Big Data, Analytics, and Data Science Life Cycle - Getting Started with Greenplum for Big Data Analytics - page 3

often non-transactional/static in nature and can be stored centralized or du-
plicated. For example:
Reference data: country codes, PIN, branch codes, and so on
Master data: accounts, portfolio managers, departments, and so on
• Analytical data : Business data is analyzed and insights derived are presen-
ted for decision making; data classified under this category usually is not
owned by the analyzing application. Transaction data from various transac-
tion processing systems is fed for analysis. This data is sliced and diced at
various levels to help problem solving, planning, and decision-support as it
gives multi-dimensional views of various business activities. It is usually lar-
ger in volume and historic in nature when compared to transactional data.
In addition to the preceding categories, there are a few other important data classi-
fications. These classifications define the character data:
• Configuration data : This classification refers to the data that describes data
or defines the way data needs to be used. There can be many categories of
configuration data. For example, an application has many clients, and each
client needs to refer to a unique set of messaging configurations (let's say
a unique queue name) or information regarding how to format a report that
needs to be displayed for a particular user, and so on. This classification is
also referred to as metadata.
• Historic data : It refers to any data that is historic in nature. Typically gives
reference to facts at a given point in time. This data requires a robust archival
strategy as it is expected to be voluminous. At the same time, it would not
undergo any changes and is usually used as a reference for comparison.
Corrections/changes to historic data can happen only in the case of errors.
Examples can be, security price at a point in time, say January 20, 1996, fin-
ancial transactions of an account in the first quarter of the year, and so on.
• Transitional data : This is one of the most important data classifications that
refer to data that is intermediary and temporary in nature. This data is usu-
ally generated to improve the data processing speed and could be kept in
memory that is evicted post its use. This data might not be available for dir-
ect consumption. Example for this data classification can be an intermediary
computation data that is stored and is to be used in a bigger scheme of data