Data Governance and CA ERwin Active Model Templates

Transcription

1 Data Governance and CA ERwin Active Model Templates Vani Mishra TechXtend March 19, 2015 ER07

2 Presenter Bio About the Speaker: Vani is a TechXtend Data Modeling practice manager who has over 10+ years of experience working with CA ERwin and data modeling solutions. Pre-sales, post-sales, web training, instructions on CA ERwin and workgroup solutions have been her forte. Vani has a passion for MDM and DG practice. She started her career with Logic Works - the original vendor for CA ERwin. Vani has done lots of consultation to CA ERwin data-modelers, Data Warehousing users, Data Architects and BI users. She is a regular speaker and presenter on TechXtend Webinar series. 2

3 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 Schedule Chat immediately following 3

4 First Thoughts.. Allie Philpin - Big Data Governance and Consumer Privacy 4

5 Need Business team goal and ROIs IT Team and EAI team Initiatives Data Governance Identify the data Acquire the data Transform /Clean the data Monitor the data Store data Deliver data and CCM 5

6 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 Processes People Data Governance Technology 6

7 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 Data Governance 7

8 Roles To be Involved 1. Domain expert function consultant 2. Information architect 3. Data steward 4. Data analyst 5. Business analyst 8

9 Driving Forces Behind Data Governance 1. Growth of data 2. Regulatory oversight & compliance 3. Data security 4. Educated executive group 9

10 Data Modeling & Data Governance 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 And last but not least, robust documentation of all of the above 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. 10

11 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 This helps you visualize your database structure and lets you derive your physical schema with help of your data stewards. 11

12 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 Assist modelers in the process of synchronizing model objects with selected template model objects 12

13 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 Master model repository for collaboration 13

14 Active Model Template Step-By-Step

15 CA ERwin Editors Launch Editor from File Model Templates 15

16 Bind Template Allows the binding of one model to another Load Entire Model Content Selective inclusion via wizard 16

17 Template Wizard 17

18 Other Options Bind Additional Templates Refresh Sync to Current State Unbind Remove Define Filter Filter Object Types and Objects Synchronize Invoke Sync Editor 18

19 DG in Model Naming Options Prefix and Suffix Abbreviations Glossary Macros 19 19

20 DG via Workgroup - Iterations & Collaboration Application Lifecycle Model Management DG 20

21 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 Trying to move very fast from no-data-governance to enterprise-wide data governance 21

22 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 consequences are modeled. 22

23 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 6. Monitor the efficacy of your controls 23

24 Conclusion 24

25 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 LinkedIn Twitter.com Maximum Data Blogspot.com Maximum Data Modeling Please enjoy the rest of your time at ERworld 2015! 25

26 Legal Notice Copyright CA All trademarks, trade names, service marks and logos referenced herein belong to their respective companies. No unauthorized use, copying or distribution permitted. THIS PRESENTATION IS FOR YOUR INFORMATIONAL PURPOSES ONLY. CA assumes no responsibility for the accuracy or completeness of the information. TO THE EXTENT PERMITTED BY APPLICABLE LAW, CA PROVIDES THIS DOCUMENT AS IS WITHOUT WARRANTY OF ANY KIND, INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NONINFRINGEMENT. In no event will CA be liable for any loss or damage, direct or indirect, in connection with this presentation, including, without limitation, lost profits, lost investment, business interruption, goodwill, or lost data, even if CA is expressly advised of the possibility of such damages. 26