Cédric Charlier explains how Belgium’s transmission system operator Elia shifted to a new paradigm by updating the definition of Data Steward. Key messages: * Focus on data consumption not data production for Data Governance and Data Quality * Data Stewardship is facilitation, not deep knowledge * Data Stewardship can be based on a network of knowledge and not on a "one-single-expert" model

1. Shift to a new paradigm by updating
the definition of “Data Steward”
Cédric L. Charlier
@Seddryck

2. Global positioning
Data Stewardship
Data governanceStrategic
level
Tactical
level
First point of
contact
Definition &
metadata
Data fitness Lineages
Security &
Policies level

3. Classical implementation
• Data steward is an expert of the
business process creating the
data
• is the natural point of contact
• is hands-on and has a good
knowledge of process output
• knows the quality of the process
output
• knows the lineages (business &
technical)

4. Some analogies
Is the pilot also the best
steward?
Is the field engineer also your
best sales representative?

5. Downstream usages
• What does your « classical » data steward know about downstream
usages?
• And where is the data value?
Datavalue
UseProduction

6. New usages of these data
Challenging the current data
fitness standard
Requiring updates in the lineage
Changing business process or
creating data duplication

7. Data centric means break the silos
• How are we challenging these
silos with this classical
approach?
• Data should be driving the
process and not just be the
hypothetical output

8. Time to switch to a new paradigm?

9. Who is our new data steward?
•The data consumer
•Expectations are driving
•definition
•quality
•Assessing data value

10. From producer-centric to consumer-centric
Before
what’s available
current output
as-is
After
what I need
what I want
to-be
Data definition
Data fitness
lineages

11. What about data knowledge?
• Data knowledge
• is broader than “how to produce”
• Includes data value
• Help the data steward to
understand:
• Data Partners/Data Custodians
• Build a network of knowledge
Data
partner
Data
steward
Data
custodian

12. Improvements design
Remove barriers to innovation/change (take the lead)
Ask the data steward to co-create improvements
• With partners
• With custodians
Focus on three axis
People Process Technology

13. Break the silos
• Avoid the notion of data
ownership
• Data belongs to the company,
not to a department
• Shared duties around data roles
• Accountability is based on KPI
defined by data stewards

14. Sponsorship through « Data Governance
Board »
• Take your responsibilities:
• KPI definition vs business outcome
• No sponsorship vs KPI’s results
• Co-sponsorship
• KPI monitoring

15. Many consumers means many data stewards?
• They could have different needs
(latency, quality …)
• Why should we align them:
• Different usages means different
needs
• Traceability of the data
requirements is important

16. At the end, we need one definition!
• Having « multiple » potential data stewards opens the discussion
• Alignment process on the definition let’s the company defines if it’s
really the same data!
• Let data stewards start independently and then converge before the
« certification »
• One definition but different fitness expectations

17. Conclusions
• Paradigm shift from producer-centric to usage-centric
• Focus on the value (and not the cost)
• Applicable to most of the data dimensions (Quality, BI & AA,
Operations …)
• Data Stewardship can be based on a network of knowledge and not
on a “one-single-expert”
• Data stewardship is facilitation not deep knowledge
• Break the silos