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Mastering Data with CA ERwin Data Modeler
Jump Start Your Data Quality Initiatives
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Abstract
• Data is a company’s greatest asset. Enterprises that can harness the power of their data will be strategically positioned for the next business evolution. But too often businesses get bogged down in defining a data management process, awaiting some “silver bullet”, while the scope of their task grows larger and their data quality erodes. Regardless of your eventual data management solution is implemented, there are processes that need to occur now to facilitate that process. In this webinar we will discuss using your current data modeling assets to build the foundations of strong data quality.
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Biography
• Victor Rodrigues brings 10 years of experience of advanced usage of the CA ERwin Modeling suite first as a Senior Support Engineer for the CA ERwin Modeling suite of products and currently as a Senior Software Engineer for Programmer’s Paradise. In this time he has used his extensive experience to implement the tool with various large and small enterprises. This experience includes customization of the CA ERwin tool via the API and Forward Engineering template editor as well as maximizing modeling by integrating the product suite which includes CA Model Validator, CA Model Manager, CA Process Modeler, SAPhir, and now CA Data Profiler.
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Agenda: The Road to Data Quality
• Start Trusting Your Data
• Obstacles & Object Lessons
• Essentials
• The Data Quality Steps
Trusting Your Data
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Data Quality Realities
• Data is a company’s greatest asset.
• Accenture survey shows 40% trust “gut” over BI.
• 61% say good data was not available.
• Data plus quality equals information.
Obstacles
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Obstacles to Data Quality
• People, Process or Software related…
– All of the above.
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Silver Bullets?
• Isn’t the Data Warehouse/ERP solution supposed to be doing this?– Definitions can be context specific.
– Delays taking ownership of your data.
Nike/I2 CMS example.
The Essentials
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Data Governance Essentials
1. Metadata Standards
2. Collaboration
3. Structure
4. Policies and Standards
5. Cultural Change
6. Getting from “as is” to “to be”
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Data Modeling as the Hub
ERP
Data Warehouse
DataModel
Database Management &Administration
Application Development
Business Intelligence (BI)
Master Data Management (MDM)
The Steps
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1 – Defining Metadata Standards
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Why Metadata Matters
• Start by Defining Meta Data– Disagreements as to what a definition is
• Too Conceptual – Definitions are not possible
• Too strict
– Everything can be defined.
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Strict Yet Flexible
• Too Strict Example.– Phone number as a single entry.
• Too Flexible.– Phone number as XML?
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Data Warehouse Example
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Data Warehouse Example
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Translation Example
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Translation Example
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Translation Example
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2 - Collaboration
• Share designs and templates.
• Model lineage and history.
• Centralized reporting.
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Overcoming Silo Mentality
• Director of National Intelligence
• “A Space” encourages collaboration.
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Collaboration
• Updates to apps migrate to source DBMS models and vice-versa.
• Define and enforce your glossary and standard abbreviations.
• Create templates.
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3 - Organization
• Build on Existing Processes– You are already governing data (informally).
– Identify your assets.
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We Need Structure
• Add structure to your existing process.
• Link your models.
• Create libraries in your Model Manager that contain linked application models, related DBMS models, etc.
• Create your Model Manager security roles.
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Possible Library Structure
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Define your Security
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4 - Enforcing Standards
• Generate diagram and repository reports to other teams.
• Promote your value to your Business Analysis teams.
• A bidirectional hub to report your standards and update your policies.
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5 - The Hard Part – Cultural Change
• Data Quality requires a change of culture.
• There is no silver bullet. It is a process.
• Like any habit, it becomes easier with time.
• Replacing bad habits with good ones.
• The process must me bottom up and top down.• NUMMI plant example
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Good Habits
• Model Everything
– Applications
– DBMS
– Data Warehouses
– ERP systems
– Others
• NoSQL databases, UML models, etc.
• Model your Data Entry.
– Valid Values?
– Nullability?
– Proper and matching Datatypes/Domains.
• Own your (meta)data.– Be a good shepherd.
– Do not pass along bad data.
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6 - Create Your “TO BE” Design
• Create the “To Be” model.
• Compare “As Is” and “To Be” environments
• Create a process.
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Conclusion
• Treat data like the asset that it is.
• Data quality creates information.
• Strong metadata definitions + good habits = data quality.
• Data modeling allows us to structure our metadata.
• Data quality is a process and requires cultural changes.
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Questions?
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Contact Me
Email [email protected]
My Blog
http://maximumdatamodeling.blogspot.com/
http://twitter.com/MaxDataModeling
http://www.linkedin.com/groups?mostPopular=&gid=3141647
