Virtual Data

Virtual data is a catch-all phrase for the profiles that will access, the configurations that will be accessible from, and the data that will be secured by Access Manager. Virtual data includes, but is not limited to, user profiles (such as employees or customers), data and service access rules, and other types of corporate data.

• What assets will Access Manager be protecting?

  Access Manager secures access to all types of data and services. An administrator can regulate who can view or configure Access Manager data as well as control access to applications, portals, and services.

• What users will leverage Access Manager?

  Users might include employees, business partners, suppliers, and current or potential customers. Each user will have a profile that includes, at a minimum, their user ID and password. Employees will undoubtedly have larger and more confidential profiles than customers who access external sales information.

• What data will be accessible?

  Data might include public information, internal information, confidential information, and restricted data. Data might also include sales information on an external web site, confidential employee profiles, access rules that protect corporate resources, server configuration information, and federated customer profiles.

• What is the authoritative source of the data?

  Often multiple schemas that define different types of data are used. These definitions need to be reconciled within your deployment. Be aware of data ownership issues, allowing the various LOB applications to maintain control over their data, where appropriate. It is imperative to balance the demands of the satellite groups in order to provide service that is representative of the overall enterprise as all services are critical to the larger organization.

Other technical considerations also include:

• Is the same information defined in multiple attributes?

• Do users have multiple cross-organizational profiles?

• Are the data stores located in front of the firewall?

• Is the data consistent across different data stores?

• How often is new data added or existing data modified?

For more information, see Categorizing Data.