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Jeremy Dodds
Supervisor of Data Analysis Services
Lori DeForest
Supervisor of Data Analysis Services
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Answers to most commonly asked Data Warehouse questions
Does Data Warehouse replace Student Management System?
No. Student Management Systems are designed to input and store current data, not analyze it. Data Warehouse provides the ability to analyze historical data collected from various sources over extended time periods.
What are processes involved in managing Data Warehouse?
The Data Warehouse brings together data from different Student Management Systems. It is the process of extracting, transforming, cleansing, and loading data elements into a central database to allow more accurate analysis and decision-making. However DW processes are far more complex than just collecting data and reporting against it. Data Warehousing requires both business and technical expertise and involves the following activities: - Accurately identifying the student information that must be contained in the DW - Extracting, cleansing, aggregating, transforming and validating the student data to ensure accuracy and consistency - Defining the correct level of summarization to support decision-making - Providing user-friendly, powerful reporting tools to access the data in the DW - Educating users about possibilities that are available to them through the DW - Establishing processes for maintaining, enhancing, and ensuring the ongoing success and applicability of the DW.
What if the data about our school is wrong on SEDREF?
If the information in SEDREF is incorrect, the Superintendent, principal or their designee should contact SED by fax or snail mail with the changes at:

Information and Reporting Services
NY State Education Department
Room 863 EBA
Albany, NY 12234
What is eScholar?
eScholarTM is a data warehouse specifically designed for school systems. A data warehouse brings together, into a single location, all of the data that a school district has and makes that data accessible to authorized users for analysis. This data includes attendance, grades, demographics, test scores, program participation, economic status and much more. All of this different information (domains of data) is usually found on different computers, in different locations, in different formats with widely varying degrees of accuracy (or cleanliness). A critical part of the data warehouse process is to "cleanse" and "standardize" all this data so that it can be combined in different ways to answer questions and provide comparisons between them. (
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