Data Governance and CA ERwin®
Active Model Templates
Vani Mishra TechXtend
Presenter Bio
About the Speaker: Vani is a TechXtend Data
Agenda
What is Data Governance?
What are the driving forces behind Data Governance?
Data Governance & Data Modeling
CA ERwin and Data Governance
CA ERwin Active Model templates
Step By Step…
Conclusion
Need…
Business team goal
and ROIs IT Team and EAI team Initiatives
Data Governance
The formal orchestration of
people, process, and technology to enable an organization to leverage data as an enterprise asset
Data governance model is a set of processes, policies, standards and
technologies required to manage and
ensure the availability, accessibility, quality, consistency, auditability, and security of data within the organization
Why Data Governance ?
Do you have any of the following questions?
• What policies are in place, who writes them, and how do they get approved and changed?
• Which data should be prioritized? What is the location and value of the data?
• What vulnerabilities exist? How are risks classified and which risks do you accept, mitigate or transfer?
• What controls are in place, who pays for the controls and where are they located?
• How is progress measured, who audits results and who receives this information?
• What does the governance process look like and who is responsible for governing?
Having one or more of these questions means
you need
Roles To be Involved
1.
Domain expert – function consultant
2.
Information architect
3.
Data steward
4.
Data analyst
Driving Forces Behind Data Governance
1.
Growth of data
2.
Regulatory oversight & compliance
3.
Data security
Data Modeling & Data Governance
DATA GOVERNACE
Data governance is a set of processes to formally manage data throughout an enterprise
Data governance helps ensure that business data is accurate and can be trusted
Data governance holds people accountable for low data quality and the fallout from using such data
Data governance is a quality control discipline for assessing, managing, using, improving, monitoring, maintaining and protecting a company’s information.
The data model conceptualizes and unites all of the things that are important to an organization, as well as the rules governing those concepts. Many enterprise data models serve as the foundation for data integration, data rationalization,
and strategic information systems planning. And all of these efforts are needed to implement a robust data governance program.
DATA MODELING
A shared, integrated approach for all corporate data
Business process alignment and the elimination of redundancies
Checks and balances to improve data accuracy
A dynamic representation of the current and future state of the business’ data and its information assets
How Data Modeling Supports Data Governance
Explore existing data modeling & data architecture
Get to basics: look into C-L-P (conceptual – logical – physical) modeling practice
Conceptual Modeling
• It is the most abstract form of Data model. • It is helpful for communicating ideas to a wide
range of stakeholders because of its simplicity.
• This also provides a good basis for Structuring of a Data Governance program.
Logical Model
• This define structure of data elements, relationship and activities of data stewards.
Physical Model
CA ERwin Active Model Templates & Governance
Reuse and Object Sharing
• Key to achieving cost savings and quality improvements
Active Model Templates
• Model objects can be more easily reused and shared
• Multiple modeling teams can leverage existing assets – rather than having to “reinvent the wheel”
Wizard driven
Active Model Objects to Think about….
Master entities/tables
Master attributes/domains
Master definitions
Master domains
Master UDPs
Master .NSM
Master conceptual model theme
Master data stewards
Master sources
CA ERwin Editors
Launch Editor from…
Bind Template
Allows the binding of one model to another
• Load Entire Model Content
Other Options
Bind
• Additional Templates
Refresh
• Sync to Current State
Unbind
• Remove
Define Filter
• Filter Object Types and Objects
Synchronize
Prefix and Suffix
Abbreviations
Glossary
Macros
DG via Workgroup - Iterations & Collaboration
Application Lifecycle Model Management
Data Governance Challenges
Cultural barriers
Lack of senior-level sponsorship
Underestimating the amount of work involved
Long on structure and policies, short on action
Lack of business commitment
Lack of understanding that business definitions vary
Data Governance Challenges
A lack of cross-organizational data governance structures, policy-making, risk calculation or data asset appreciation, causing a disconnect between business goals and IT programs.
Governance policies are not linked to structured requirements gathering, forecasting and reporting.
Risks are not addressed from a lifecycle perspective with common data repositories, policies, standards and calculation processes.
Metadata and business glossaries are not used as to track data quality, bridge semantic differences and demonstrate the business value of data. Few technologies exist today to assess data values, calculate risk and
support the human process of governing data usage in an enterprise. Controls, compliance and architecture are deployed before long-term
Six Steps to Data Governance Success
1. Get a governor and the right people in place to govern
2. Survey your situation
3. Develop a data governance strategy
4. Calculate the value of your data
5. Calculate the probability of risk
Thank You for Attending!
For any further questions, feel free to join the Chat Session following this presentation, or contact me outside of ERworld. Vani Mishra([email protected])
LinkedIn Maximum Data Modeling
Twitter.com @MaxDataModeling
Blogspot.com Maximum Data Modeling
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