1 Introduction to Oracle Retail Data Model Customization

Components of the Oracle Retail Data Model

Oracle Retail Data Model includes the following components which are described in detail in Oracle Retail Data Model Reference:

• Logical model which is a third normal form (3NF) entity-object standards-based model.

• Physical model defined as an Oracle Database schema.

• Intra-ETL database packages and SQL scripts to extract, transform, and load (ETL) data from the Oracle Retail Data Model 3NF physical tables to the Oracle Retail Data Model derived and aggregate objects.

• Sample reports and dashboards developed using Oracle Business Intelligence Suite Enterprise Edition.

• Data Mining Models models for: Employee Analysis, Customer Analysis, Store Analysis, Item Analysis, and Product Analysis.

• DDL and installation scripts.

Oracle Products That Make Up Oracle Retail Data Model

Several Oracle technologies are involved in building the infrastructure for Oracle Retail Data Model:

• Oracle Database with OLAP, Data Mining and Partitioning Option

• Oracle Development Tools

• Oracle Business Intelligence Suite Enterprise Edition Presentation Tools

Oracle Database with OLAP, Data Mining and Partitioning Option

Oracle Retail Data Model uses a complete Oracle technical stack. It leverages the following data warehousing features of the Oracle Database: compression, partitioning, advanced statistical functions, materialized views, data mining, and online analytical processing (OLAP).

Oracle Development Tools

You can use the following Oracle tools to customize the predefined physical models provided with Oracle Retail Data Model, or to populate the target relational tables and materialized cube views.

Oracle Business Intelligence Suite Enterprise Edition Presentation Tools

Oracle Business Intelligence Suite Enterprise Edition is a comprehensive suite of enterprise BI products that delivers a full range of analysis and reporting capabilities. You can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to customize the predefined dashboard reports that are provided with Oracle Retail Data Model.

Prerequisite Knowledge for Implementors

As outlined in "Oracle Products That Make Up Oracle Retail Data Model", the Oracle Retail Data Model uses much of the Oracle stack. Consequently, to successfully implement the Oracle Retail Data Model, the implementation team needs:

• Experience performing information and data analysis and data modeling. (Experience using Oracle SQL Developer Data Modeler, is a plus.)

• An understanding of the Oracle technology stack, especially data warehouse (Database, Data Warehouse, OLAP, Data Mining, Warehouse Builder, Oracle Business Intelligence Suite Enterprise Edition)

• Hands-on experience using:

  • Oracle Database

  • PL/SQL

  • SQL DDL and DML syntax

  • Analytic Workspace Manager

  • Oracle SQL Developer Data Modeler

  • Oracle Business Intelligence Suite Enterprise Edition Administrator, Answers, and Dashboards

• One implementation team member is familiar or has training with Oracle Retail Data Model, or the team engages a partner with Oracle Retail Data Model implementation experience.

Responsibilities of a Data Warehouse Governance Committee

Governance is of concern to any enterprise, executive team or individual with an interest in the processes, standards, and compliance. It is even more important to organizations that have invested in data warehousing.

Data warehouse governance occurs within the context of overall IT governance. It provides the necessary policies, process and procedures, which must be clearly communicated to the entire corporation, from the IT employees to the front-end operational personnel.

Before you customize Oracle Retail Data Model, setup a data warehouse governance steering committee if one does not exist. The role of this steering committed is to oversee the data warehouse to provide an environment that reaches across the enterprise and drives the best business value.

Data Warehouse Governance Committee: Overall Responsibilities

The data warehouse governance steering committee sets the direction and is responsible for the governance framework, including the following areas:

• The entire data warehouse life cycle

• The data to process and the data to make available to end-users

• The minimum quality criteria for the data that is available to end users and how to measure and analyze these criteria against the quality of the data that is the source data for the data warehouse.

• The business goals of the organization to apply core information from the data warehouse.

• The policies, procedures, and standards for data resources and data access.

• The life cycle of data warehouse component management.

Data Warehouse Governance Committee: Data Governance Responsibilities

The more detailed focus in data warehouse governance is data governance. Data governance tasks include:

• Approving the data modeling standards, metadata standards and other related standards. This includes determining a metadata strategy as discussed in "Managing Metadata for Oracle Retail Data Model"' and identifying the data modeling tools to use that support these standards.

• Determining the data retention policy.

• Designing a data access policy based on legal restrictions and data security rules.

• Designing a data backup strategy that aligns with the impact analysis to the business unit.

• Monitoring and reporting on data usage, activity, and alerts.

Metadata Categories and Standards

Metadata is organized into three major categories:

• Business metadata describes the meaning of data in a business sense. The business interpretation of data elements in the data warehouse is based on the actual table and column names in the database. Gather this mapping information and business definition and rules information into business metadata.

• Technical metadata represents the technical aspects of data, including attributes such as data types, lengths, lineage, results from data profiling, and so on.

• Process execution metadata presents statistics on the results of running the ETL process itself, including measures such as rows loaded successfully, rows rejected, amount of time to load, and so on.

Since metadata is so important in information management, many organizations attempt to standardize metadata at various levels, such as:

• Metadata Encoding and Transmission Standard (METS). A standard for encoding descriptive, administrative, and structural metadata regarding objects within a digital library.

• American National Standards Institute (ANSI). The organization that coordinates the U.S. voluntary standardization and conformity-assessment systems.

• International Organization for Standardization (ISO). The body that establishes, develops, and promotes standards for international exchange.

• Common Warehouse Metamodel (CWM). A specification, released and owned by the Object Management Group, for modeling metadata for relational, non-relational, multi-dimensional, and most other objects found in a data warehousing environment.

When you implement your metadata management solution, reference your data warehouse infrastructure environment and make the decision which standard to follow.

Working with a Metadata Repository

You manage metadata using a Metadata Repository. At the highest level, a Metadata Repository includes three layers of information. The layers are defined in the following order:

1. A Physical layer. This metadata layer identifies the source data.

2. A Business Model and Mapping layer. This metadata layer organizes the physical layer into logical categories and records the appropriate metadata for access to the source data.

3. The Presentation layer. This metadata layer exposes the business model entities for end-user access.

The first step in creating a Metadata Repository is to scope your metadata management needs by:

• Identifying the metadata consumers. Typically, there are business consumers and technical consumers.

• Determine the business and technical metadata requirements.

• Aligning metadata requirements to specific data elements and logical data flows.

Then:

• Decide how important each part is.

• Assign responsibility to someone for each piece.

• Decide what constitutes a consistent and working set of metadata

• Determine where to store, backup, and recover the metadata.

• Ensure that each piece of metadata is available only to those people who need it.

• Quality-assure the metadata and ensure that it is complete and up to date.

• Identify the Metadata Repository to use and how to control that repository from one place

After creating the metadata definitions, review your data architecture to ensure you can acquire, integrate, and maintain the metadata.

As the data is updated in your data warehouse day by day, you need to update the Metadata Repository. To change business rules, definitions, formulas or process especially when customizing the Oracle Retail Data Model, your first step is to survey the metadata and do an impact analysis to list all of the attributes in the data warehouse environment that would be affected by a proposed change.

Browsing the Metadata Repository Supplied With Oracle Retail Data Model

To customize the Oracle Retail Data Model model, you must understand the dependencies among Oracle Retail Data Model components, especially how the report KPIs are mapped to the physical tables and columns and how that data has been transformed through the intra-ETL.

Oracle Retail Data Model provides a tool called the "Oracle Retail Data Model Metadata" browser that helps you discover these dependencies. When you install Oracle Retail Data Model with and the sample reports, the metadata browser is delivered as a Dashboard.

There are four tabs (reports) in the Oracle Retail Data Model Metadata browser:

• Measure-Entity tab: Business Areas and Measures Attributes and Entities

• Entity-Measure tab: Entity to Attribute Measures

• Program-Table tab

• Table-Program tab

Measure-Entity tab: Business Areas and Measures Attributes and Entities

On the Measure-Entity tab the measure descriptions, computational formulas with physical columns, physical tables, and corresponding entities can be viewed by Business Area.

To browse the data, select the business area and measure description that you are interested in.

Entity-Measure tab: Entity to Attribute Measures

The Entity-Measure tab displays the measures supported by the entities and how they are calculated. You can discover information about particular entities and attributes.

For example, take the following steps to learn more about an entity:

1. Select the entity.

2. Click GO.

Program-Table tab

The Program-Table tab displays the input and output tables used in the selected programs. For example, take the following steps to learn more about intra-ETL mappings:

1. Select the program type (that is, intra-ETL or report) and program name for showing particular report or intra-ETL information.

2. Select GO.

Table-Program tab

The Table-Program tab lists the Programs used by a given table and whether that table is an input or output, or both, of that program.

To discover what reports use a particular table, you must move a particular table from the right pane to the left (Selected) pane.

For example, to see the reports that use a particular table, take the following steps:

1. In the right pane of the Table-Program tab, select the table.

2. Move the table to the Selected list on the left by clicking on < (left arrow), and click OK.

3. Select GO.

   The reports for the selected table are displayed.

Using the Metadata Generation Packages

As described in "Browsing the Metadata Repository Supplied With Oracle Retail Data Model", Oracle Retail Data Model metadata browser lets you view and analyze metadata through a set of BIEE reports to better understand relationships between metadata objects and for end-to-end impact analysis.

You use the Oracle Retail Data Model metadata browser generation packages to generate and update the Oracle Retail Data Model metadata browser.

There are four main tables and other staging tables and views in the metadata generation package. The tables are: MD_ENTY, MD_PRG, MD_KPI, and MD_REF_ENTY_KPI; these are the tables that support metadata browser reports.

For more information, see Chapter 6, "Metadata Collection and Reports".

Using Oracle Warehouse Builder with the Oracle Retail Data Model

Oracle Warehouse Builder provides excellent metadata management features that you can use with Oracle Retail Data Model.

Before you import source metadata into Oracle Warehouse Builder, you create a module to contain metadata definitions. The type of module you create depends on the source from which you are importing metadata into your Metadata Repository. Oracle Warehouse Builder supports many different sources and targets as discussed in Oracle Warehouse Builder Sources and Targets Guide. For example, when connecting to an Oracle database, Oracle Warehouse Builder queries the database dictionary to extract all needed metadata on tables, views, sequences, dimensions, cubes, data types, PL/SQL packages, and so on.

Oracle Warehouse Builder also provides an interface tool called the Metadata Dependency Manager through which you can explore dependencies among data objects, as represented by the metadata in your Oracle Warehouse Builder repository. The Metadata Dependency Manager presents dependencies in the form of interactive lineage and impact diagrams. A lineage diagram traces the data flows for an object back to the sources and displays all objects along those paths. An impact diagram identifies all the objects that are derived from the selected object.

Information provided by the Metadata Dependency Manager can help you in many circumstances. For example:

1. Starting from a target object, such as a dimension, cube, or business intelligence tool report, you can identify the columns in each data source that are used in computing the results in the target.

2. You can assess the impact of design changes in an object such as a source table or a pluggable mapping that is used throughout a project.

3. You can propagate a design change, such as a change to the data type of a source table column, to downstream objects in the design.

Using end-to-end data lineage and impact analysis reduces project risk by allowing better planning for design changes, faster identification of unanticipated impacts when source systems change, and enabling more effective auditing of your business intelligence results, master data or other data integration processes.

You can also define and use SQL-based or XML-based custom metadata stores to retrieve definitions of source and target objects such as tables and views.

For data files extracted from some mainframe sources, Oracle Warehouse Builder can interpret Cobol Copybook files that describes the structure of the data file, and create its source metadata based on that.

Oracle Warehouse Builder application adapters or application connectors provide additional metadata about ERP and CRM application sources.

Oracle Warehouse Builder can deploy or execute process flows and schedules to Oracle Enterprise Manager and Oracle Workflow. In general, you can deploy a schedule in any Oracle Database location (Oracle Database 11g or later).