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What Is Data Warehousing and Why Is It Important?Dr Brian Preston, Nassau BOCES, Massapequa NY

"Data warehousing is the coordinated, architected, and periodic copying of data from varioussources, both inside and outside the enterprise, into an environment optimized for analyticaand information processing." (Alan Simon, Data Warehousing for Dummies.)

Across the country, school districts are being held accountable for improving studentachievement on multiple state examinations. School Report Cards are reporting performanceto the public in increasingly detailed ways, and on the horizon, student achievement will bedisaggregated even further for public accountability. New plans for the provision of services tostudents in all grade levels who are at-risk of not meeting new graduation standards are now

being approved for implementation in the coming school year. The state educationdepartments regularly identify the need to use data as the starting point for virtually all staterequired plans. Getting at this data is frequently challenging, since in a typical district, thedata resides in a student database, a transportation database, a human resources database,financial systems, and filing cabinets and folders. Efficiently collecting all the relevant data onthe factors that affect student performance is time consuming, at the very least, and in somecases, impossible, because the data was never collected in the first place.

Data warehousing represents a significant solution to this increasing challenge. A datawarehouse is, simply put, a single large database that has collected relevant information fromseveral other sources into a single accessible format, from which users in a district can tease

out useful information about students that would otherwise remain hidden.

Here's an example of using data for planning in the current, cumbersome manner. OneEastern states staff worked with a district to examine patterns of low performance among 4 th

graders on ELA examinations. Using LEAP data from grade 4 testing, and PEP test data fromthe same students in grade 3, collected in Excel format, it was difficult to see patterns of lack ofsuccess. We looked at ethnicity, mobility, poverty, and ESL status and did not see bigdifferences on scores. The district provided a list they had identified as at-risk, and furtheanalysis of the at-risk population showed that ESL students were doing better on the ELAexamination than native speakers of English. That was not making a lot of sense, so we askedthe district to identify the language spoken at home for these students. This data had to be

culled by hand from paper records in the district. When this was complete, we discovered thatSpanish speakers outperformed the native speakers of English, and speakers of otherlanguages were doing significantly worse. We were able to suggest several additionaanalyses for the district to undertake to determine why this was happening, and they were ableto use the information to develop efficient ways to apply limited resources to createinterventions appropriate for their low performing students.

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