Data Reporting
Data Reporting refers to the ability to collect, interpret and present meaningful insights from data. It involves knowing how to run relevant reports, understanding data analytics, displaying data visually for ease of comprehension, and communicating findings effectively to inform business decisions.
Level 1: Emerging
At an emerging level, you are beginning to understand the basics of data reporting. You can create simple reports and present data in a clear, yet straightforward manner.
Level 2: Proficient
At a proficient level you are able to create accurate and informative reports using data visualization tools. You can analyze data trends and present findings in a clear and concise manner.
Level 3: Advanced
At an advanced level, you are able to produce sophisticated, insightful data reports, utilizing advanced analytical tools and techniques to present complex information clearly and effectively to diverse stakeholders.
Advanced Data Analytics Techniques
Advanced Data Analytics Techniques refer to the superior proficiency in employing complex data analysis tools. This includes statistical analyzes, predictive modeling, data mining, algorithm development, and big data processing to derive meaningful insights, facilitate strategic decisions, and foster data-driven innovation within an organization.
Level 1: Emerging
At an emerging level, you are beginning to comprehend and apply advanced data analytics techniques. You are capable of simple data interpretation and limited analysis, with room for growth and development.
Level 2: Proficient
At a proficient level you are skilled in using advanced data analytics techniques such as regression analysis, machine learning algorithms, and data visualization to extract insights and make data-driven decisions within your role.
Level 3: Advanced
At an advanced level you are proficient in utilizing complex data analytics techniques to extract valuable insights, make informed decisions, and drive innovative solutions within the organization.
Data Scalability Solutions
Data Scalability Solutions pertain to the ability to design, implement, and manage data systems that can efficiently adapt and expand in response to increased data volume, transaction speed, and storage requirements, ensuring optimal performance and strategic advantage as organizational needs and complexity grow.
Level 1: Emerging
At an emerging level, you are beginning to understand how to scale data solutions, with a basic familiarity of concepts like data volume, velocity, and variety in a business context.
Level 2: Proficient
At a proficient level you are able to effectively assess and implement data scalability solutions to ensure efficient storage, processing, and retrieval of data within the organization.
Level 3: Advanced
At an advanced level you are proficient in implementing complex data scalability solutions that can efficiently handle large volumes of data while ensuring optimal performance and cost-effectiveness within the organization.
Artificial Intelligence Fundamentals
Artificial Intelligence Fundamentals refer to the elementary understanding and application of AI concepts, including machine learning, neural networks, natural language processing. It encompasses the ability to comprehend AI architecture, decision-making algorithms, and the ethical implications related to AI utilization in business contexts.
Level 1: Emerging
At an emerging level, you are starting to understand fundamental AI concepts, showing awareness of key methodologies, and beginning to apply basic AI and machine learning principles to tasks.
Level 2: Proficient
At a proficient level you are able to demonstrate an understanding of basic concepts and applications of artificial intelligence, including machine learning algorithms and natural language processing techniques.
Level 3: Advanced
At an advanced level you are proficient in understanding complex AI algorithms, can develop advanced AI models, and can effectively apply AI techniques to solve real-world problems within the organization.
Machine Learning Algorithms
Machine Learning Algorithms are computational methods used in data analysis to automate predictive model generation. They allow machines to learn from data, identify patterns and make decisions with minimal human intervention. Proficiency involves understanding, selecting and deploying these algorithms to solve specific business challenges.
Level 1: Emerging
At an emerging level, you are familiar with basic concepts of Machine Learning Algorithms. You understand common algorithms, can interpret outputs and apply them in simple, guided contexts.
Level 2: Proficient
At a proficient level, you are able to implement and customize machine learning algorithms to solve complex problems, demonstrating a solid understanding of their principles, functionality, and limitations.
Level 3: Advanced
At an advanced level, you are able to independently design, implement, and optimize complex machine learning algorithms to solve real-world problems, demonstrating expertise in advanced model selection and hyperparameter tuning.
Data Science Project Management
Data Science Project Management is the proficiency to coordinate and guide data-related projects, encompassing skills in establishing project goals, determining data necessities, coordinating data science teams, monitoring project progress, managing risks, and ensuring the effective usage and interpretation of data to reach the desired objective.
Level 1: Emerging
At an emerging level, you are capable of understanding key data science project elements, such as scope and resources, but might require supervision when executing project plans.
Level 2: Proficient
At a proficient level you are able to effectively manage and lead data science projects, ensuring timely completion, resource allocation, risk management, and stakeholder communication. You have a solid understanding of project management principles.
Level 3: Advanced
At an advanced level you are proficient in leading and overseeing complex data science projects, managing resources effectively, mitigating risks, and delivering high-quality results within specified timeframes.
Data Auditing
Data Auditing is the capability to systematically review, inspect and assess the accuracy, completeness, and compliance of an organization's data assets. It involves identifying irregularities, errors and potential areas of improvement to ensure data integrity, reliability and adherence to governance standards.
Level 1: Emerging
At an emerging level, you are beginning to understand the fundamentals of data auditing. You can identify basic data irregularities but require help and support in rectifying these issues.
Level 2: Proficient
At a proficient level you are skilled at conducting thorough data audits to ensure data accuracy, integrity, and security within the organization. You can identify and address potential data quality issues effectively.
Level 3: Advanced
At an advanced level you are able to independently conduct comprehensive and rigorous audits of data, ensuring accuracy, integrity, and compliance with organizational policies and industry regulations.
Data Warehousing
Data warehousing refers to the process of collecting, managing, and storing an organization's digital data from various sources. It aids in business analysis, decision-making, and reporting by providing a consolidated, consistent data view. Proficiency involves understanding warehousing concepts, tools, and managing data flow effectively.
Level 1: Emerging
At an emerging level, you are beginning to understand the fundamentals of data warehousing. You're familiarizing yourself with the concepts of data storage, organization and retrieval for analytical use.
Level 2: Proficient
At a proficient level you are able to design, implement, and manage data warehouses effectively. You can optimize data retrieval processes, ensure data quality, and troubleshoot any issues that may arise.
Level 3: Advanced
At an advanced level, you are proficient in designing, implementing, and managing complex data warehousing solutions. You possess in-depth knowledge of data modeling, ETL processes, and data integration techniques.
Real-time Data Processing
Real-time Data Processing refers to the ability to continuously ingest, analyze and interpret live data, enabling instant decision-making based on current information. This capability involves understanding real-time systems, managing streaming data, and applying algorithms for prompt data manipulation and interpretation.
Level 1: Emerging
At an emerging level, you are beginning to understand and apply techniques for real-time data processing, capable of basic tasks with guidance but not yet fully self-sufficient.
Level 2: Proficient
At a proficient level you are able to effectively process real-time data, making timely and informed decisions based on the information obtained. You possess a strong understanding of data processing techniques.
Level 3: Advanced
At an advanced level, you are a master at real-time data processing, effortlessly managing and analyzing data streams in real-time to make informed decisions quickly and effectively within the organization.
Cloud Data Management
Cloud Data Management refers to the capability to administer, control, and ensure the accuracy, security, and accessibility of data stored in cloud-based systems. It encompasses data integration, warehousing, protection, retrieval, and effective interpretation for improved decision making within an organization.
Level 1: Emerging
At an emerging level, you are beginning to understand cloud data management. You can navigate cloud storage, implement backup processes, and use basic cloud data security measures with guidance.
Level 2: Proficient
At a proficient level you are able to effectively manage and optimize cloud data storage, retrieval, and security within the organization, ensuring data integrity and compliance with regulations.
Level 3: Advanced
At an advanced level, you are adept at designing and implementing complex cloud-based data management solutions that optimize efficiency, security, and scalability within the organization.
Coding for Data Analysis
Coding for Data Analysis refers to the proficiency in utilizing programming languages, such as Python or R, to manipulate, analyze, and interpret data. It encompasses skills in data cleaning, statistical analysis, data visualization, and implementing algorithms to solve data-related problems, aiding in insightful decision-making.
Level 1: Emerging
At an emerging level, you are beginning to understand and apply basic coding principles to analyze data. You can perform simple data manipulations and interpret basic statistical summaries.
Level 2: Proficient
At a proficient level you are able to write complex code for data analysis tasks without assistance. You can effectively manipulate data, create models, and automate processes using coding languages like Python or R.
Level 3: Advanced
At an advanced level, you are proficient in using various coding languages for complex data analysis tasks, such as manipulating and visualizing data, implementing machine learning algorithms, and optimizing code efficiency.
Data Storytelling
Data Storytelling is the capability to effectively communicate pertinent information derived from data analysis. It involves synthesizing data insights into a compelling, understandable narrative, utilizing visual and contextual aids, to enable informed decision-making and foster data-driven culture within an organization.
Level 1: Emerging
At an emerging level, you are beginning to understand and convey meaningful narratives derived from data, though guidance may be necessary when interpreting complex data sets and crafting stories.
Level 2: Proficient
At a proficient level you are able to effectively communicate insights from data using storytelling techniques, making complex information accessible and engaging for diverse audiences within the organization.
Level 3: Advanced
At an advanced level, you are adept at crafting compelling narratives using data to influence decision-making, effectively communicating complex information through visuals, storytelling techniques, and actionable insights.
Data Ethics
Data Ethics refers to the responsible and principled application of data handling, acknowledging and adhering to standards of appropriateness, legality, privacy, and fairness in data collection, processing, storage, and sharing practices within an organization.
Level 1: Emerging
At an emerging level you are starting to understand data ethics, displaying a growing awareness of confidentiality, privacy matters and acknowledging the responsible use of data assets.
Level 2: Proficient
At a proficient level you are able to identify and evaluate ethical implications of data practices, making informed decisions to ensure data usage aligns with ethical standards and regulations.
Level 3: Advanced
At an advanced level you are consistently applying ethical principles to complex data scenarios, including addressing privacy concerns, ensuring transparency, and advocating for responsible data practices within the organization.
Data Security
Data Security capability refers to the ability to implement measures and protocols that safeguard an organization's data from unauthorized access, breaches, and theft. It encompasses understanding of encryption, access control, data backup, and appropriate handling of sensitive data according to compliance regulations.
Level 1: Emerging
At an emerging level, you are starting to comprehend basic principles of data security, gaining initial knowledge about safeguarding data and protecting it from unauthorized access or breaches.
Level 2: Proficient
At a proficient level you are able to effectively implement and maintain data security measures, ensuring confidentiality, integrity, and availability of data while adhering to organizational policies and procedures.
Level 3: Advanced
At an advanced level, you are proficient in implementing advanced data security measures, analyzing potential threats, designing and managing secure data environments, and ensuring compliance with data security regulations.
Data Quality Assessment
Data Quality Assessment is the capability to evaluate, analyze, and ensure the accuracy, consistency, and reliability of data. It involves identifying anomalies, errors, and inconsistencies in datasets, while understanding the implications of data quality on business outcomes and decision-making processes.
Level 1: Emerging
At an emerging level, you are beginning to understand and identify data quality issues. You can evaluate basic data integrity and use simple tools to detect and repair data errors.
Level 2: Proficient
At a proficient level you are able to independently assess data quality to identify inconsistencies, errors, and outliers using appropriate tools and methodologies, contributing to data accuracy and reliability within the organization.
Level 3: Advanced
At an advanced level, you are able to independently design and implement complex data quality assessment strategies, utilizing advanced statistical techniques and tools to ensure accuracy and reliability of data.
Data Integration
Data Integration is the capability of combining and managing data from diverse sources to provide unified, consistent and valuable insights. It involves synchronization, mapping, transformation and delivery processes, ensuring the consistency and readability of data, enhancing accuracy in decision-making.
Level 1: Emerging
At an emerging level, you are beginning to understand and apply data integration techniques. You're grasping the concept of deriving insights from combined datasets yet require more practice.
Level 2: Proficient
At a proficient level you are able to efficiently integrate data from multiple sources, ensuring accuracy and consistency. You can confidently use tools and techniques to merge, clean, and transform data.
Level 3: Advanced
At an advanced level, you can seamlessly integrate and transform data from multiple sources using advanced techniques such as data blending, ETL processes, and automation, ensuring optimal data accuracy and consistency.
Statistical Methods
Statistical Methods refers to the employee's ability to apply quantitative techniques to interpret data, forecast trends, and detect patterns. This includes mastering data collection, data analysis, hypothesis testing, regression analysis, and more, thereby assisting in sound, data-driven decision making within the organization.
Level 1: Emerging
At an emerging level, you are gaining familiarity with statistical methodologies and beginning to apply basic statistical analysis techniques using relevant software in your data oriented tasks.
Level 2: Proficient
At a proficient level you are able to apply various statistical methods to analyze and interpret data accurately, making informed decisions and recommendations based on statistical findings within your organization.
Level 3: Advanced
At an advanced level you are able to apply complex statistical methods to analyze and interpret data, make data-driven decisions, and communicate findings effectively to stakeholders within the organization.
Data Interpretation
Data Interpretation refers to the ability to analyze and deduce meaningful insights from presented data. It includes understanding patterns, trends, discrepancies, and drawing conclusions to influence strategic decisions and actions. This capability requires strong analytical skills, critical thinking, and comprehension of data visualization techniques.
Level 1: Emerging
At an emerging level, you are beginning to learn and understand how to read, analyze and draw basic conclusions from numerical and qualitative data sources and reports.
Level 2: Proficient
At a proficient level you are able to interpret complex data sets, identify patterns, trends, and relationships, and present findings in a clear and understandable manner to support decision-making within the organization.
Level 3: Advanced
At an advanced level you are able to deeply analyze complex data sets, identify trends, patterns, and outliers, and provide insightful recommendations and predictions based on your findings.
Inferential Statistics
Inferential Statistics is the ability to analyze, interpret and draw conclusions from data, using statistical methodologies. This capability enables understanding patterns, correlations, and making predictions by extrapolating from observed data, providing informed insights that underpin strategic decision-making and risk management in an organization.
Level 1: Emerging
At an emerging level, you are beginning to understand the basic concepts of inferential statistics, with the ability to perform simple data analysis and draw initial conclusions from data sets.
Level 2: Proficient
At a proficient level you can confidently apply inferential statistics to draw conclusions and make predictions based on data, demonstrating a solid understanding of statistical concepts and techniques.
Level 3: Advanced
At an advanced level you are proficient in applying advanced inferential statistical techniques with precision and dexterity, interpreting complex data sets with depth and accuracy.
Descriptive Statistics
Descriptive Statistics refers to the capability to summarise and interpret data effectively, providing meaningful insights about the sample. It involves understanding, applying, and drawing conclusions from numerical data using statistical measures such as mean, mode, median, and standard deviation.
Level 1: Emerging
At an emerging level, you are grasping basic concepts of descriptive statistics, such as measures of central tendency and dispersion. You're starting to interpret data characteristics from simple data sets.
Level 2: Proficient
At a proficient level you are able to effectively analyze and interpret data using descriptive statistics to uncover trends, patterns, and insights, helping you make informed decisions based on data.
Level 3: Advanced
At an advanced level you are proficient in applying complex statistical techniques to analyze and interpret data, effectively communicating insights and recommendations to drive strategic decision-making within the organization.
Probability Theory
Probability Theory is the mathematical framework for analysing situations with uncertain outcomes, used to predict and understand the likelihood of specific events occurring in data-related contexts. It encapsulates concepts of randomness, chance and statistical significance, fundamental for data-driven decision making.
Level 1: Emerging
At an emerging level you are beginning to grasp basic probability theory concepts. You're able to identify events, calculate simple probabilities and understand terms like randomness and independence.
Level 2: Proficient
At a proficient level you are able to apply complex probability theory concepts to analyze data, make informed decisions, and communicate insights effectively within your organization.
Level 3: Advanced
At an advanced level, you are proficient in applying complex probability theory concepts to analyze data and make informed decisions. You can confidently interpret results and effectively communicate findings to others.
Qualitative Analysis
Qualitative Analysis is the ability to interpret non-numerical data. This includes identifying patterns, themes, biases, or relationships amid unstructured data such as text, images, or auditory content, aiding in comprehensive decision-making. It involves critical thinking, hypothesis testing, contextual understanding, and creative interpretations.
Level 1: Emerging
At an emerging level, you are beginning to understand how to interpret non-numerical data. You can identify basic patterns and themes, but need guidance to fully analyze qualitative information.
Level 2: Proficient
At a proficient level you are able to analyze qualitative data effectively, identifying trends, patterns, and themes to draw insightful conclusions. You can interpret findings accurately and communicate them clearly.
Level 3: Advanced
At an advanced level, you are able to conduct thorough qualitative analysis, using advanced techniques to interpret complex data sets and provide valuable insights for strategic decision-making within the organization.
Data Modeling
Data Modeling is a capability involving the design and deployment of data systems and databases. It incorporates the concept, creation, testing, and modification of data models, to logically define and visually represent data structures, optimising an organization’s data management and enhancing decision-making processes.
Level 1: Emerging
At an emerging level you are gaining understanding of data structures, developing abilities in creating and interpreting simple data models, and starting to utilize them effectively.
Level 2: Proficient
At a proficient level you are able to create and implement complex data models that accurately represent the relationships between different data elements, ensuring data consistency and integrity within organizational systems.
Level 3: Advanced
At an advanced level you are proficient in complex data modeling techniques, able to design and implement efficient data structures, optimize performance, and handle intricate data relationships effectively.
Big Data Analytics
Big Data Analytics is the capability to effectively analyze vast volumes of varied data types, employing advanced data tools and methods, to support business decision-making, unearth insights, reveal patterns and predict trends, thereby fostering strategic growth and enhancing operational efficiency within an organization.
Level 1: Emerging
At an emerging level, you are developing fundamental understanding of big data analytics. You are starting to engage with large datasets and exploring basic analytical tools and methodologies.
Level 2: Proficient
At a proficient level you are able to analyze complex big data sets, identify trends and patterns, and provide actionable insights to drive informed decision-making within the organization.
Level 3: Advanced
At an advanced level you are an expert at utilizing sophisticated analytical techniques to extract actionable insights from massive and complex datasets, driving strategic decision-making and innovation within the organization.
Machine Learning Basics
The capability to comprehend fundamental concepts of machine learning, including understanding types of algorithms and models, application of data pre-processing, training, validation measures, and basic implementation for making predictive and insightful data-driven decisions.
Level 1: Emerging
At an emerging level, you are familiar with the concept of machine learning. You understand basic algorithms, and can use simple models under supervision for predictive and classification tasks.
Level 2: Proficient
At a proficient level you are able to understand and apply fundamental concepts of machine learning, including algorithms, models, and evaluation techniques. You can effectively interpret and communicate results to others.
Level 3: Advanced
At an advanced level, you are able to apply complex machine learning algorithms to solve real-world problems, interpret results accurately, and make informed decisions based on the data analysis outcomes.
Predictive Analytics
Predictive Analytics refers to the utilization of statistical techniques, algorithms and AI technology to analyze historical data and predict future trends, behaviors and outcomes. It equips employees with the ability to anticipate business opportunities, manage risks and make strategic, data-driven decisions.
Level 1: Emerging
At an emerging level, you are familiarizing yourself with predictive analytics concepts. You can utilize data patterns to anticipate future outcomes, but still need guidance and support.
Level 2: Proficient
At a proficient level you are able to analyze data trends and patterns to make accurate predictions using advanced statistical models and machine learning techniques.
Level 3: Advanced
At an advanced level you are able to develop complex predictive models, interpret results, and make data-driven decisions to drive strategic business outcomes.
Data Mining
Data Mining involves the ability to extract and analyze large data sets, identify patterns and relationships via interpretation techniques, enabling decisive forecasting. This capability requires a strong understanding of statistical methods, predictive modelling, and an ability to communicate complex data insights with clarity.
Level 1: Emerging
At an emerging level, you are beginning to understand and apply basic principles of data mining. You're learning to extract and transform data, while developing fundamental analytical skills.
Level 2: Proficient
At a proficient level, you are able to effectively utilize various data mining techniques to identify patterns, trends, and relationships within large datasets to gain valuable insights for decision-making within the organization.
Level 3: Advanced
At an advanced level, you are proficient in data mining, able to extract valuable insights from complex datasets using advanced techniques such as machine learning and predictive analytics.
Spreadsheet Skills
Spreadsheet Skills refer to the ability to effectively utilize spreadsheet software, understanding and applying functions like formatting cells, data manipulation, calculations, and creating charts. It involves efficiently managing and analyzing data, performing complex mathematical functions, and interpreting results to support decision-making processes.
Level 1: Emerging
At an emerging level, you are comfortable navigating spreadsheet software. You can create, format, and make basic calculations within spreadsheets while demonstrating fundamental data entry skills.
Level 2: Proficient
At a proficient level you are able to demonstrate advanced skills in using spreadsheet applications, such as creating complex formulas, conditional formatting, and data visualization techniques to analyze and present data effectively.
Level 3: Advanced
At an advanced level, you are able to expertly manipulate complex data sets, create advanced formulas and functions, and automate tasks using macros in spreadsheet software with precision and efficiency.
SQL Proficiency
SQL Proficiency refers to one's ability to use Structured Query Language for manipulating and retrieving database information. This involves creating, implementing, and managing databases, understanding SQL syntax, writing efficient queries, troubleshooting errors, and optimising performance for data-driven decision making.
Level 1: Emerging
At an emerging level, you are beginning to navigate SQL. You've an elementary understanding of SQL syntax, capable of executing basic queries and manipulating small datasets.
Level 2: Proficient
At a proficient level you are able to independently write complex SQL queries, perform data manipulation, and troubleshoot code efficiently. You can optimize queries for better performance and understand advanced SQL concepts.
Level 3: Advanced
At an advanced level, you are able to confidently write complex SQL queries, optimize database performance, and design advanced data models to meet business requirements effectively and efficiently.
Data Collection
Data Collection is the systematic process of gathering and measuring information from a variety of sources in an established systematic fashion that enables an organization to answer relevant questions, evaluate outcomes and make predictions about future probabilities and trends.
Level 1: Emerging
At an emerging level, you are able to identify potential data sources, collect basic data accurately, and understand fundamental practices for handling it ethically and safely.
Level 2: Proficient
At a proficient level you are able to independently collect data using a variety of methods, ensuring accuracy, relevancy, and reliability. You understand the importance of data quality and ethics.
Level 3: Advanced
At an advanced level you are proficient in designing complex data collection strategies, implementing advanced data collection techniques, and ensuring data accuracy, reliability, and integrity throughout the process.
Data Cleaning
Data Cleaning refers to the process of identifying, correcting or removing errors, inaccuracies, duplicates and inconsistencies from datasets. This capability enhances data quality, reliability, and accuracy thus ensuring its fitness for decision-making, predictions, and business analytics.
Level 1: Emerging
At an emerging level, you are developing skills in identifying, understanding, and rectifying errors or inconsistencies within data sets to improve quality and accuracy.
Level 2: Proficient
At a proficient level you are able to identify and execute advanced data cleaning techniques independently, ensuring high-quality data sets for analysis and decision-making within the organization.
Level 3: Advanced
At an advanced level you are able to independently identify, clean, and validate data with complex structures and multiple sources, using advanced techniques and tools to ensure accuracy and reliability.
Data Classification
Data Classification refers to the ability to categorize and label data based on its sensitivity, value, and relevance. This includes understanding different data classification models, implementing data protection measures and complying with data privacy regulations. It enables appropriate handling and secure management of an organization's data assets.
Level 1: Emerging
At an emerging level, you are beginning to understand the basic principles of data classification, capable of categorizing data into basic groups and recognizing its potential value and sensitivity.
Level 2: Proficient
At a proficient level you are able to accurately classify various types of data, apply appropriate labeling mechanisms, and understand the importance of categorizing data for security and compliance purposes.
Level 3: Advanced
At an advanced level you are able to accurately classify various types of data, taking into consideration sensitivity levels, regulations, and business requirements. Your expertise in data classification is unparalleled.
