If technology can affect student learning, shouldn't it be considered in making decisions? Data-driven decision making models include data from curriculum, instruction, test scores, lunch programs, budgets, and transportation. None of the current models include anything about technology. The challenge with any type of data-driven decision making process is threefold: collecting the information, finding what set of filters (questions) need to be created, and ensuring the filtered information is understandable and meaningful. The key to making it meaningful is filtering it specifically for the person viewing it. Questions need to be formed about which data is important. The reported data (or answers) can be used for all school stakeholders to make decisions. Because filtering is the bridge from the technical to the meaningful, the author begins with it. Ed Tech data can be filtered and ultimately made meaningful. Once the answers are provided, this data can be used like any other data to make decisions. The decisions include curriculum, instruction, learning, and assessment. (Contains 6 resources.)