they stay within desired tolerances as internal and external factors change over time. Following these steps uses the Six Sigma technique known as DMAIC - Define, Measure, Analyze, Improve, and Control. The typical approach is to focus deeply on metrics like sales and gross margin while spending little or no time measuring or managing elements of the customer experience. At Amazon, this focus is reversed.

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

Reflection on healthy data culture in business: sometimes executives think success comes from controlling the narrative with their metrics. They tightly restrict access, shape what information is shared up the chain, and spend more energy on positioning than improving results. The best data driven organizations work differently: they democratize access to the data (with responsible governance), openly discuss failures and successes, and encourage a culture of experimentation and learning. High performance leadership isn't about secrecy. It's about owning the results and showing a commitment to keep improving. Better, never best. Trust is built through transparency and accountability to the results. In professionals sports, everyone can see the score. Everyone can see the shots you make and the shots you miss. The best players miss all the time. What makes them great is that they learn from the shots they miss, practice, and get back in the game again to take it to the next level. It's the same in business: winners are never afraid to be seen missing a shot. What they fear is complacency. Victory comes to the brave leaders willing to take measured risks, learn from the results, and collaborate with their teams and peers to make those results even better next time. In my experience, the more your culture embraces open discussion on data and metrics, the faster you will create success for your customers. Get uncomfortable, embrace experimentation and learning, and invest in making your data better, more visible, and more discussed if you want to win in the age of AI.