Significance of Data Assessment

Data doesn't give definitive answers. This reality has become starkly apparent during my years in tech. I've watched skilled engineers and analysts present opposing conclusions using the same datasets. These weren't technical misunderstandings - they reflected a more profound challenge in approaching data-driven decisions. In countless meetings, data transformed from a discovery tool into a shield for existing beliefs. A product manager would highlight engagement metrics supporting feature expansion, while engineering would emphasize the same dataset's performance implications. Both analyses were technically sound. Both missed the larger picture. Something shifted when we started each analysis by examining our assumptions. Instead of asking, 'What does the data say?' we began with, 'Why are we analyzing this specific data in this specific way?' Three insights shaped my perspective: First, strong analyses start by acknowledging what we don't know. Our most productive conversations began with clear statements of our assumptions and limitations. Second, data serves us better as a tool for questioning than answering. Understanding the context and constraints of our analysis matters more than statistical significance. Third, embracing ambiguity leads to better decisions than forcing false certainty. The most impactful outcomes emerged when we combined robust analysis with clear principles and nuanced judgment. I've seen too many organizations chase the illusion of purely data-driven decisions. The reality? Data informs rather than determines. It guides rather than dictates. For those building data-informed teams: How do you handle decisions when your data presents multiple valid interpretations? What practices help you recognize and challenge your own analytical assumptions?"

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 goes beyond numbers, allowing us to predict the future and make new discoveries. However, not all data insights are equally valuable. The challenge is figuring out how to obtain truly meaningful and impactful insights from the data we have. ➟Validity Check. Is your data depicting the true reality? Ensure your methodologies are rigorous and your analyses adhere to objective standards. ➟Reliability Review. Consistency is key. Data should yield the same insights under the same conditions across different times and settings. ➟Relevance Reflection. Does the insight serve your current objectives? Insights must be directly connected to your strategic needs to add value. ➟Timeliness Test. Data's relevance fades over time. Frequently update your insights to reflect the latest data, especially in fast-changing fields. ➟Ethical Examination. From data collection to analysis, ensure transparency, fairness, and privacy protection to maintain the integrity of your insights. ➟Impact Inquiry. Beyond accuracy, insights should empower decision-makers to drive meaningful, positive change that reflects societal and business responsibilities. So, when you uncover insights from data, don't just accept them at face value. Ask probing questions to critically evaluate those insights. This process helps turn raw data and information into actionable strategies and decisions that drive real impact. As you analyze your data, consider what meaningful changes or innovations will your data-inspired choices lead to? #DataLiteracy #BusinessIntelligence #EthicalDataUse #StrategicDecisionMaking #ProfessionalGrowth #InsightfulLeadership #DataLiteracyInPractice #TurningDataIntoWisdom https://lnkd.in/en6rpdsR

Data is at the heart of scientific discovery. So why isn’t data more imbued into scientific decision-making? Whether in the early phases of compound synthesis or later on when working with lead candidates, the opportunity to use data to sharpen thinking and drive decisions is there… Yet many biotechs instead turn to intuition or rely on human memory. “This structure ‘felt’ right”. “I don’t remember why we paused work on this asset”. “Do we have anything better suited to this new profile? -- I don’t know, let me remember…” Teams that rely on intuition over data make worse decisions at every stage. At best, this leads to dangerously expensive delays. At worst, it risks the biotech’s ability to even get a therapeutic to market. The challenge? The sheer volume of data being generated over long periods of time. Efficiently and consistently reviewing relevant data is hard. Keeping mental track of all the information you have as contexts change (months or years down the line) is impossible. Solving this requires the right tools and processes in place – ones that allow for a consistent surfacing of relevant info, and a robust mapping/tracking of this data to the decisions being made. Kaleidoscope makes sure as much data as possible is at your fingertips, and allows you to identify and coordinate the collection of the data that you need to replace intuition. But tools alone are not enough – you need people who believe and processes that support a more rigorous approach. What approaches have you found work well, for driving more informed decision-making? Comment with tricks you use to make sure your team uses data over intuition.

Contrarian take: Information can be valuable even if it's not immediately actionable. Implication: It's worth doing thorough analyses even if it seems to have low immediate ROI. There is no doubt that the value of data is ultimately determined by its impact on the business. However, it is a common misconception to equate business impact with generating "actionable" insights on a daily basis. Setting aside the absurdity of this expectation, in my opinion, the most important role of data is to align the organization clearly against input levers and output metrics. Building this shared mental model is crucial for data-powered conversations, debates and decision-making especially at the business leadership level. A high-caliber leadership team distinguishes itself from others by its deep understanding of the system's operations, the numerical scale of each of its activities, the inherent connections, and its granular details. This culture cannot be developed overnight. It requires a strong appetite for data and relentless analysis, with a focus on deep dives and detailed slicing and dicing of existing data, even if no actionable insights are immediately apparent. And this is totally ok because the power of information compounds. Exploit it.

Perhaps no issue with regard to data-driven decision making is more important than the concept of validity. But what does validity mean? I wanted to share a publicly available editorial I helped coauthor on this topic ( https://lnkd.in/gwZfwbaP) that can provide guidance to both academics and those in industry. A few thoughts: •Validity claims concern the interpretation and use that are attached to data. For example, we interpret the producer price index for the general freight trucking, long-distance, less-than-truckload sector measured by the BLS as representing a broader construct of "LTL Prices in the United States". We can use these data for different purposes (e.g., budgeting for LTL costs in 2024, indexing our own contract prices, etc.). •The process that generates data is a key facet of validity. The trucking ton-mile index I produce performs well on this dimension because all inputs are generated by representative government data sources that themselves are benchmarked. •Data may be valid for one use but not another. Trucking firms' CSA scores can be used by shippers for carrier selection, but it would be very questionable to terminate operating authority solely because CSA scores are high. Implication: anytime you plan on using data to inform decisions, you need to keep the various facets of validity in mind. You should continually push data providers to provide evidence to support the validity claims they make from their data. #supplychain #supplychainmanagement #data #freight #trucking

We shouldn't always take data at face value. This was my takeaway listening to 13 year NHLer Dominic Moore on a recent panel... The theme of the panel was the influence data has had on hockey. Dominic brought up an important point we can all apply to business. Here's what he shared: Early in his career, coaches would get on him for having fewer blocked shots than they perceived he should have, but he viewed this a different way. In his mind, a lack of blocked shots meant he was in a better position to start with. His point being if players were in the right position to start with, they shouldn't need to be blocking shots. This type of thinking is so important with data being shared so vastly and quickly today. I'm as data-driven as they come, but at times we need to take a step back to critically think about the data we're consuming. Often surface-level data intended to help can have underlying outcomes we are missing. Data-driven decision-making is the way, but don't discount your human intuition. #sportsbiz #linkedinsports #dataanalytics

INFORMED DECISIONS are more than being informed of a decision...🙄 Imagine you are a lawyer preparing for a crucial trial. You have spent months gathering evidence, interviewing witnesses, and crafting your arguments. You are confident that you've built a ROCK SOLID case. But on the day of the trial, you discover that most of your data is actually corrupted, inconsistent, or unreliable. Your witnesses are changing their stories. Your arguments are now based on FALSE ASSUMPTIONS. The facts were NEVER really facts. All of your so-called "evidence" is basically WORTHLESS. You have NO WAY of proving your points or refuting your opponent's claims. You are doomed to LOSE the trial and maybe ruin your reputation. How would you feel? This is what it's like to be a leader in an organization that doesn't use verifiable data to support its decisions and actions. Without proven data, you are operating in the dark, relying on guesswork or hearsay. You're vulnerable to errors, biases, and manipulation. You're wasting time, money, and resources on INEFFECTIVE or HARMFUL strategies. You're missing opportunities, losing customers, and damaging your brand. You're losing alignment and trust with your employees. ----> But here's the #PivotToPositivity 👇🏾 Using verifiable data is essential for making sound organizational changes in the workplace. Data is not just a tool; it is a MINDSET. It is a way of thinking, acting, and leading that transforms your organization from good to great. Data is the LIFEBLOOD of any organization: 📈 It provides insight, direction, and feedback. 📈 It helps you identify problems, find solutions, and measure results. 📈 It enables you to innovate, improve, and grow. 📈 It empowers you to communicate, collaborate, and persuade. ----> What are some of the ways you've used to gather data to help you make decisions in the workplace? #Data #Leadership #Change #Feedback -----------------------------------------------------------------⠀ Light up the bell 🔔 on my profile to catch my future workplace culture and leadership content! Follow #PivotToPositivity to make sure you don't miss upcoming conversations!! ⠀

Overwhelmed by statistics? Been there. These are some of the common misconceptions that I had to unravel. 1️⃣ 🚫 Misconception: Statistical significance equals practical significance. - Statistical significance shows if a difference is likely due to chance. - It doesn't imply that the difference is practically meaningful. - Effect size and context are key! 💡 2️⃣ 🚫 Misconception: A small p-value means the finding is important. - A small p-value means the result isn't likely due to chance alone. - It doesn't gauge the magnitude or importance of the effect. - The size and practical implications of the effect should be evaluated with the p-value. 📊 3️⃣ 🚫 Misconception: A lack of statistical significance means there is no effect. - Lack of statistical significance doesn't necessarily mean there is no effect. - It could be due to insufficient sample size or variability in data. - Effect size, confidence intervals, and other measures also matter! 🧪 4️⃣ 🚫 Misconception: Statistical significance is the only criterion for decision-making. - Statistical significance is an important factor, but not the only one. - Practical significance, effect size, costs, risks, and context also matter in decision-making. 💼 5️⃣ 🚫 Misconception: Statistical analysis can turn bad data into good results. - Statistical analysis can't fix poor data. Remember, garbage in, garbage out! - Prioritize data quality, appropriate study design, and valid measurement methods. 🗑️➡️🗑️ 6️⃣ 🚫 Misconception: Statistics are always objective and free from bias. - Statistics can be influenced by various biases. - Awareness and steps to minimize their impact are crucial during research. 🧠