Podcast
Questions and Answers
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?
Which of the following is NOT considered a hard skill for a Business Analyst?
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?
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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?
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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?
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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?'
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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?
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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?
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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?
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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?
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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?
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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.