Data Modeling Reports

From Data Modeling tools, reports can be easily generated for technical and business needs. The reports that have been generated from logical data model and physical data model are called as business reports and technical reports respectively. Most of the data modeling tools provide default reports like subject area reports, entity reports, attribute reports, table reports, column reports, indexing reports, relationship reports etc. The advantage of these reports is, whether they are technical or non-technical, everybody would understand what is going on within the organization.

Other than default reports provided by data modeling tools, a data modeler can also create customized reports as per the needs of an organization. For example, if an expert asks of both logical and physical reports of a particular subject area in one file(e.g in .xls), logical and physical reports can be easily merged and reports can be easily generated accordingly. Data Modeling tools provide the facility of sorting, filtering options and the reports can be exported into file formats like .xls, .doc, .xml etc.

Logical Data Model Report:

Logical Data Model Report describes information about business such as the entity names, attribute names, definitions, business rules, mapping information etc.

Logical Data Model Report Example:

Logical Data Model Report Example

Physical Data Model Report:

Physical Data Model Report describes information such as the ownership of the database, physical characteristics of a database (in oracle, table space, extents, segments, blocks, partitions etc), performance tuning (processors, indexing), table name, column name, data type, relationship between the tables, constraints, abbreviations, derivation rules, glossary, data dictionary, etc., and is used by the technical team.

Physical Data Model Report Example:

Physical Data Model Report Example

 

Data Modeling Standards | Modeling Data

Data Modeling standardization has been in practice for many years and the following section highlight the needs and implementation of the data modeling standards.

Standardization Needs | Modeling data:

Several data modelers may work on the different subject areas of a data model and all data modelers should use the same naming convention, writing definitions and business rules.

Nowadays, business to business transactions (B2B) are quite common, and standardization helps in understanding the business in a better way. Inconsistency across column names and definition would create a chaos across the business.

For example, when a data warehouse is designed, it may get data from several source systems and each source may have its own names, data types etc. These anomalies can be eliminated if a proper standardization is maintained across the organization.

Table Names Standardization:

Giving a full name to the tables, will give an idea about data what it is about. Generally, do not abbreviate the table names; however this may differ according to organization’s standards. If the table name’s length exceeds the database standards, then try to abbreviate the table names. Some general guidelines are listed below that may be used as a prefix or suffix for the table.

Examples:

Lookup – LKP – Used for Code, Type tables by which a fact table can be directly accessed.
e.g. Credit Card Type Lookup – CREDIT_CARD_TYPE_LKP

Fact – FCT – Used for transaction tables:
e.g. Credit Card Fact – CREDIT_CARD_FCT

Cross Reference – XREF – Tables that resolves many to many relationships.
e.g. Credit Card Member XREF – CREDIT_CARD_MEMBER_XREF

History – HIST – Tables the stores history.
e.g. Credit Card Retired History – CREDIT_CARD_RETIRED_HIST

Statistics – STAT – Tables that store statistical information.
e.g. Credit Card Web Statistics – CREDIT_CARD_WEB_STAT

Column Names Standardization:

Some general guidelines are listed below that may be used as a prefix or suffix for the column.

Examples:

Key – Key System generated surrogate key.
e.g. Credit Card Key – CRDT_CARD_KEY

Identifier – ID – Character column that is used as an identifier.
e.g. Credit Card Identifier – CRDT_CARD_ID

Code – CD – Numeric or alphanumeric column that is used as an identifying attribute.
e.g. State Code – ST_CD

Description – DESC – Description for a code, identifier or a key.
e.g. State Description – ST_DESC

Indicator – IND – to denote indicator columns.
e.g. Gender Indicator – GNDR_IND

Database Parameters Standardization:

Some general guidelines are listed below that may be used for other physical parameters.

Examples:

Index – Index – IDX – for index names.
e.g. Credit Card Fact IDX01 – CRDT_CARD_FCT_IDX01

Primary Key – PK – for Primary key constraint names.
e.g. CREDIT Card Fact PK01- CRDT-CARD_FCT_PK01

Alternate Keys – AK – for Alternate key names.
e.g. Credit Card Fact AK01 – CRDT_CARD_FCT_AK01

Foreign Keys – FK – for Foreign key constraint names.
e.g. Credit Card Fact FK01 – CRDT_CARD_FCT_FK01