Advanced Data Management
Content
Lecture 1: Introduction Data Representing Real World (26-10).............................................................2
Tutorial 1: Case study Modelling Business Data (27-10).........................................................................7
Readings week 1:................................................................................................................................8
Lecture 2: Operational and Strategic Values of Data (02-11)...............................................................10
Readings week 2:..............................................................................................................................16
Tutorial 2 & 3: Tableau Part 1 (03-11) & Tableau Part 2 (12-11)..........................................................18
Lecture 3: Data Structure and User Generated Data Quality (09-11)...................................................19
Readings week 3:..............................................................................................................................24
Lecture 4: Metadata and Ontology Engineering (16-11)......................................................................28
Readings week 4:..............................................................................................................................33
Tutorial 4: Case Study Protégé (17-11).................................................................................................34
Lecture 5: Governance (Marinka Voorhout – Philips) (23-11)..............................................................36
Readings week 5:..............................................................................................................................37
Lecture 6: Enterprise Systems & Enterprise Resource Planning (31-11)...............................................40
Readings week 6:..............................................................................................................................46
Lecture 7: Data Management Roadmap and Conclusions (07-12)........................................................49
Optional Readings: Book Bas van Gils...............................................................................................55
,Lecture 1: Introduction Data Representing Real World
(26-10)
What is data?: Facts (measurements or statistics) used as a basis for reasoning, inference or analysis.
Reasoning, inference, or analysis à value creation. So we reason about the data, try to make
inferences on the data and try to analyze the data in order to learn something, in order to create
some value which is the end goal.
Data is the rawest form, on the bottom of the pyramid.
These are pieces of information (statistics or
measurements) in the rawest form.
Information is a level of abstraction higher, data is
given some context, additional details. à abstract
information from data.
Level of abstraction on top of information; knowledge.
à Abstract knowledge from information.
Then wisdom, another level of abstraction higher, the
top of the pyramid. Making important decisions from
managerial perspective. Extracting wisdom and
incorporating it into decision making is a human job.
Data management: DMBOK (Data Management Body of Knowledge): “Data Management is the
development, execution, and supervision of plans, policies, programs and practices that deliver,
control, protect and enhance the value of data and information throughout their lifecycles”.
Advanced Data Management: Some of the things that data managers do is develop, execute and
supervise this as a business process. The result of their development, executions and supervision as
perhaps some information or architecture blueprints that we have. These enterprise architecture
blueprints are going to be used in another process to deliver, control, protect and enhance. The final
output is some value from the data à increasing revenue decreasing the costs.
Understand the application/ business (information modelling).
Evaluate and interpret the data (ontology engineering):
Apply tools to generate value (business intelligence and analytics):
Comply and keep up with an everchanging landscape (governance, risk assessment, IT
management)
2
, Understand the domain (DM perspective): Modelling the domain allows to understand the role of
technology and its data requirements.
Model: a formal representation of the target domain, using constructs and construction rules. We
build models to describe a domain in unambiguous ways:
Analysis of existing domain à current situation; as-is / statis quo
Planning or designing a future state à to-be
In order to reason about the phenomena in that domain (understand them) and communicate
between the stakeholders (people involved; business owner, client, analyst, consultant, developer,
etc.) à tool for communication between parties involved.
Also used in building a business case (communication)
Using models we can explore, observe, analyze, explain and predict phenomena in the domain à
existing domain. And build (or plan/design) artifacts that operate in the domain à future state.
Uses of Models in DM:
Understand the Business in order to Generate Value (Weeks 2 and 3)
Data Governance (Week 4)
Integration and Metadata Management (Week 6)
Improving Data Security and Quality (Weeks 4 and 5)
A More Abstract View of Information Systems: Information Systems are models or representations
of real-world phenomena and applications (Representation Theory).
Representation theory (Burton-Jones et al., 2017): the basic assumption of the representation theory
is that our information systems are also models/representations of the real world (e.g. learning
management systems or accounting systems).
They are from an abstract view comprised of three structures:
1. Deep Structure: meanings and facts about real world phenomena in form of data and
business rules.
DM and business process management. à plans, policies, programs, practises
(enterprise architecture blueprints)
2. Surface Structure: features such as user interfaces that allow users to engage with the deep
structure.
DM and IT management / System Design. à use interface or surface structure to
engage with the rules/ meaning of data what we have
3. Physical Structure: the infrastructure (e.g., hardware and network) that enable the
implementation of surface and deep structures.
DM and (physical) architecture. à engage in designs like coding, programming,
network, design, internet infrastructure.
3
Content
Lecture 1: Introduction Data Representing Real World (26-10).............................................................2
Tutorial 1: Case study Modelling Business Data (27-10).........................................................................7
Readings week 1:................................................................................................................................8
Lecture 2: Operational and Strategic Values of Data (02-11)...............................................................10
Readings week 2:..............................................................................................................................16
Tutorial 2 & 3: Tableau Part 1 (03-11) & Tableau Part 2 (12-11)..........................................................18
Lecture 3: Data Structure and User Generated Data Quality (09-11)...................................................19
Readings week 3:..............................................................................................................................24
Lecture 4: Metadata and Ontology Engineering (16-11)......................................................................28
Readings week 4:..............................................................................................................................33
Tutorial 4: Case Study Protégé (17-11).................................................................................................34
Lecture 5: Governance (Marinka Voorhout – Philips) (23-11)..............................................................36
Readings week 5:..............................................................................................................................37
Lecture 6: Enterprise Systems & Enterprise Resource Planning (31-11)...............................................40
Readings week 6:..............................................................................................................................46
Lecture 7: Data Management Roadmap and Conclusions (07-12)........................................................49
Optional Readings: Book Bas van Gils...............................................................................................55
,Lecture 1: Introduction Data Representing Real World
(26-10)
What is data?: Facts (measurements or statistics) used as a basis for reasoning, inference or analysis.
Reasoning, inference, or analysis à value creation. So we reason about the data, try to make
inferences on the data and try to analyze the data in order to learn something, in order to create
some value which is the end goal.
Data is the rawest form, on the bottom of the pyramid.
These are pieces of information (statistics or
measurements) in the rawest form.
Information is a level of abstraction higher, data is
given some context, additional details. à abstract
information from data.
Level of abstraction on top of information; knowledge.
à Abstract knowledge from information.
Then wisdom, another level of abstraction higher, the
top of the pyramid. Making important decisions from
managerial perspective. Extracting wisdom and
incorporating it into decision making is a human job.
Data management: DMBOK (Data Management Body of Knowledge): “Data Management is the
development, execution, and supervision of plans, policies, programs and practices that deliver,
control, protect and enhance the value of data and information throughout their lifecycles”.
Advanced Data Management: Some of the things that data managers do is develop, execute and
supervise this as a business process. The result of their development, executions and supervision as
perhaps some information or architecture blueprints that we have. These enterprise architecture
blueprints are going to be used in another process to deliver, control, protect and enhance. The final
output is some value from the data à increasing revenue decreasing the costs.
Understand the application/ business (information modelling).
Evaluate and interpret the data (ontology engineering):
Apply tools to generate value (business intelligence and analytics):
Comply and keep up with an everchanging landscape (governance, risk assessment, IT
management)
2
, Understand the domain (DM perspective): Modelling the domain allows to understand the role of
technology and its data requirements.
Model: a formal representation of the target domain, using constructs and construction rules. We
build models to describe a domain in unambiguous ways:
Analysis of existing domain à current situation; as-is / statis quo
Planning or designing a future state à to-be
In order to reason about the phenomena in that domain (understand them) and communicate
between the stakeholders (people involved; business owner, client, analyst, consultant, developer,
etc.) à tool for communication between parties involved.
Also used in building a business case (communication)
Using models we can explore, observe, analyze, explain and predict phenomena in the domain à
existing domain. And build (or plan/design) artifacts that operate in the domain à future state.
Uses of Models in DM:
Understand the Business in order to Generate Value (Weeks 2 and 3)
Data Governance (Week 4)
Integration and Metadata Management (Week 6)
Improving Data Security and Quality (Weeks 4 and 5)
A More Abstract View of Information Systems: Information Systems are models or representations
of real-world phenomena and applications (Representation Theory).
Representation theory (Burton-Jones et al., 2017): the basic assumption of the representation theory
is that our information systems are also models/representations of the real world (e.g. learning
management systems or accounting systems).
They are from an abstract view comprised of three structures:
1. Deep Structure: meanings and facts about real world phenomena in form of data and
business rules.
DM and business process management. à plans, policies, programs, practises
(enterprise architecture blueprints)
2. Surface Structure: features such as user interfaces that allow users to engage with the deep
structure.
DM and IT management / System Design. à use interface or surface structure to
engage with the rules/ meaning of data what we have
3. Physical Structure: the infrastructure (e.g., hardware and network) that enable the
implementation of surface and deep structures.
DM and (physical) architecture. à engage in designs like coding, programming,
network, design, internet infrastructure.
3
