Data Warehousing Evaluating Information Systems Success & Failures Flashcards
What is data warehousing?
o Data warehousing – collection of data created to support decision-making
- Subject oriented (not transaction oriented)
- integrated (into a single repository throughout the organization)
- time variant – maintains historical data
- nonvolatile – users cannot change/update the data
Discuss data warehousing initiatives
o data warehousing initiatives
- motivations:
- improve business intelligence by providing decision-makers with access to accurate and timely data
- improve operational efficiency -> centralizing data from different sources, organizations can streamline their reporting processes and eliminate redundancies
- identify inefficiencies in their operations and take corrective action
-considerations :
- clear understanding of their business goals and the data they need to achieve those goals
- invest in the right technology, tools, and personnel to support their data warehousing efforts
- plan in place for ongoing maintenance and management of their data warehouse to ensure that it remains up-to-date and relevant over time
Which development methods can be used in the delivery of data warehouses? Part 1
o Data mart strategy (Kimball)
- Start small, think big
- bottom-up
- Start with one business need/mart and get data from a small number of source systems and then expand
Pro&Con:
+ provides usable data faster, at lower costs and with less financial risk
- silos and integration problems
- worst case scenario – separate data marts are created but never integrated
-> growth should be anticipated and planned
-develop :
- consistent data definitions, dimensions and measures
- scalable architecture
- appropriate access tools
Which development methods can be used in the delivery of data warehouses? Part 2
o Enterprise data warehouse approach (Inmon)
- Top-down approach
- data marts are only created after the warehouses built & populated with that data
- most likely only successfull if companies have to handle large volumes of data, integrate data from multiple sources and provide data governance
- risk of never finishing the project
What is ETL
extraction, transformation, loading
o Takes data from source systems (extraction), prepares it for decision support services (transformation) & places it in the target data base (loading)
The process typically involves several steps, including data profiling, data cleansing, data integration, and data loading
What are data marts?
o specific subpart of the warehouse which stores data for a limited number of subjects
- Independent data mart – built directly from source system (only point solution)
- Dependent data mart – created with data from the warehouse
-> dependent marts are much preferred over independent ones
What are operational data stores?
o Operational data stores (ODS) – consolidate data from multiple sources and provides near real-time, integrated view of volatile, current data -> same process as data warehouse but historical data are not maintained
-> provide integrated data for operational purposes
-> can be used to avoid for ERP implementation
What is data mining?
o process of extracting useful and actionable insights from large datasets
o involves analyzing data from various perspectives and summarizing it into valuable information that can be used to make informed business decisions
What is data mining?
o process of extracting useful and actionable insights from large datasets
o involves analyzing data from various perspectives and summarizing it into valuable information that can be used to make informed business decisions
What are the methodological challenges associated with measuring the effects of IT investments on economic performance?
There are several methodological challenges associated with measuring the effects of IT investments on economic performance, including:
•Causality: It can be difficult to establish a direct causal relationship between IT investments and economic performance, as there may be other factors that influence economic outcomes.
•Time frame: The effects of IT investments may not be immediately apparent, and it can take several years for the benefits of a particular investment to materialize.
•Measurement: Measuring the effects of IT investments on economic performance can be challenging, as it requires the use of appropriate metrics and data sources.
•Context: The effects of IT investments may vary depending on the context in which they are made, such as the industry, company size, and competitive landscape.
Which methods can be used to evaluate information systems success?
User satisfaction surveys: These surveys can provide insights into how well the system meets the needs of its users.
•System usage metrics: These metrics can provide information about how frequently the system is used and how it is being used
•Business process metrics: These metrics can provide insights into how well the system supports business processes and contributes to business objectives.
•Financial metrics: These metrics can provide information about the costs and benefits of the system, including return on investment (ROI) and total cost of ownership (TCO).
•Expert assessments: Expert assessments can provide a more qualitative evaluation of the system, taking into account factors such as usability, reliability, and security
Examples:
1. System Usability Scale (SUS) – assesses the perceived ease of use and user satisfaction with the system through a standardized questionnaire.
2. Technology Acceptance Model (TAM) – considers two factors, perceived usefulness and perceived ease of use, as the key determinants of system acceptance and adoption.
3. DeLone and McLean Information Systems Success Model – six dimensions: system quality, information quality, use, user satisfaction, individual impact, and organizational impact.
4. Balanced Scorecard (BSC) – considers four perspectives, financial, customer, internal business processes, and learning and growth, to evaluate the effectiveness and efficiency of information systems
5. Return on Investment (ROI)
6. Case studies and user feedback
Why do so many IT projects fail? Discuss the (behavioral) reasons for failure of IS projects
o IS projects can fail for various reasons
1. Technical
2. Organizational
3. Behavioral :
- Lack of user involvement
- Resistance to change
- Poor communication
- Unclear requirements
- Over-reliance on technology
- Lack of project management skills
Explain how self-justification theory can be used to explain escalation.
Self-justification theory suggests that individuals may escalate their commitment to a failing project in order to justify the time, effort, and resources they have already invested in it. They may convince themselves that the project will eventually succeed, or that they have the skills or knowledge to turn it around.
Explain how prospect theory can be used to explain escalation
Prospect theory suggests that individuals may be more likely to continue investing in a failing project if they perceive the potential losses as greater than the potential gains of abandoning it. This can lead to an escalation of commitment to the project, as individuals seek to avoid the regret and negative consequences associated with failure.
Explain how agency theory can be used to explain escalation
Agency theory suggests that individuals may escalate their commitment to a failing project if they perceive that doing so is in their own self-interest, even if it goes against the interests of the organization as a whole. For example, a project manager may escalate their commitment to a failing project in order to protect their own reputation or career prospects, rather than acting in the best interests of the organization.
