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Analytical - Competency

Definition: Analytical skills are the ability to think critically, be open-minded, and reduce complex issues into more manageable parts. The ability to collect, validate, and analyze data is important for making decisions, forecasting, and developing models. Attention to detail and a willingness to work with quantitative data are needed. Having a good understanding of systems, how to present data, and how to conduct research is useful. Analytical skills also require a certain degree of curiosity.
Job Skills
Analytical
Administrative Skill
Decision Making
Quality
Critical Thinking
Problem Solving
Initiative
Innovation
Goals
Time Management
Change Management
Juggling Multiple Responsibilities
Achievement
Results Oriented
Commitment To Result
Technical
Technology Use/Management
Clarity
Excellence
Objectives
Risk Management
Safety
Regulatory/Compliance
Self-Comments:
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360-Feedback Assessments Measuring Analytical Skills:
Survey 1 (4-point scale; Competency Comments)
Survey 2 (4-point scale; Competency Comments)
Survey 3 (5-point scale; Competency Comments)
Survey 4 (5-point scale; radio buttons)
Survey 5 (4-point scale; words)
Survey 6 (4-point scale; words)
Survey 7 (5-point scale; competency comments; N/A)
Survey 8 (3-point scale; Agree/Disagree words; N/A)
Survey 9 (3-point scale; Strength/Development; N/A)
Survey 10 (Comment boxes only)
Survey 11 (Single rating per competency)
Survey 12 (Slide-bar scale)
Survey 13 (4-point scale; numbers; floating anchors)
Survey 14 (4-point scale; N/A)
Performance Assessments that include Analytical:
Assessment 1 (5-point scale; IDP Comments)
Assessment 2 (3-point scale with Comments)
Assessment 3 (Manager Assessment; 360-Feedback)
Assessment 4 (3-point scale; Rating Limits)
Assessment 5 (3-point scale; Rating Limits)
Assessment 6 (5-point scale with Comments)
Assessment 7 (Comment Boxes Only; IDP)
Assessment 8 (Comment Boxes Only)
Assessment 9 (3-point scale with Letter Grade)
Assessment 10 (360-Feedback; Bonus/Merit Pay)
Assessment 11 (Core Values & Job Competencies)
Assessment 12 (4-point scale; 6 Comment Boxes)
What are Analytical Skills?
Analytical skills include critical thinking using data and logic; performing reductive analysis to make complex systems and problems more manageable; maintaining an open mind to consider different perspectives and viewpoints; and the ability to collect, validate, and analyze data. Analytical skills require attention to detail and fluency with quantitative data to develop forecasts and models of systems. An orientation toward research and the ability to format and visualize data for presentations are important.
Core Components of Analytical Skill
  • Thinking Critically: Evaluating information objectively, questioning assumptions, and applying structured reasoning to develop well-founded conclusions. Performing an impartial assessment of evidence, identifying gaps in data, understanding complex issues, countering biases with logic, and analyzing problems from multiple viewpoints.
  • Reductive Analysis: Breaking down complex problems, processes, or datasets into fundamental components to better understand key elements to dissect large issues into smaller, manageable parts, reducing complexity to core elements, and structuring information for deeper analysis.
  • Open Minded: Embracing diverse perspectives, questioning assumptions, and being flexible in adjusting viewpoints based on credible evidence to explore alternative strategies, consider opposing viewpoints, adapt to new methods when traditional approaches fall short, and remain receptive to innovative solutions.
  • Problem-Solving: Applying structured techniques to identify, prioritize, and implement solutions effectively to define issues, determine root causes, select optimal solutions, and execute resolution strategies through a methodical approach.
  • Validating Data: Ensuring the accuracy, credibility, and relevance of data before it is used for decision-making requires verifying sources, checking consistency across multiple references, validating assumptions, and implementing techniques to ensure precision.
  • Data Analysis: Interpret, process, and extract insights from data to uncover meaningful patterns and trends. This may require applying statistical methods, synthesizing information from various sources, using logical reasoning to determine relevance, and identifying useful correlations.
  • Quantitative Ability: Using numerical data, financial metrics, and mathematical models to assess costs, risks, and performance outcomes to interpret balance sheets, calculate financial ratios, develop budgets, apply algorithms, and measure portfolio value under market conditions.
  • Forecasting and Modeling: Using historical data, trends, and mathematical models to predict future events and guide business planning to identify patterns, model financial scenarios, project revenues and expenses, recognize relationships in complex systems, and make recommendations based on anticipated outcomes.
  • Attention to Detail: Carefully examine specifics, detect discrepancies, and ensure precision in analysis and execution to perform auditing of financial transactions, verifying product quality, maintaining rigorous documentation, and identifying hidden inconsistencies in reports and processes.
Why Analytical Skills important in the workplace?
Analytical Skills are important for several reasons:
  • Critical Thinking: Uses data and logic to help ensure desireable outcomes to situations.
  • Reductive Analysis: Decomposes and deconstructs complex tasks and systems into smaller components.
  • Open Minded: Following the data and facts may require flexibility in adopting new positions or viewpoints.
  • Data Collection: Collecting precise information that is up-to-date.
  • Data Validation: Ensuring information is accurate, valid, and reliable.
  • Data Analysis: Interpret and analyze data.
What are key aspects of Analytical Skills?
  • Critical Thinking
  • Reductive Analysis
  • Keeping an open mind
  • Data Collection
  • Data Validation
  • Data Analysis
  • Quantitative Ability
  • Forecasting/Modeling
  • Attention to Detail
  • Systems Thinking
  • Data Presentation
  • Curiosity
How can I improve my Analytical Skills?
  • Continuous Learning: Enroll in courses or workshops that focus on data analysis, critical thinking, and problem-solving. There are many online platforms like Coursera, LinkedIn Learning, and Udemy that offer courses in these areas.
  • Practice with Real Data: Use actual business data to practice your analytical skills. Analyze sales data, customer feedback, or financial reports to gain insights and make data-driven decisions.
  • Seek Feedback: Share your analyses with colleagues or mentors and seek constructive feedback. This can help you identify areas for improvement and gain new perspectives.
  • Stay Curious: Cultivate a habit of asking questions and seeking out new information. Curiosity drives deeper understanding and innovation.
  • Use Analytical Tools: Familiarize yourself with analytical tools and software like Excel, Tableau, or SQL. These tools can help you manage and analyze data more efficiently.
  • Collaborate with Others: Work with others on projects that require analytical thinking. Collaborating can help you see how different people approach and solve problems.
  • Work on your Attention to Detail: Pay attention to the small details in data and reports. This can help you catch errors and identify important trends.
What questions could be included on a 360-degree survey that measure analytical skills?
When creating a questionnaire to measure analytical skills, be sure to include items that measure the critical thinking, reductive analysis, keeping an open mind, data collection, data validation, and data analysis. When creating a 360-degree or other performance assessment, try to select one or two items from each group.

Questionnaire Items to Include



Critical Thinking
Critical Thinking focuses on evaluating information objectively, questioning assumptions, and applying structured reasoning to develop well-founded conclusions. This dimension highlights impartial assessment of evidence, identifying gaps in data, understanding complex issues, countering biases with logic, and analyzing problems from multiple viewpoints. It prioritizes thoughtful evaluation and intellectual discipline, ensuring that decisions are based on rigorous analysis rather than intuition or personal biases.


Reductive Analysis
Reductive Analysis focuses on breaking down complex problems, processes, or datasets into fundamental components to better understand key elements. This dimension highlights dissecting large issues into smaller, manageable parts, reducing complexity to core elements, and structuring information for deeper analysis. It prioritizes structured breakdown and simplification, ensuring that analytical tasks are divided into digestible segments for enhanced comprehension and resolution.


Open Minded
Open Minded focuses on embracing diverse perspectives, questioning assumptions, and being flexible in adjusting viewpoints based on credible evidence. This dimension highlights exploring alternative strategies, considering opposing viewpoints, adapting to new methods when traditional approaches fall short, and remaining receptive to innovative solutions. It prioritizes intellectual flexibility and adaptability, ensuring individuals are willing to revise their understanding based on new insights.


Problem-Solving
Problem-Solving emphasizes applying structured techniques to identify, prioritize, and implement solutions effectively. This dimension centers on defining issues, determining root causes, selecting optimal solutions, and executing resolution strategies through a methodical approach. It prioritizes practical application and solution-driven execution, ensuring that problems are not only analyzed but also efficiently addressed.


Data Collection
Data Collection emphasizes gathering, measuring, and ensuring the accuracy of relevant information before analysis begins. This dimension centers on obtaining data from various sources, maintaining precision in measurements, recognizing missing information, and verifying relevance. It prioritizes information acquisition and validation, ensuring that analytical decisions are based on robust, well-sourced data.


Data Validation
Data Validation emphasizes ensuring the accuracy, credibility, and relevance of data before it is used for decision-making. This dimension highlights verifying sources, checking consistency across multiple references, validating assumptions, and implementing techniques to ensure precision. It prioritizes quality control and reliability, making sure that conclusions are based on trustworthy and error-free information.


Data Analysis
Data Analysis focuses on interpreting, processing, and extracting insights from data to uncover meaningful patterns and trends. This dimension centers on applying statistical methods, synthesizing information from various sources, using logical reasoning to determine relevance, and identifying useful correlations. It prioritizes interpretation and strategic insights, ensuring that data is leveraged effectively to inform decisions and optimize performance.


Quantitative Ability
Quantitative Ability focuses on using numerical data, financial metrics, and mathematical models to assess costs, risks, and performance outcomes. This dimension highlights interpreting balance sheets, calculating financial ratios, developing budgets, applying algorithms, and measuring portfolio value under market conditions. It prioritizes data-driven precision and numerical analysis, ensuring organizations make decisions based on measurable financial factors.


Decision Making
Decision Making focuses on assessing available information, weighing risks and costs, and selecting the most effective course of action based on solid evidence and reasoning. This dimension highlights optimizing limited resources, balancing trade-offs, evaluating different sources of information, and making rational judgments. It prioritizes strategic choice and optimization, ensuring decisions align with efficiency, feasibility, and business objectives.


Forecasting/Modeling
Forecasting/Modeling emphasizes using historical data, trends, and mathematical models to predict future events and guide business planning. This dimension centers on identifying patterns, modeling financial scenarios, projecting revenues and expenses, recognizing relationships in complex systems, and making recommendations based on anticipated outcomes. It prioritizes predictive analytics and trend recognition, ensuring organizations make informed decisions with forward-looking insights.


Attention to Detail
Attention to Detail emphasizes carefully examining specifics, detecting discrepancies, and ensuring precision in analysis and execution. This dimension centers on auditing financial transactions, verifying product quality, maintaining rigorous documentation, and identifying hidden inconsistencies in reports and processes. It prioritizes accuracy and meticulous scrutiny, ensuring that minor errors do not compromise outcomes and that processes are carried out flawlessly.


Systems Thinking
Systems Thinking emphasizes understanding interdependencies between different business components and recognizing how changes in one area can affect the broader system. This dimension centers on diagnosing impacts across value chains, integrating customer feedback with market trends, identifying systemic causes beyond immediate effects, and ensuring holistic decision-making. It prioritizes big-picture analysis and interconnected reasoning, ensuring organizations consider broader implications when making strategic choices.


Data Presentation
Data Presentation focuses on structuring, visualizing, and conveying data effectively to ensure clarity and comprehension. This dimension highlights creating charts, graphs, and reports, organizing information for easy comparisons, designing straightforward presentations, and making data-driven arguments persuasive. It prioritizes communication and accessibility, ensuring that insights are presented clearly and compellingly to various audiences.


Curiosity and Creativity
Curiosity and Creativity focuses on asking insightful questions, exploring alternative perspectives, and developing innovative ways to interpret data and identify problems. This dimension highlights seeking new knowledge, visualizing data in unconventional ways, considering multiple viewpoints, and using logic to uncover potential issues. It prioritizes exploration and imaginative thinking, ensuring individuals approach problems with openness and ingenuity.


Research Oriented
Research Oriented emphasizes applying structured scientific methodologies to validate hypotheses, analyze experimental data, and derive accurate conclusions based on empirical evidence. This dimension centers on testing theories, utilizing research principles, proving ideas through data-driven methods, and ensuring findings are based on verifiable results. It prioritizes rigorous investigation and empirical validation, ensuring that conclusions are substantiated by systematic research rather than intuition or speculation.
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