The document explains the concept of meta models, which are abstracted data models providing structured guidelines for creating various models, similar to blueprints. It highlights the importance of solid models in enhancing data quality, ensuring consistency, and supporting data integration in modern enterprises. Additionally, it offers a framework for building a meta model and emphasizes leveraging tools, best practices, and standards for effective development and management.

1. www.ewsolutions.com
YOUR GUIDE TO
UNDERSTANDING
META MODELS

2. DEFINING A META MODEL
In simple terms, a Meta Model is an abstracted data
model that defines how and what types of models
can be built. It provides a standardized structure,
rules, and semantics for creating different models.
Think of it as a blueprint that guides the designing of
other models.
But why should you care about models? Well, think
about it; the better your blueprints, the better your
final product. And in the digital age where data is the
new oil, having robust, flexible, and streamlined data
and metadata models can make or break your
enterprise.

3. DIVING DEEPER INTO MODELS
WHAT TYPES OF
DATA ENTITIES EXIST
HOW THESE ENTITIES
INTERACT
THE ATTRIBUTES AND
PROPERTIES OF EACH
ENTITY
TO PUT IT DIFFERENTLY, MODELS ARE ALL ABOUT OPTIMIZING
STRUCTURE AND CONSISTENCY IN THE WORLD OF DATA MODELS.

4. META MODELS VS.
METADATA MODELS
Now, let's add another layer to your freshly-
baked knowledge cake: the Metadata
Model. As the name suggests, Metadata
structures and categorizes metadata — or
'data about data.' It chronicles how, when,
and by whom a certain data instance was
created and how it has been used and
modified over time.

5. WHAT'S THE POINT OF
META MODELS IN MODERN
ENTERPRISES
• Consistency Through Standardization: Ensures
everyone in the organization is on the same page
when interpreting data models and corresponding
data.
• Increasing Data Quality: The more structured your
data is, the more accurate and reliable it becomes.
• Supporting Data Integration: Facilitating smoother
integration of diverse data sources.
• Driving Metadata Management: Aids in creating
metadata models, leading to more effective
metadata management.

6. BUILDING YOUR META MODEL
– A STEP-WISE APPROACH
• Define entities: Begin by identifying the types of
'things' or entities your model will contain.
• Set relationships: Next, sketch out how these
entities relate to each other.
• Attribute assignments: For each entity type,
define its attributes.
• Establish rules: Finally, lay down rules that govern
these entities, relationships, and attributes.

7. ENHANCING COLLABORATION
THROUGH SHARED
UNDERSTANDING
• Leveraging Tools and Technologies: Also, leverage
tools and technologies that aid in the development,
visualization, and management of your model.
Sophisticated software solutions not only simplify the
creation process but also ensure consistency and
accuracy through automation.
• Implementing Best Practices and Standards:
Adopting best practices and industry standards is
another critical step. These practices, whether they
pertain to naming conventions, documentation
standards, or modeling methodologies, elevate the
quality of your model. They engender trust and
facilitate smoother integration with external data
models and standards.

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9. Thank You
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