Data Literacy in Educational Leadership

You ran the data meeting on Friday. Everyone nodded. Nothing changed on Monday. Here's what really happened. Data was collected. The team discussed the data. But nobody decided 𝙝𝙤𝙬 𝙩𝙤 𝙩𝙚𝙖𝙘𝙝 𝙙𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙩𝙡𝙮. Here's the problem: we've confused 𝘤𝘰𝘭𝘭𝘦𝘤𝘵𝘪𝘯𝘨 data with 𝘶𝘴𝘪𝘯𝘨 it. Data without a clear instructional response isn't a system. It's a filing cabinet. So what does acting on data actually look like? After your next assessment, before your data meeting, ask your team one question: "𝗕𝗮𝘀𝗲𝗱 𝗼𝗻 𝘁𝗵𝗶𝘀 𝗱𝗮𝘁𝗮, 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 ���𝗲 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆 𝗻𝗲𝘅𝘁 𝘁𝗶𝗺𝗲?" Not re-teaching the same lesson. Not moving on and hoping it clicks. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆? Here's a simple three-step protocol to make that question actionable: 𝗦𝘁𝗲𝗽 𝟭: 𝗡𝗮𝗺𝗲 𝘁𝗵𝗲 𝗺𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. Don't stop at "students got question 4 wrong." Ask why. Was it a procedural error? A conceptual gap? A language barrier? The misconception tells you how to respond. The mistake only tells you something went wrong. 𝗦𝘁𝗲𝗽 𝟮: 𝗠𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗼𝘃𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻. If students have a conceptual gap, teachers should use the CRA model (Concrete, Representational, Abstract) as a guide. Start with manipulatives or real-world context, move to visuals, then rebuild the abstract. If it's procedural, slow down the steps and make student thinking as visible as possible. The response has to match the root cause, not just re-cover the content. 𝗦𝘁𝗲𝗽 𝟯: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗮𝗻𝗱 𝗮𝘀𝘀𝗶𝗴𝗻 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗯𝗲𝗳𝗼𝗿𝗲 𝗹𝗲𝗮𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗼𝗼𝗺. Every instructional response needs a name attached to it. Who is trying what, in which class, by when and what does that instruction actually look like? Without ownership, the plan dies in the meeting. 𝗗𝗮𝘁𝗮 𝗺𝗲𝗲𝘁𝗶𝗻𝗴𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗲𝗻𝗱 𝘄𝗶𝘁𝗵 𝗮 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗽𝗹𝗮𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁. ♻️ If this idea resonates, repost to help school leaders and math teams turn data into action, not just conversation. 📧 If you're interested in more practical strategies like this, I'm launching a new newsletter called The 3-1-4, where I share practical strategies for improving math instruction and leadership. The first issue goes out on Pi Day (March 14). Link in the comments. _______________________________ Hi, I'm Dwight Williams. A proud first-gen everything, and I help schools and districts strengthen math instruction through coaching, curriculum support, and data-informed systems that drive student confidence and achievement. 👍🏿 Like | 🔔 Follow | 💬 Comment | 🔁 Repost

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

Leaders, here’s a reality check! A data-driven future isn’t just about systems and strategies—it’s about people. Your success depends on: → Connecting people to your vision → Empowering them with the tools and skills to succeed → Leading with a focus on collaboration and inclusivity Data may drive decisions, but it’s the people that unlock its full potential. As you scale your organization, don’t overlook the human connections that turn data into meaningful impact. When your people grow, your organization thrives.     Want to harness the full potential of data? Want to drive smarter decisions and stronger organizations?   Start by building an inclusive data infrastructure where everyone can:   • Access data • Act on data • Align with data   Here's how:   1. Engage Individuals Show the value of data in decision-making.   2. Educate Teams Teach them how to leverage data to meet their goals.   3. Enable Infrastructure Connect systems, drive governance, foster literacy.   4. Promote Transparency Ensure data is open and accessible.   5. Encourage Collaboration Create a culture where data is shared and used collectively.   6. Support Continuous Learning Offer training and resources to build data skills.   7. Lead by Example Use data-driven insights in your leadership.   With these steps, you can transform your organization. Or enhance the data culture you already have.   It's not just good for your people. It's good for your community, too.   Data matters. Make it count.   P.S. Want to chat about keynotes? DM me “KEYNOTE”

𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝘂𝘀𝗲𝗱 𝘁𝗼 𝗯𝗲 𝘁𝗵𝗲 𝘂𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗯𝗮𝘀𝗲𝗹𝗶𝗻𝗲 𝗦𝗸𝗶𝗹𝗹. 𝗡𝗼𝘄, 𝗗𝗮𝘁𝗮 𝗟𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝘀𝗵𝗮𝗿𝗲𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 𝘀𝗽𝗼𝘁 ! Is Data Literacy the new Writing? The data says yes. ✍️ If you asked leaders 10 years ago what the most critical day-to-day skill was, the answer was almost universally "communication and writing." Fast forward to today, and the landscape has completely transformed. Data is no longer a niche skill for analysts; it is the new baseline language of business. 📈 A massive 88% of leaders now rate basic data literacy as "important" or "very important" for day-to-day tasks. ⚖️ This officially puts data literacy on par with, and even slightly ahead of, our most trusted foundational skills, including writing (86%), project management (83%), and delivering presentations (81%). 🚨 60% of leaders surveyed admit their organizations currently have internal skill gaps when it comes to AI and data. They warn that this lack of literacy directly leads to slower rates of innovation, poor decision-making, and reduced competitiveness, according to a new interesting research published by DataCamp using data from a survey of 517 US and UK business leaders conducted in partnership with YouGov . ☝️ 𝙈𝙮 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡 𝙫𝙞𝙚𝙬: When I look at these new findings, my mind immediately goes beyond the corporate boardroom and straight into our classrooms. For generations, our education system has been built on a core foundation: reading and writing. We spend over a decade teaching children how to craft the perfect essay, structure their arguments, and communicate clearly. But if data is truly the new language of the modern world, our school curriculums are drastically out of date. We can't wait until people enter the workforce to teach them how to read a chart, spot a statistical bias, or interpret a dataset. If data literacy is now exactly as critical as writing for professionals, we must start teaching it to our kids with the exact same urgency. It is time to add Data to the ABCs... 🙏 Thank you DataCamp researchers team for these insightful findings: Jonathan Cornelissen 🔑Are we training our teams for this new reality, or are we still treating data like a niche technical skill? #DataLiteracy #FutureOfWork

🚀 Unlocking the Potential of Data in Education: From Data-Driven to Data-Informed 📊✨ Do you think you're ready to elevate your approach to school improvement? My latest article dives into the often blurred lines between "data-driven" and "data-informed" decision-making and their profound educational implications. 🔍 Key Highlights: Data-Driven vs. Data-Informed: Understand the distinct differences and why it matters. Five-Level Hierarchy: Learn the stages from basic data collection to integrating R&D for innovation. Practical Examples: Real-world scenarios from schools and districts that illustrate each level. 📚 Levels of Transition: Data Collection and Basic Analysis: Reactive decision-making based on primary data. Descriptive Analytics: Identifying trends to inform improvement. Diagnostic Analytics: Understanding the root causes of trends and issues. Predictive Analytics: Forecasting outcomes for proactive planning. Prescriptive Analytics and R&D Integration: Driving innovation through evidence-based strategies. 👩🏫 Transformative Practices: Discover how transitioning to a data-informed approach can revolutionize school improvement, leading to more strategic, proactive, and innovative solutions. Dive into the full article to explore how these transformative practices can set the foundation for continuous educational growth and excellence. #Education #SchoolImprovement #DataDriven #DataInformed #Innovation #R&D #Analytics #EducationalLeadership #ContinuousImprovement

📊 How can we use data science to truly improve schools? For over 50 years, education leaders have been urged to leverage data for decision-making. Yet despite massive investments in dashboards and analytics systems, research shows that the link between data use and actual improvements in student outcomes is often weak. In my new paper, “Data Science in Education Administration, Policy, and Practice”, I argue that education data science should be understood as a third core methodology in education research, alongside quantitative and qualitative traditions. Open Access Preprint: https://lnkd.in/eKYTr3i3 Key insights: 🔹 Beyond dashboards: Data science is more than reporting — it involves machine learning, visualization, and exploratory data analysis to support evidence-based improvement cycles. 🔹 Prediction matters: School leaders need accurate predictions, not just statistical model fit. Accuracy should stand alongside theory in informing decisions. 🔹 Algorithms in education must be Accurate, Accessible, Actionable, and Accountable (the “4As”). 🔹 Capacity building: We need to train educational data scientists who can both analyze data and communicate findings to policymakers, teachers, and communities. In effect, we must train people who can talk to people and talk to machines. 👉 The goal is not to replace theory, but to balance explanation with prediction — and to center human judgment, ethics, and collaboration in the process. 🔑 Key Takeaways for the Field For Practice: Schools and districts should embed data science partnerships — not just dashboards — into leadership and improvement cycles. Joint sensemaking between analysts and leaders is essential. For Research: We must expand beyond model fitting to systematically test prediction accuracy and build open, reproducible workflows that connect theory, and application. For Training: Graduate programs in education leadership and policy need roadmaps for education data science capacity building — equipping future leaders to understand, question, and apply advanced analytics responsibly. A key practice for training from Data Science is the Common Task Framework which focuses on: (a) open large-scale real-world deidentified datasets, (b) a shared culture of shared code for shared research, (c) public and open evaluation of algorithms. I’d love to hear from colleagues! Let me know what you think! Open Access Preprint: https://lnkd.in/eKYTr3i3 #EducationResearch #DataScience #EducationPolicy #SchoolLeadership #LearningAnalytics #EdTech