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.

1. Petr Hájek November 25, 2020
Webinar:
Data Landscape Mapping

2. 2
Too much data…

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

16. 16
Questions
& Answers

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Thanks

18. Backup Slides