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Mastering your data with ca e rwin dm 09082010
 

Mastering your data with ca e rwin dm 09082010

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    Mastering your data with ca e rwin dm 09082010 Mastering your data with ca e rwin dm 09082010 Presentation Transcript

    • Mastering Data withCA ERwin Data Modeler Jump Start Your Data Quality Initiatives
    • 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. PAGE 2
    • 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. PAGE 3
    • Agenda: The Road to Data Quality• Start Trusting Your Data• Obstacles & Object Lessons• Essentials• The Data Quality Steps PAGE 4
    • Trusting Your Data
    • 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. PAGE 6
    • Obstacles
    • Obstacles to Data Quality• People, Process or Software related… – All of the above. PAGE 8
    • 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. PAGE 9
    • The Essentials
    • Data Governance Essentials1. Metadata Standards2. Collaboration3. Structure4. Policies and Standards5. Cultural Change6. Getting from “as is” to “to be” PAGE 11
    • Data Modeling as the Hub Application Development Business Intelligence (BI) ERP Data ModelDatabase Management & Data Warehouse Administration Master Data Management (MDM) PAGE 12
    • The Steps
    • 1 – Defining Metadata Standards PAGE 14
    • 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. PAGE 15
    • Strict Yet Flexible• Too Strict Example. – Phone number as a single entry.• Too Flexible. – Phone number as XML? PAGE 16
    • Data Warehouse Example PAGE 17
    • Data Warehouse Example PAGE 18
    • Translation Example PAGE 19
    • Translation Example PAGE 20
    • Translation Example PAGE 21
    • 2 - Collaboration• Share designs and templates.• Model lineage and history.• Centralized reporting. PAGE 22
    • Overcoming Silo Mentality • Director of National Intelligence • “A Space” encourages collaboration. PAGE 23
    • Collaboration• Updates to apps migrate to source DBMS models and vice-versa.• Define and enforce your glossary and standard abbreviations.• Create templates. PAGE 24
    • 3 - Organization• Build on Existing Processes – You are already governing data (informally). – Identify your assets. PAGE 25
    • 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. PAGE 26
    • Possible Library Structure PAGE 27
    • Define your Security PAGE 28
    • 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. PAGE 29
    • 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 PAGE 30
    • Good Habits • Model Everything • Own your (meta)data. – Applications – Be a good shepherd. – DBMS – Do not pass along bad data. – Data Warehouses – ERP systems – Others • NoSQL databases, UML models, etc. • Model your Data Entry. – Valid Values? – Nullability? – Proper and matching PAGE 31 Datatypes/Domains.
    • 6 - Create Your “TO BE” Design• Create the “To Be” model.• Compare “As Is” and “To Be” environments• Create a process. PAGE 32
    • 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. PAGE 33
    • Questions? PAGE 34
    • Contact MeEmail MeVictor.rodrigues@programmers.comMy Bloghttp://maximumdatamodeling.blogspot.com/http://twitter.com/MaxDataModelinghttp://www.linkedin.com/groups?mostPopular=&gid=3141647 PAGE 35