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analytics-midterm
analytics
| Question | Answer |
|---|---|
| Competency centered on specialized business knowledge to derive insights for decisions | Operational Analytics |
| The role defined by project management skills and insight derivation | Analytics Manager |
| Competency involving the creation and communication of actionable visual narratives | Data Visualization and Presentation |
| Telling a story with data rather than just showing charts | Data Storytelling |
| Technical competency utilizing scientific methods to discover new knowledge | Research Methods |
| The profession defined by strategies and processes used to uncover new information | Data Scientist |
| The 4-step research model | Hypothesis, Research Methods, Artifact, and Evaluation |
| Technical competency utilizing software/system engineering to develop analytics applications | Data Engineering Principles |
| The role responsible for consolidating all data in one repository | Data Engineer |
| Technical competency applying mathematical and statistical concepts to analysis | Statistical Techniques |
| The role that analyzes raw research data to extract meaningful information | Data Scientist |
| Technical competency implementing machine learning methods and algorithms | "Data Analytics, Methods, and Algorithms" |
| The profession responsible for choosing appropriate algorithms for data insights | Data Scientist |
| Technical competency applying IT, computational thinking, and programming | Computing |
| The characteristic of Workplace Skills regarding proficiency levels | No three-level skill set |
| Workplace skill: Analyzing facts objectively to form a judgment | Critical Thinking |
| Workplace skill: Exchanging information and ideas effectively | Communication |
| Workplace skill: Working with others to achieve a common goal | Collaboration |
| Workplace skill: Thinking of new ways to solve problems with a positive attitude | Creativity and Attitude |
| Workplace skill: Managing time and resources to meet objectives | Planning and Organizing |
| Workplace skill: Understanding core business functions and industry landscape | Business Fundamentals |
| Workplace skill: Prioritizing the needs of the client or end-user | Customer Focus |
| Workplace skill: Proficiency in using software and hardware solutions | Working with Tools and Technology |
| Workplace skill: Continuously and independently learning new skills | Dynamic (Self-) Re-Skilling |
| Workplace skill: Building and maintaining professional relationships | Professional Network |
| Workplace skill: Adhering to moral principles and standards of conduct | Ethics |
| A new resource in the digital world that is mined and refined like natural minerals | Data |
| The process of mining and refining data to extract value | Analytics |
| A complete process of creating, collecting, processing, analyzing, and extracting value from data within an organization | Data Value Chain |
| The process that covers the entire lifecycle of data from its origin to its use in decision-making and insights | Data Value Chain Lifecycle |
| Data created from human activity, biodata, machine logs, and social media | Data Creation / Generation |
| The step in the Data Value Chain that involves extracting and consolidating data into a single repository | Information |
| The processes of data cleaning, categorization, transformation, and aggregation | Becoming Information |
| The pivotal question answered once data is processed into information | "What Happened?" |
| The step in the Data Value Chain involving data analysis to find patterns and trends | Insights |
| The two questions answered by uncovering patterns in the insights phase | "Why did it happen?" and "What is likely to happen next?" |
| The step that involves translating analyzed data into practical recommendations for future actions | Imperatives |
| The question addressed by taking decisive actions informed by analyzed data | "What steps should be taken next?" |
| Policies for ensuring data quality, compliance, and responsible handling | Data Governance |
| The end-to-end oversight of data processes within the data phase | Data Management |
| The discipline of safeguarding data from unauthorized access | Data Security |
| Responsible and ethical handling of data throughout its lifecycle | Data Ethics |
| The discipline of building data systems and managing the flow of information | Data Engineering |
| Managing structured data within the information phase | Data Warehousing |
| The process of designing the underlying structure of data systems | Data Architecture |
| Extracting insights to provide a foundation for business decisions | Business Intelligence (BI) |
| The analytics discipline responsible for summarizing historical data | Descriptive Analytics |
| The extraction of insights and identification of patterns within large datasets | Data Mining |
| The discipline that allows systems to learn and improve from experience | Machine Learning |
| The analytics type responsible for analyzing data to understand why an event occurred | Diagnostic Analytics |
| Identifying future events based on historical data patterns | Predictive Analytics |
| Enhancing efficiency in decision-making within the imperatives phase | Optimization |
| Modeling real-world scenarios to test potential outcomes | Simulation |
| Recommending specific actions derived from descriptive and predictive analysis | Prescriptive Analytics |
| Professionals responsible for developing, enforcing, and maintaining data quality and security | Data Steward |
| The informal title for Data Stewards who keep and maintain data quality | Data Gatekeepers |
| The profession that designs, constructs, tests, and maintains data systems using ETL | Data Engineer |
| The standard technical method: Extract, Transform, Load | ETL |
| The role applying statistical techniques and models to create data-driven predictions | Data Scientist |
| The role responsible for leveraging insights and crafting definitive prescriptions for clients | Functional Analyst |
| The system that provides data-driven options while leaving the final decision to the human user | Decision Support System (DSS) |
| Phases of the Data Value Chain | Creating, Collecting, Processing, Analyzing, Extracting Value |
| Four Stages of the Data Value Chain Flow | Data, Information, Insights, Imperatives |
| Sources of Data Creation | Biodata, Machine Logs, Bank Information, Medical Records, Social Media Post |
| Processes to transform Data into Information | Data Cleaning, Data Categorization, Data Transformation, Data Aggregation |
| Four Analytics Disciplines under "Data" | Data Governance, Data Management, Data Security, Data Ethics |
| Four Analytics Disciplines under "Information" | Data Engineering, Data Warehousing, Data Architecture, Business Intelligence |
| Three Analytics Disciplines under "Insights" | Data Mining, Algorithms, Machine Learning |
| Two Analytics Disciplines under "Imperatives" | Optimization, Simulation |
| Five Main Analytics Professions | Data Steward, Data Engineer, Data Scientist, Functional Analyst, Analytics Manager |
| Responsibilities of a Data Steward | Develop, Enforce, Maintain, Data Security, Data Usage |
| Responsibilities of a Data Engineer | Design, Construct, Test, Maintain, ETL (Extract, Transform, Load) |
| Related Jobs to a Functional Analyst | Research Analyst, Human Resource Analyst, Marketing Analyst, Financial Analyst, Operations Analyst |
| Project Management phases handled by an Analytics Manager | Initiation, Planning, Execution, Monitoring, Closure |
| Three Main Categories of Analytics Competencies | Business and Organization Skills, Technical Skills, Workplace Skills |
| Four Competencies within Business and Organization Skills | Domain Knowledge and Application, Data Management and Governance, Operational Analytics, Data Visualization and Presentation |
| Five Competencies within Technical Skills | Research Methods, Data Engineering Principles, Statistical Techniques, Data Analytics Methods and Algorithms, Computing |
| Three Proficiency Levels for Competencies | Entry Level, Immediate Level, Expert Level |
| Essential Workplace Skills (21st Century Skills) | Critical Thinking, Communication, Collaboration, Creativity and Attitude, Planning and Organizing, Business Fundamentals, Customer Focus, Working with Tools and Technology, Dynamic (Self-) Re-Skilling, Professional Network, Ethics |
| Skills defined for a Functional Analyst (Domain Knowledge) | Domain-Related Knowledge, Insights to effectively contextualize data |
| Skills defined for a Data Steward (Data Management) | Develop and Implement Data Management Strategies, Enforcing Privacy and Data Security, Implement Data Policies and Regulations, Understand Ethical Considerations |
| Skills defined for an Analytics Manager (Operational Analytics) | General Knowledge of Business Analytics, Specialized Knowledge of Business Techniques, Insight Derivation for Decision-Making |
| Skills defined for a Data Engineer (Engineering Principles) | Utilize software and system engineering, Develop data analytics application |
| Skills defined for a Data Scientist (Research Methods) | Utilize scientific and engineering methods, Discover and create new knowledge and insights |
| Skills defined for Computing | Apply information technology and computational thinking, Utilize programming languages, Utilize software and hardware solutions |
Created by:
froguto7