Breaking the conspiracy for ignorance

Stakeholders

About the author

Context of the presentation

Finance industry specifics

Data representation models

Structure of the presentation

Risk: False feeling of control

Lesson 1: data should provide information

Risk: Internal fraud

Lesson 2: version control needed (A)

Lesson 2: version control needed (B)

Risk: Internal fraud

Lesson 3: visualize gaps

Risk: Manipulation

Lesson 4: formal and unambiguous documentation needed

Risk: surprised by disaster

Lesson 5: disaster plan need (A)

Lesson 5: disaster plan need (B)

Risk: Production “accidents”

Lesson 6: production control (A)

Lesson 6: production control (B)

Risk: Unforeseen commitments

Lesson 7: knowledge representation

Risk: missed business opportunities

Lesson 8: mapping conceptual/physical data model (A)

Lesson 8: mapping conceptual/physical data model (B)

Risk: missed efficiency opportunities

Lesson 9: flexibility of the data models

Risk: functional abends

Lesson 10: recognized impact of changes

Risk: no timely information

Lesson 11: accessible documentation

Risk: fictitious information

Lesson 12: throw light on the matter

Risk: conspiracy for ignorance

Lesson: increased risk awareness

Forms of ignorance

Breaking the conspiracy

The alternative: inertia leads to

Overview of the lessons learned

Criterion 1: the solution provides a data model

Criterion 2: the data model must be generic of nature

Criterion 3: the solution provides a thesaurus: taxonomy and lexicon

Criterion 4: the solution provides a means of managing temporal info

Criterion 5: the solution provides a dashboard function

Criterion 6: the solution provides graphical representations

The journey towards an optimal solution

The ontology

The RDF (S) ontology representation

The OWL format ontology

From data model to software application

Summary of differences traditional / semantic application development

Thanks