This document discusses lean data lineage and how it can help organizations get more value from their data. It describes using lean data management principles and tools to analyze, benchmark, and deliver data lineage. Applying techniques like iterative development, prototyping, cross-functional teams, and actionable metrics can help organizations more efficiently discover data sources, build data models and documentation, measure results, and continually learn and improve their data lineage. This lean approach aims to provide outcomes like increased efficiency, clarity into how data moves through an organization, and more timely insights from data.

1. Lean Data Lineage
accelerated approaches to
achieve business outcomes

2. Data To Value
Architecture
Onsite consulting &
onsite / offsite
managed services
We help firms get more
value from their data
Lean data specialists

3. Tools & Partners
Data governance & metadata repository
• Semanta Encyclopaedia
Data Profiling & Quality Management
• Experian Pandora
Data Lineage & Enterprise Information
Flows
• Manta Tools - Flow, Checker, Data Types
Semantic Web & Linked Data platform
• Server – knowledge graph management
• Entity -Extraction
Amazon Web Services
• Cloud storage, analysis & processing tools.
Data Modeling
• SPARX Enterprise Architect
• Oracle Data Modeller
Data Visualisation
• Tableau
technology partners help
accelerate delivery
across all practice areas

4. Situation
the ability within a data architecture to
trace data from its origination
through all the processes that touch it
in that architecture
to all the places where it is used

5. Business Drivers
Cost Management
Compliance
Increasing Value

6. Data Movement Evolution
Point To Point
Data Integration
Integration Hairball

7. Complication
larger organisations have very
complex data integration and
business intelligence landscapes

8. Data Lineage Use Cases
Impact Analysis
Regulatory Reporting
Error Resolution

9. Data Lineage Environment
Expertise
Tooling
Documentation

10. Solution
apply lean data management approach and tools
to analyse, benchmark and deliver

11. Lean Data Management
Lean Data
Management
Shorter
iterations
Prototyping
& Minimum
Viable
Products
Build-
Measure-
Learn cycle
Early
adopters
Cross
functional
teams
Actionable
metrics

12. Data Lineage Acceleration
Harvest metadata
Capture quality,
governance &
modelling notes
Review issues &
enrich
documentation
Discover
Build
Measure
Learn
Test rules &
capture metrics
Data Profiling &
Data Discovery
Dependency, relationship
& lineage analysis
Integrate metadata
Prototype data
solutions
Implement practical
Data Governance
Produce insights and
analysis
Track progress
Plan next steps &
progress solutions

13. Outcomes
what will be different if you apply these techniques?

14. Lineage Outcomes
Efficiency & Accuracy
Clarity & Transparency
Timeliness

15. Future State Vision
data quality &
governance
metrics
data models,
glossaries &
dictionaries
disparate
data &
metadata
data profiling
& metadata
discovery
Ontologies
Controlled vocabularies

16. Managed Services - Architecture
Unstructured Data
Documents,
webpages etc.
analysed
- Customers
- Products
- Sales
- Activities
- Searches
- … Issues, Insights and
actions fed back into
cycle
Data DiscoveryData
Modelling
Insights & enhancements
presented using best
practice artefacts
Data
Profiling
Data Mining
Clean, transformed,
enriched, integrated, well
defined data outputs
Data
Management
Data
Cleansing
Standardising &
enriching
Reverse
Engineering
Documenting
Data
Sources
Structured
Data
Disparate
Files &
Databases
analysed

17. Passionate, Innovative, Lean
Lean Data Management Specialists
Data to Value Ltd.
2nd Floor Elizabeth House
39 York Road
London SE1 7NQ
United Kingdom
T +44 (0) 208 278 7351
www.datatovalue.co.uk
#leandata
@DataToValue
info@datatovalue.co.uk