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Some say that schools don’t change. Many things may have remained the same but one thing is new: data. Today the walls of principals’ offices display performance results and data walls in teachers’ lounges highlight whether students have accomplished their learning targets. Data has become hot currency in school reforms.

For some, making performance data visible promotes accountability and evidence-based practice. For others, it doesn’t spark enough insight about the complex nature of teaching and learning. It is true that data in schools are often based on simple statistics and analytics, rather than understanding of human relationships and children’s emotions that drive learning in schools.

International student assessments, national education statistics, and various surveys have indeed become important tools in educational leadership. This is often called big data, signifying information sets that are so large and complex that processing them by conventional data processing applications isn’t possible. Today, big data in education covers a range of indicators about teaching and learning processes, and increasingly reports on student achievement trends over the long term.

Big data have already shaped many areas of conventionally human-dominated work where data and smart machines have taken over jobs of man. Some foresee similar developments in the education horizon. Schools will diminish but education will flourish when big data and smart machines will take the roles of teachers. Online programs in many universities have already done that when algorithms calculate the best approaches for students to make the grade.

Sure enough, big data offers more versatile information about teaching and learning in schools. Despite all this new information and the benefits, however, there are clear handicaps in how big data has been used in education reforms. In fact, policymakers often forget that big data can only reveal correlations between variables in education, not causality. Correlation is a valuable part of evidence in education leadership, but it must be proved to be real and then all possible causative relationships must be carefully explored.

Big data alone won’t be able to fix education systems. This is where information about human behaviors and personal experiences become important. Martin Lindstrom calls that small data: tiny clues that reveal big trends. In education, these small clues are often hidden in the invisible fabric of teaching and learning. Discovering this hidden fabric should therefore be a priority for teachers and principals improving schools.

Small data emerges from the notion that in the world of big data we also need different information about learning in school. In education, the notion of small data is nothing new. Good schooling has always been based on teachers’ and students’ punctual and purposeful observations, formative assessments, and reflections of what is happening during teaching and learning processes in schools.

Now, at the dawn of a new education data science, in which smart machines can support the analysis of organized complexities in education, it is becoming increasingly important to enhance the attitudes and skills related to small data in both educational research and practice in schools. Good education is based on collective human judgment that is supported by a variety of evidence—both quantitative and qualitative. From the leadership point of view, if you don’t lead by small data, you will be led by big data.