As a teacher or school leader, being able to analyze and use data is very important. Thorough, reliable information should be used to guide virtually all decisions that educators make. Data ranges from a small classroom scope to a very wide district or even state scope. Educators and leaders need to be able to use data to inform their decisions in the classroom, in schools, in districts and throughout the state. Data architecture requires one to look at the "target state" when making important decisions. "Data Architecture describes how data is processed, stored, and utilized in an information system. It provides criteria for data processing operations so as to make it possible to design data flows and also control the flow of data in the system." (Wikipedia). If an educational leader is putting data architecture to use, they should be following a prescribed process of analyzing and using important information to guide their work. This is such a very broad topic that can answered on a number of levels. On a small scale, data can and should guide teachers to making decisions regarding teaching techniques, student support, differentiation, interventions, etc. On a grander scale data should guide school leaders in all of their decision making regarding funding, program initiatives, interventions, and so much more. There is an abundance of data available- so school leaders also need to take into account the veracity of the data in which they are using. The source of the data makes a big impact on the level in which it should be regarded and used. Some data sources have a large bias, like we saw in this week's article sponsored by Dell. Teachers, admin, and school leaders need to understand how to analyze data and use it to benefit their districts and schools. They need to understand the data system, how to use it, when to use it, and to which extent it should be used. It is important for school leaders to make it a priority to become very familiar and comfortable with the data systems within their district and state. Decision making is virtually impossible without analyzing reliable data first.
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April 2016
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