Databases Reference
In-Depth Information
2.
Records and retention management.
Every ILM program
must maintain a catalog of laws and regulations that apply
to information in the jurisdictions in which a business
operates. These laws, regulations, and business needs
drive the need for a retention schedule that determines
how long documents should be kept and when they
should be destroyed. Records management solutions
enforce a business process around document retention.
3.
Legal Holds and Evidence Collection (eDiscovery).
Most corporations and entities are subject to litigation
and governmental investigations that require them to
preserve potential evidence. Large entities may have
hundreds or thousands of open legal matters with
varying obligations for data. Data sources include e-mail,
instant messages, Excel spreadsheets, PDF documents,
audio, video, and social media.
4.
Test Data Management.
The big data governance
program needs tools to streamline the creation and
management of test environments, subset and migrate
data to build realistic and right-sized test databases, mask
sensitive data, automate test result comparisons, and
eliminate the expense and effort of maintaining multiple
database clones.
•
Big Data Security and Privacy.
Since big data platforms provide
a wide array of possibilities to access any data types (internal
and external), the questions around ethical usage of data, data
security, and privacy become critical. A big data platform should
make positions to ensure security and privacy of data following
some of the methods outlined below:
1.
Data Masking.
These tools are critical to de-identify
sensitive information, such as birth dates, bank account
numbers, street addresses, and Social Security numbers.
2.
Database Monitoring.
These tools enforce separation of
duties and monitor access to sensitive big data by privileged
users. The database monitoring functionality must have a
minimal impact on database performance and should not
require any changes to databases or applications.
•
Cloud.
Organizations are also turning to the cloud because of
perceived flexibility, faster time-to-deployment, and reduced
capital expenditure requirements. A number of vendors offer big
data platforms in the cloud and we list a few examples below:
