Data Management

This page was composed originally as part of the initial data warehouse project plan, and contains historical information - a discussion of one key reason why the data warehouse ended up being deployed, namely, to facilitate data management.

So what is data management, and why do we need it at Carleton?  And why does it form a core part of the data warehouse plan?

Data at Carleton, specifically administrative data about (prospective) students, faculty, staff, alumni, friends, trustees, parents, etc. is housed under many roofs, i.e., it is housed in multiple distinct information systems.  The data is also stored in semi-compatible or occasionally outright incompatible ways.  And it is entered, maintained, updated, and secured using separate software packages, separate security procedures, and separate support staff.

The separateness, or fragmentation, of Carleton's data infrastructure has certain benefits.  For example, it facilitates the development of deep expertise in each of the separate systems and in the kinds of data they contain.  At the same time, though, the fragmentation makes it hard for overarching analysis of data that crosses multiple systems.  It can also make it difficult to find and access data, because of the plethora of systems, software, procedures, and support personnel that must be navigated.

The goal of the data warehouse project/service is to help bring all the most-used data at Carleton under one roof, with one set of naming conventions, one set of data definitions, one set of security policies, and one "approved" set of access tools and methods that we can centrally support.  The goal of the data warehouse, in other words, is consolidation and standardization.

To achieve these goals of consolidation and standardization it is vital that we have a procedure in place for making unified decisions about naming, data definitions, security policy, access tools, and access methods, i.e., a mechanism for managing data in a standard way, and an agreed upon set of policies that govern the decision making process.  Practically speaking what this translates into is

  1. An institutional data management policy, and
  2. A data management group charged with formulating the data management policy and for making ongoing policy decisions necessary to the construction, maintenance, and support of a data warehouse

Carleton's formal Data Management Policy (1 above) may be found here (see also the link in the sidebar).

The data management group (2 above) has also been formed, as of April 2009.  It is comprised of data managers from most administrative offices, and representatives from each of the 3 enterprise management systems.  We meet once per month to discuss data policy, processes, and standardization.