Summary Advanced Data Management
Lecture 1
Data management
Definition: Data are the facts (measurements or statistics) used as
a basis for reasoning, inference, or analysis.
o Reasoning, inference, or analysis -> Value Creation
Definition: 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.
Understand the Domain (DM Perspective)
Modelling the domain allows us to understand the role of technology and its data
requirements.
What is a model?
o A model is a formal representation of the target domain, using constructs and
construction rules.
We build models to describe a domain in unambiguous ways
o Analysis of existing domain
o Planning or designing a future state
In order to reason about the phenomena in that domain and communicate between the
stakeholders.
o Also used in building a business case (communication)
Using models we can explore, observe, analyze, explain and predict phenomena in the
domain and build (or plan/design) artifacts that operate in the domain.
Uses of Models in DM
Understand the Business in order to Generate Value
Data Governance
Integration and Metadata Management
Improving Data Security and Quality
A More Abstract View of Information Systems
Information Systems are models or representations of real-world phenomena and applications
(Representation Theory).
, Information Systems are comprised of three structures:
o Deep Structure: meanings and facts about real world phenomena in form of data and
business rules.
DM and business process management.
o Surface Structure: features such as user interfaces that allow users to engage with the
deep structure.
DM and IT management / System Design.
o Physical Structure: the infrastructure (e.g., hardware and network) that enable the
implementation of surface and deep structures.
DM and (physical) architecture.
Application of Models in DM
Business Case
Before committing resources, businesses need to have a (somewhat) clear picture of what the
expected costs and benefits are
o Businesses need to understand the rationale for undertaking an initiative
Conducting such analysis is challenging as a large part of Data Management fall under the
category of complex systems where an ultimate solution cannot be analyzed to solve a
problem.
Conceptual models allow us to simplify and formalize the complexity
Business case – systems thinking
To tackle the complexity, systems thinking conceptualizes the problem as:
o System made of interacting components;
o Interactions happen within an environment;
o The systems fulfil a goal.
Two methods to analyze complex systems:
o Soft Systems Methodology including Rich Pictures where the interactions and roles
are modelled.
o System Dynamics where cause and effect relationships among various variables are
studied (using causal loops).
, Example of a rich picture and a casual loop
Another incentive for business case
Successful: On time, on budget, with all features and functions.
Challenged: Project completed, but either over time, over budget, or with fewer features than
originally specified.
Failed Project: cancelled at some point during development
Reasons for Project Failures
Information Systems Research
Information Systems research domain is the confluence of people, organizations, and
technology (Hevner et al. 2004).
Lecture 1
Data management
Definition: Data are the facts (measurements or statistics) used as
a basis for reasoning, inference, or analysis.
o Reasoning, inference, or analysis -> Value Creation
Definition: 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.
Understand the Domain (DM Perspective)
Modelling the domain allows us to understand the role of technology and its data
requirements.
What is a model?
o A model is a formal representation of the target domain, using constructs and
construction rules.
We build models to describe a domain in unambiguous ways
o Analysis of existing domain
o Planning or designing a future state
In order to reason about the phenomena in that domain and communicate between the
stakeholders.
o Also used in building a business case (communication)
Using models we can explore, observe, analyze, explain and predict phenomena in the
domain and build (or plan/design) artifacts that operate in the domain.
Uses of Models in DM
Understand the Business in order to Generate Value
Data Governance
Integration and Metadata Management
Improving Data Security and Quality
A More Abstract View of Information Systems
Information Systems are models or representations of real-world phenomena and applications
(Representation Theory).
, Information Systems are comprised of three structures:
o Deep Structure: meanings and facts about real world phenomena in form of data and
business rules.
DM and business process management.
o Surface Structure: features such as user interfaces that allow users to engage with the
deep structure.
DM and IT management / System Design.
o Physical Structure: the infrastructure (e.g., hardware and network) that enable the
implementation of surface and deep structures.
DM and (physical) architecture.
Application of Models in DM
Business Case
Before committing resources, businesses need to have a (somewhat) clear picture of what the
expected costs and benefits are
o Businesses need to understand the rationale for undertaking an initiative
Conducting such analysis is challenging as a large part of Data Management fall under the
category of complex systems where an ultimate solution cannot be analyzed to solve a
problem.
Conceptual models allow us to simplify and formalize the complexity
Business case – systems thinking
To tackle the complexity, systems thinking conceptualizes the problem as:
o System made of interacting components;
o Interactions happen within an environment;
o The systems fulfil a goal.
Two methods to analyze complex systems:
o Soft Systems Methodology including Rich Pictures where the interactions and roles
are modelled.
o System Dynamics where cause and effect relationships among various variables are
studied (using causal loops).
, Example of a rich picture and a casual loop
Another incentive for business case
Successful: On time, on budget, with all features and functions.
Challenged: Project completed, but either over time, over budget, or with fewer features than
originally specified.
Failed Project: cancelled at some point during development
Reasons for Project Failures
Information Systems Research
Information Systems research domain is the confluence of people, organizations, and
technology (Hevner et al. 2004).
