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.
