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Geek Sync | Avoid the Seven Mistakes Data Modelers Make in Aiding Data Governance


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You can watch the replay for this Geek Sync webcast, Avoid the Seven Mistakes Data Modelers Make in Aiding Data Governance, in the IDERA Resource Center,

Data privacy, protection, and compliance legislation is becoming ever more important. In that context, organizations have been looking towards their data governance teams to make sure that they understand their data, know how it is classified, and where it resides.

In this session, join Karen Lopez in discussing the mistakes that data modelers make in supporting data governance programs — and that you should avoid! These mistakes include collaboration errors, data model security fails, data stewarding missteps, data model integrity harms, and more.

Newer compliance regulations can make these mistakes costly and difficult to recover from. Karen wants you to love your data — and your data model!

Speaker: Karen Lopez has more than 20 years of database design experience. She specializes in the practical application of design approaches, balancing development time frames with the need to deliver solutions that will support business agility and data quality needs. She’s known for her fun and engaging speaking and teaching style. She tweets about data, space exploration and her travel experiences at @datachick. Karen blogs at

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Geek Sync | Avoid the Seven Mistakes Data Modelers Make in Aiding Data Governance

  1. 1. Avoid the Seven Mistakes Data Model ers Make in Aiding Data Governance Karen Lopez, Data Evangelist InfoAdvisors
  2. 2. Karen Lopez • Karen has 20+ years of data and information architecture experience on large, multi-project programs. • She is a frequent speaker on data modeling, data- driven methodologies and pattern data models. • She wants you to be #TeamData®
  3. 3. Why this topic? Data Modeling + Data Governance = WIN Buzz More focus on compliance Governance + Data Models Teamwork FTW
  4. 4. Failing to Leverage Data Catalogs/Portals Data Inventory is a business need
  5. 5. Data Model Portal + Data Catalog Model diagrams Model content Glossaries Cross model searches Commentary/engagement
  6. 6. © 2016 IDERA, Inc. All rights reserved. Proprietary and confidential.© 2020 IDERA, Inc. All rights reserved. 8
  7. 7. © 2016 IDERA, Inc. All rights reserved. Proprietary and confidential.© 2020 IDERA, Inc. All rights reserved. 9
  8. 8. © 2016 IDERA, Inc. All rights reserved. Proprietary and confidential.© 2020 IDERA, Inc. All rights reserved. 10
  9. 9. © 2016 IDERA, Inc. All rights reserved. Proprietary and confidential.© 2020 IDERA, Inc. All rights reserved. 11
  10. 10. Portals and Data Catalogs
  11. 11. Commentary in Data Models
  12. 12. Tips • Leverage Portals and Catalogs • Feed/Subscribe to other catalogs • Encourage commentary • Encourage engagement
  13. 13. Not Understanding Data Compliance Impacts Data Compliance is a business need
  14. 14. DATA SENSITIVITY CLASSIFICATION Identifying sensitive data Attribute/Column Names Data profiling Data definition Labeling data Financial PII GDPR/CCPA Sharing data with its label First Name (PII) Credit Card Number (Financial) Special Meal Request (HIPAA)
  15. 15. Data Protection Security is a business requirement Business requirements in LDM Implementation in PDM
  16. 16. Tips • Use tools to classify your data • Analyze data • Update LDM • Update PDM
  17. 17. Assuming Data Modeling is About Databases Only Data Understanding is a business need
  18. 18. Purposes of a Data Model Recording Recording decisions Explaining Explaining concepts Establishing Establishing requirements Designing Designing for data Implementing Implementing for data
  19. 19. Data Models Need to be targeted to: • Audience • Purpose • Project* status • Review goals
  21. 21. Tips • Present tailored data models • Make them highly available (paper and online) • Use them…
  22. 22. Ignorning Data Security & Privacy Requirements Data Security is a business need
  23. 23. Data Security Requirements • Encryption • Masking • Row Level/Column Level • Access (CRUD) • Lifecycle
  24. 24. Encryption • Storage/file level • Column level • Database level • Database + network + client
  25. 25. Data Masking Applying a pattern to a column to mask that data Karen Kxxxxxx 1234 5678 9999 1234 **** **** **** 1234 15 April 2020 1 January 1990 $125,000.00 $0.00 K******@*******.com
  26. 26. Row Level Security Restricts access to certain rows or columns in a table Doctors should only be able to see data (rows) or patients to which they are assigned Salespeople should see only data for the region to which they are based Non-HR people should not see salary amounts
  27. 27. Access Levels & Lifecycles • Role-based access • User-based access • CRUD
  28. 28. Tips • Collaborate with security groups • Get business requirements • Add to LDM • Add to PDM • Collaborate with DBAs and Devs
  29. 29. Making the Logical Data Model Technically Intimidating Data Modeling is a business need
  30. 30. Add Callouts
  31. 31. Tips • Use Text Boxes • Use Hover Tips • Limit content • Have multiple layouts • Use tool features to manage
  32. 32. Making the Data Model Difficult to Review Data Clarity is a business need
  33. 33. Let’s just look at some examples…
  34. 34. Pain Points • Handwriting fonts • Difficult fonts • Difficult fonts • Too small fonts • Ugly fonts • Low Contrast Colors
  35. 35. Scaling  Save paper, yes  Fit-to-Page is nice  But…it’s not always a good thing  Scale for readability first
  36. 36. Tips  Use Templates  Require Templates  Get graphics designer help  Standardize fonts  Sample models are problematic 
  37. 37. Data Models Go Unused Data Collaboration is a business need
  38. 38. Reasons Unused Data Models • Silo-based organization • “Agile” • Availability • Hard to use • Not model-driven development
  39. 39. Requirements Data Model Database* More requirements / changes / tuning / whims + Non Model Stuff Data Model Driven Data Model Driven
  40. 40. How? Forward Engineering Reverse Engineering Round Trip Engineering Validation Checks Repository Collaboration Measure Compliance Reuse
  41. 41. Repository Collaboration
  42. 42. Tips  Data Model Driven Development  Data governance at each phase  Use Repository for collaboration  Use Team Server for collaboration  Use data models for collaboration
  43. 43. What does this mean to a Data Modeler/Architect? Use Data Models for several purposes, with multiple presenations Understanding when to use where to manage meta data: Data Modeling Tools or Data Governance tools Understanding the difference between Data Governance and Data Policing
  44. 44. White Paper
  45. 45. 61
  46. 46. Karen Lopez