Podcast
Questions and Answers
Which skill is primarily categorized as a soft skill necessary for Business Analysts?
Which skill is primarily categorized as a soft skill necessary for Business Analysts?
What ethical challenge is highlighted by the use of big data in organizations?
What ethical challenge is highlighted by the use of big data in organizations?
Which of the following is NOT considered a hard skill for a Business Analyst?
Which of the following is NOT considered a hard skill for a Business Analyst?
In clustering, what is primarily used to group data?
In clustering, what is primarily used to group data?
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Which type of data is considered unstructured and poses new ethical challenges?
Which type of data is considered unstructured and poses new ethical challenges?
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Which component of a business process primarily involves the transformation of resources and information?
Which component of a business process primarily involves the transformation of resources and information?
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What is the outcome of a well-designed business process regarding cost for a company?
What is the outcome of a well-designed business process regarding cost for a company?
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In the data analysis process, which step involves defining goals and organizing resources?
In the data analysis process, which step involves defining goals and organizing resources?
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What does the 'data preprocessing' stage primarily involve?
What does the 'data preprocessing' stage primarily involve?
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Which characteristic of well-designed business processes ensures all activities necessary to achieve a business goal are included?
Which characteristic of well-designed business processes ensures all activities necessary to achieve a business goal are included?
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Which type of analytics answers the question 'What is happening?'
Which type of analytics answers the question 'What is happening?'
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How does the use of IT in business processes contribute to increased speed?
How does the use of IT in business processes contribute to increased speed?
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What role do repositories play in a business process?
What role do repositories play in a business process?
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Which analytics type is primarily focused on determining what will happen in the future?
Which analytics type is primarily focused on determining what will happen in the future?
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What is a common tool used in both descriptive and prescriptive analytics?
What is a common tool used in both descriptive and prescriptive analytics?
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What is the primary goal of prescriptive analytics?
What is the primary goal of prescriptive analytics?
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Which enabler would primarily be associated with analysing historical data to answer business-related questions?
Which enabler would primarily be associated with analysing historical data to answer business-related questions?
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Which role does a business analyst play in strategy implementation?
Which role does a business analyst play in strategy implementation?
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What is a key benefit of using optimization in prescriptive analytics?
What is a key benefit of using optimization in prescriptive analytics?
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In the context of business analysis, what is usually the focus of post-implementation reviews?
In the context of business analysis, what is usually the focus of post-implementation reviews?
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Which component is essential for a computer-based information system?
Which component is essential for a computer-based information system?
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Study Notes
Week 12
- The DIKW model shows how data progresses through knowledge.
- Analytics leads to decisions, which are based on wisdom.
- Decisions involve considering consequences and stakeholders' responses.
- Traditional decision-making relies on human judgment, experience, and expertise.
- Decision types span managerial, tactical, and strategic levels.
Decision Types
- Decision-making complexity increases with management level.
- Operational decisions focus on daily operations, involving data input from transaction processing systems.
- Managerial/tactical decisions concern resource allocation and short-term strategies.
- Strategic decisions address long-term organizational issues, needing high-level knowledge.
- Strategic decisions are usually made in groups.
Data-driven vs Judgment-driven
- Manager types demonstrate varying use of analytics and judgment.
- High data-driven types (Type A) heavily use analytics.
- High judgment-driven types (Type D) rely heavily on judgment.
- Intermediate types (Type B and C) use a balanced approach.
Data Ethics (PAPA Framework)
- Privacy, accessibility, accuracy, and property are ethical considerations.
- Privacy concerns personal information, what people can reveal.
- Accessibility questions who has access and under what conditions?
- Accuracy deals with data reliability and accountability.
Extended PAPA Frameworks
- Newer ethical challenges created by data types (big data, etc), different types of analysis, and social media.
- These require more considerations in Extended PAPA Frameworks.
Skills for Business Analysts
- Soft skills include communication, critical thinking, problem-solving, collaboration, adaptability, negotiation, and time management.
- Hard skills include data analysis (Excel, SQL, statistical software), data visualizations (Tableau, Power BI), programming (Python or R), technical writing, and database management.
Week 11 - Clustering
- Clustering groups data based on similarities.
- It lacks a target variable.
- Examples include grouping customers based on demographics and buying behavior.
- Clusters are often difficult to identify and label.
- K-means is a referenced clustering algorithm.
- k values are determined by the analyst.
- The algorithm assigns data points to the nearest cluster center.
- It re-computes centers until the cluster assignment stabilizes.
Association Rule Mining
- Finds relationships (co-occurrences) amongst data variables.
- Often used to analyze market basket data.
- Apriori algorithm frequently identifies subsets that occur together.
- This is crucial for promoting related products.
Data Mining vs Statistics
- Data mining uses a wider range of data than statistics.
- Statistical analysis typically involves testing hypotheses from smaller samples.
- Data mining frequently involves analysis of large amounts of data.
Week 9 - Data Mining
- Data mining is the process of extracting useful information from large datasets.
- It uses statistical and algorithmic methods to discover patterns.
- CRM management, financial analysis, and medical applications are examples.
- It can provide insights to improve operations.
Main Types of Data Mining Methods
- Prediction involves predicting future occurrences.
- Classification involves sorting data into categories.
- Clustering involves grouping similar data points.
- Associations identify co-occurring variables.
- Time-series analysis examines trends over time.
Classification vs Regression
- Classification predicts categorical outputs (e.g., "Yes" or "No").
- Regression predicts numerical outputs (e.g., temperature).
Week 6 - Big Data
- Big data refers to large datasets that traditional methods cannot handle.
- It has four characteristics: Volume, Variety, Velocity, and Veracity.
- Big Data can be used in diverse ways, e.g., advertising, healthcare, and retail.
- Sources of big data include many kinds of digital archives, documents, and media.
Data Warehouses and Data Marts
- Data warehouses store an organisation's entire enterprise data.
- Data marts focus on specific departments or smaller business units.
- Data warehouses include processed or prepared data.
- Data marts utilize data from the warehouse but may not process data as deeply.
- Data lakes are locations that house raw and unprocessed data.
Week 4 & 5 - Databases
- Databases store and organize linked records.
- Tables hold the data.
- Relationships link different tables based on associated data values.
- Tables have primary key fields that identify each record uniquely.
- Foreign key fields in another table link records in different tables.
- Metadata describes the data.
Week 4 & 5 - Data Modelling
- Data modeling involves visually representing database structure.
- Relational databases organize data in tables.
- Entity-relationship diagrams (ERDs) show how entities (types of data or things to be represented) relate to one another.
Week 4 & 5 - ERD Notations
- Rectangular boxes represent entities (e.g., Customers, Employees, or Sales).
- Ellipses represent attributes (e.g., customer name or product quantity or price).
- Diamonds represent relationships.
- Cardinality represents the relationship’s strength (One to Many, etc).
Week 3 - Business Process Management (BPM)
- BPM is a methodical approach to improving or defining business processes.
- It includes four key stages: Modeling the current process; creating system components to support the updated process; implementing the updated business process; and monitoring its effectiveness (using policies and ongoing assessments)
- BPM helps streamline workflows, improve efficiency, reduce costs, and ensure improved customer value.
Week 3 - Varied types of BPM
- Functional processes may involve a single department or function.
- Inter-functional processes involve more than one department in a company.
- Inter-organizational processes require collaboration beyond organizational boundaries.
BPM Notation
- BPM notation tools capture processes.
- Using symbols to indicate different elements of a process helps convey the steps and logic.
Week 2 - Business Processes
- Processes are structured networks of activities, encompassing resources, facilities, and data.
- Information systems are part of business processes.
- Transforming raw information into a useful output is fundamental to any business process.
- Increased use of technology has a direct impact on how business processes operate (e.g., increased automation).
Week 1 - Business Analytics
- Business Analytics (BA) is a set of procedures and methods for solving business problems through data analysis.
- Data analysis processes include planning, data pre-processing, modeling, and follow-up.
- Statistical model results can help provide meaningful, quantitative insights into a business issue.
- BA uses tools like SQL, Python, R Studio, and Tableau.
Types of Decision Modeling
- Deterministic models utilize known data.
- Probabilistic models incorporate uncertainty using probability distributions.
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Description
Explore the intricacies of decision-making processes and the DIKW model in this quiz. Understand how data evolves into knowledge and leads to informed decisions at various managerial levels. Delve into the contrasts between data-driven and judgment-driven decision-making styles.
