1. Data modeling. Presentation by – Anupama Vudaru, Phani Kondapalli Content by – Prathibha Madineni, Subrahmanyam Kolluri October 2010

2. Preface Agenda – Basics of Data Modeling, Insurance industry and Erwin Duration and timings – 4 days x 2 hrs Expectations – In-class, hands on and post session work Course contents – Divided into slides, videos and print outs Legends used – Post-session work – Attendees are expected to do hands-on home work assigned for the day

3. Contents Day 1 A.Data Modeling overview B.Data Modeling development life cycle C.Components of Data Modeling D.Data Modeling notations and design standards E.Case study – CDM overview Day 2 A.Conceptual data model B.Types of Data modeling C.Various tools available D.Developing CDM using Erwin E.Case study – LDM overview Day 3 A.Logical data model B.Developing LDM using Erwin C.Meta Data preservation for Design Considerations D.Dimensional Data Modeling E.Case study – PDM overview Day 4 A.Physical data model B.Logical Data Model vs Physical Data Model C.Developing PDM using Erwin D.Advanced Features of Erwin

4. A.Data Modeling overview B.Data Modeling development life cycle C.Components of Data Modeling D.Data Modeling notations and design standards E.Case study – CDM overview Day 1

5. A. Data Modeling overview 1. What is a Data Model? Data modeling is the process of describing information structures and capturing business rules in order to specify information system requirements. A conceptual representation of data structures (tables) required for a database A graphical representation of ―Nature of data ―Business rules governing the data ―How it will be organized in the database with less complexity A data model represents a balance between the specific needs of a particular RDBMS implementation project, and the general needs of the business area that requires it. Mrs. Smith’s video library Is this how my business looks technically?

6. A. Data Modeling overview 2. Need for developing a Data Model A new application for OLTP (Online Transaction Processing), ODS (Operational Data Store), data warehouse and data marts. Rewriting data models from existing systems that may need to change reports. Incorrect data modeling in the existing systems A data base that has no data models. Effective means to express and communicate the business requirements. Johns Life Insurance (JFI) corporation * is a prominent life insurance provider in Dream Valley nation. We shall be using the case study of its business wherever possible in these sessions. * A fictitious life insurance corporation designed for this training.

7. A. Data Modeling overview 3. Benefits of Data Model The terms used in the model are stated in the language of the business, not that of the system development organization. Acts as a single version of truth and as a reference to: ― The DBA team to setup database ― The App dev team for macro designs and development ― As a means of communication in the team and with end-users Provides a clear picture of business relationships seen as entity relations or referential integrity constraints Provides a logical RDBMS-independent picture of the database Can be used to produce an executive summary diagram

8. B. Data Modeling development life cycle Second Phase Conceptual Data Modeling(CDM) CDM includes all major subjects and their components and inter dependencies. CDM contains business processes and regarding functioning of the organization. Third Phase Logical Data Modeling(LDM) LDM is the version of the model that represents entities, attributes and entity relationships. LDM can be validated against all of the business requirements of an organization. Fourth Phase Physical Data Modeling(PDM) PDM includes all required tables, columns, relationship, database properties for the physical implementation of the database. PDM can be validated against the data flow. Fifth Phase Database creation DBAs instruct the data modeling tool to create SQL code from physical data model. Then the SQL code is executed in server to create databases. 1. Life cycle First Phase Gathering Business Requirements Data Modelers interact with business analysts to get the functional requirements and with end users to find out the reporting needs.

9. B. Data Modeling development life cycle 2. Process, efforts and timelines Industry knowledge Understand the business requirements Meets with business executives Meets with end users Meets development team Meets database team Documentation and Meta Data Integration Requirements CDM LDM PDM DB

10. C. Components of Data Modeling CONCEPTUAL MODELING Subject Represents a grouping of related information for a single subject Ex: Account Relationship Relationship between subject areas are identified in terms of cardinality One to one Many to Many One to Many LOGICAL MODELINGPHYSICAL MODELING Entity Subjects are narrowed down to specific objects Ex: Savings Account Attribute Property or characteristic of an entity Ex: Number, Customer ID, Name, etc. Relationship Relationships between entities, as per the cardinality and type is identified Zero, one or more, Exactly Identified or Non-Identified Table A set of data elements organized in columns and rows Column Set of data element used to store specific type of data / values Constraint Defined with relationship using foreign keys constraints Others Column properties – data type, length/precision/scale, null, key type Indexes – type and columns Partitions – type and basis

11. D. Data Modeling notations and design standards 1. Notations

12. D. Data Modeling notations and design standards 2. Design Standards Overall Data Model Standards a.Data Model Name b.Data Model Definition Entities and their Relationships a.Entity Name b.Entity Definition c.Supertype/subtype Entity d.Entity Attribution e.Entity Normalization/De-normalization f.Entity Relationship - Relationship Name, Attributes a.Attribute Name b.Attribute Definition c.Unique Identifiers Database Related a.Naming Standards b.Common Database Standards Refer to: http://mike2.openmethodology.org/wiki/Data_Modelling_Standards_D eliverable_Template