Data Modeling, Master Data Management, Metadata, and Data Quality Management Overview

Information modeling describes the metadata needed to understand the data, processes, and rules related to an enterprise (Figure 1). It has three main areas:

Data Modeling – Logical data models define business terms and data elements in context, such as grouping customer and prospect entities.

Process Modeling – Defines the business processes used by the enterprise, employing data model entities to describe how data is created or transformed, e.g., the process of converting a prospect into a customer.

Rule Modeling – Describes data governance and compliance policies, specifying quality and management rules that data must follow, such as age restrictions for customers or archival rules for data older than five years.

Data modeling is the process by which IT and business agree on a common list of business terms (entities), their attributes, constraints, and relationships. Maintaining and documenting data models becomes a key capability for organizations to serve diverse data acquisition needs across critical projects.

There are several forms of data models:

Relational model – used for building Online Transaction Processing (OLTP) systems, typically normalized to the third normal form to avoid redundancy.

Dimensional model – used for building Online Analytical Processing (OLAP) systems; data warehouses can be designed using Kimball or Inmon methodologies, or a hybrid approach.

Master Data Management

Master Data Management (MDM) includes processes, governance, policies, standards, and tools that consistently define and manage an organization’s critical data, providing a single reference point.

Master data may include:

Reference data – business objects for transactions and dimensions for analysis.

Analytical data – supports decision‑making.

MDM aims to keep master data unified and consistent; it shares a common goal with Enterprise Information Architecture (EIA) – a consistent definition of master data. Ultimately, the architecture of master data is a shared process among MDM, Enterprise Information Management (EIM), and EIA, creating an information‑management environment that reduces the effort of managing master data.

Metadata Management

Metadata provides a reference framework for data. Forrester Research defines metadata as “information that describes or supports the context of data, content, business processes, services, business rules, and policies within an organization’s information system.” For example, an app listed in Apple’s App Store has metadata such as description, price, user rating, reviews, and developer.

Relevant types of metadata in a data‑management environment include:

Technical metadata – technical information about data, such as source table names, column names, and data types (e.g., string, integer).

Business metadata – business context for data, such as names, definitions, owners, and related reference data for business terms.

Operational metadata – information about data usage, such as last update date, access frequency, or last accessed date.

Metadata management is an end‑to‑end process for creating, enriching, and maintaining a metadata repository and its associated processes. It involves establishing procedures, mental models, organization, and capabilities to build a metadata environment. Like BI and MDM, metadata management faces challenges related to business‑process governance and culture.

Data Quality Management

Data quality can be viewed as:

The degree to which data accurately reflects the real‑world scenario it describes.

The state of completeness, validity, consistency, timeliness, and accuracy that makes data suitable for a specific purpose.

The sum of data characteristics and attributes that determine its suitability for a given purpose.

The processes and techniques that ensure data values meet business requirements and acceptance criteria.

Data that is complete, standards‑based, consistent, accurate, and timestamped.

Data quality management involves establishing and deploying roles, responsibilities, policies, and procedures related to data acquisition, maintenance, dissemination, and disposition. A partnership between business and technical teams is essential for success. Business units define and validate data‑quality rules, while the IT group builds and manages the overall environment (architecture, technical infrastructure, systems, and databases) for electronic data assets.

The following diagram illustrates the data‑quality‑management process:

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