4 Steps Towards Data Transparency
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The webinar discusses the importance of data landscape mapping to achieve data transparency through a structured metadata approach. It outlines a step-by-step process to identify, store, contextualize, and manage data effectively within an organization. Additionally, it emphasizes the necessity of understanding data ownership and quality to enhance overall data management.
4 Steps Towards Data Transparency
- 1. Petr Hájek November 25, 2020 Webinar: Data Landscape Mapping
- 3. 3 Typical responses to “problems with data” Metadata Governance Data Warehouse Data Stewardship Data Stewardship Data Governance Officer Data Quality Department Master Data Management Information Management Competence Data Architecture Operational Data Store Business Glossary Data Dictionary Data Management Program
- 4. 4 Each good story book begins with a map
- 5. 5 How to achieve a “Data Transparency” The goal is to prepare multi-dimensional or layered map in the form of (semi-)structured metadata which will allow us to browse through the enterprise data landscape like in any geographical digital map. We call this process a “Data Landscape Mapping”
- 6. 6 Metadata structure for Data Transparency Model DATA ELEMENT Logical Model Entity Business Process Mapping Physical Data Storage Data Lineage Data Utilisation Information Security & Privacy Detected Semantic Data Profile Data Quality Ownership 3 2 1 4
- 7. 7 Before you start › Do not be ashamed for Excel (Do not start with oversized data management toolsets) › Combine manual, automated and semi-automated activities › Allow for ‘Hic Sunt Leones’ places in your map
- 8. 8 Step 1 – Logical Data Model: What data? › Identifications of entities › Business definitions of entities › Structures of entities, their attributes and relationships
- 9. 9 Step 2 – Physical Data Stores: Where is the data? › Where is the data physically? › Are there any overlaps in the data? › Do we need to bother with data consolidation? › Shall we aspire for “golden records”? › What are the volumes of the data? › What are numbers of records? › What are daily increments of the data? › How much data is changed during the day/month/year? Semantic Model Real World Physical Data Stores
- 10. 10 Step 3 – Business Processes Context: Who needs the data? › How frequently do we need to “touch” the data? › How frequently to we need to update/refresh the data? › Are answers for these questions the same equally for all business processes? › Or, are there different needs for the data in terms of accessibility, level of detail, data quality, frequency etc.? › What is the quality of data? › Are we able to define it and measure it? Credit: https://medium.com/@sonicmsba/how-to- build-an-effective-business-context-for- data-analytical-problems-cb02906341cd Business Context Modeling Data Garage Storytelling
- 11. 11 Step 4 – Organization dimension: Who owns the data? › Who is responsible owner of the data? › Who understands the data? › Who takes care of the data?
- 12. 12 Metadata for Data Transparency Model DATA ELEMENT Logical Model Entity Business Process Mapping Physical Data Storage Data Lineage Data Utilisation Information Security & Privacy Detected Semantic Data Profile Data Quality Ownership
- 13. 13 Metadata Model – Reductio ad absurdum DATA_OBJECT DATA_OBJECT_ INSTANCE ATTRIBUTE ATTRIBUTE_ INSTANCE DATA_ELEMENT DATA_ELEMENT_ INSTANCE
- 14. 14 Present your maps 1 7 3,5 5 0,5 Business Proces 1 Business Proces 2 Business Proces 3 Business Proces 4 Business Proces 5 1 System A 100% 14% 29% 20% 200% 15 System B 1500% 214% 429% 300% 3000% 3 System C 300% 43% 86% 60% 600% 0,5 System D 50% 7% 14% 10% 100% 1 System E 100% 14% 29% 20% 200% 4 System F 400% 57% 114% 80% 800% 5 System G 500% 71% 143% 100% 1000% 3 System H 300% 43% 86% 60% 600% 17 System I 1700% 243% 486% 340% 3400% 3 System J 300% 43% 86% 60% 600% 10 System K 1000% 143% 286% 200% 2000% DataRetentionCapacity(yrs) Data Retention Requirements (yrs)
- 15. 15 Meta MartmDWH Metadata sources What next? Build your “Metadata Warehouse” Standard Business DWH solution Stage / Data Lake DWH Core Data Mart Integrated Metadata solution Data Load Data Integration Data Usage Ingest Metadata Organize Metadata Consume Metadata
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- 18. Backup Slides