Working with Spreadsheets
Working with Spreadsheets is the adept application of tools and techniques to efficiently manage, analyze, and present collected data. It involves understanding the principles of data entry, formulas, and data visualisation. This capability impacts the ease and effectiveness of data uplift efforts, elevating decision making and strategic planning processes.
Level 1: Emerging
At a foundational level you are confident entering, editing, and organizing data in spreadsheets to support basic data uplift tasks. You follow set templates and use simple formulas, charts, and filters to present data clearly. Your careful handling of data ensures accuracy and helps others make informed decisions.
Level 2: Proficient
At a developing level you are able to enter, organize, and update data in spreadsheets with growing confidence, using simple formulas and basic formatting. You begin to present your data in ways that make uplift patterns easier to see and share. This increases the reliability of your data and supports more informed decisions within your team.
Level 3: Advanced
At a proficient level you are able to confidently use spreadsheets to organize, analyze, and visualize data to support data uplift activities. You apply formulas and tools to uncover patterns or issues, ensuring data is accurate and valuable. Your work enables teams to make informed decisions that genuinely improve business outcomes.
Using Data to Inform Decisions
Using Data to Inform Decisions is the strategic application of data comprehension to drive impactful choices. It encompasses the ability to interpret, analyze and utilise relevant data, fostering informed decision-making processes. This capability empowers Data Uplift by providing accurate insights, facilitating effective operational strategies and ultimately fostering enhanced organizational outcomes.
Level 1: Emerging
At a foundational level you are beginning to use data to support simple decisions in your daily work. You can identify basic sources of data, understand straightforward data displays, and use this information to guide your actions. This builds your confidence in using data and helps you contribute to improving team outcomes through informed choices.
Level 2: Proficient
At a developing level you are beginning to use data to guide your decisions, with some support from others. You can interpret simple data sets and draw basic conclusions that inform your day-to-day work. Your growing confidence helps Data Uplift by enabling more thoughtful contributions to team goals and operational improvements.
Level 3: Advanced
At a proficient level you are able to confidently interpret and analyze relevant data to guide your decisions, always ensuring your actions align with organizational goals. You use data insights to recommend effective solutions and support operational improvements across your area. Your approach helps drive Data Uplift by making informed choices that improve results for your team and the wider business.
Using Dashboards for Monitoring
Using Dashboards for Monitoring' is the ability to deploy visualisation tools effectively for tracking data uplift progress. It involves understanding and implementing appropriate dashboard design, regularly reviewing data, and making evidence-based decisions. This capability enhances accountability, enables rapid response to challenges and supports successful data uplift initiatives.
Level 1: Emerging
At a foundational level you are able to access and navigate dashboards to view key metrics and trends related to data uplift progress. You can recognize basic visual patterns and regularly check dashboard updates to stay informed. This helps you understand how data uplift efforts are tracking and supports your participation in team discussions.
Level 2: Proficient
At a developing level you are able to use existing dashboards to monitor data uplift progress and spot basic trends or issues. You review the displayed information regularly and share key findings with your team. Your actions help ensure the data uplift project stays visible and that small challenges are flagged early.
Level 3: Advanced
At a proficient level you are able to set up and use dashboards to actively monitor data uplift progress in your area. You review dashboard information regularly, spot trends, and make timely adjustments to keep projects on track. Your approach ensures that data uplift initiatives are transparent and responsive to emerging challenges.
Understanding Data Types
Understanding Data Types is a crucial capability for enhancing data uplift. This pertains to the knowledge and aptitude for identifying, categorising and comprehending various data kinds. Grasping the nuances of different data types directly influences how information is analyzed and utilised, thus, impacting decision-making and strategic planning within a data uplift context.
Level 1: Emerging
At a foundational level you are able to recognize basic data types such as numbers, text, and dates, understanding their general purpose. You can correctly sort and label these types when working with data uplift activities. This helps you avoid errors when handling information and ensures data is set up well for more effective analysis and decision-making.
Level 2: Proficient
At a developing level you are able to recognize common data types and begin to understand how they fit into data uplift activities. You can identify basic differences between types and use this knowledge to help in simple data categorisation tasks. This builds a foundation for more accurate data analysis and better informed decisions.
Level 3: Advanced
At a proficient level you are able to confidently identify and categorise a broad range of data types relevant to data uplift projects. You consider how the characteristics of different data types affect storage, analysis and reporting. Your understanding directly improves the quality and effectiveness of data-driven decisions in your team or area.
Understanding Data Lifecycle
Understanding Data Lifecycle is a critical aspect of the Data Uplift framework. This capability involves understanding the journey that data undergoes, from creation and initial storage, to the final stages of archiving or deletion. It also includes acknowledging the various transformations data may experience throughout its lifecycle. This comprehension enables effective data management, fostering improved organizational intelligence, and empowering decision-making based on robust, precise data analysis.
Level 1: Emerging
At a foundational level you are aware that data goes through distinct stages, from its creation to its eventual archiving or deletion. You recognize that data may change form or purpose during this journey, even if you’re not yet applying this knowledge in your daily work. This basic understanding helps you appreciate the importance of handling data responsibly as part of Data Uplift.
Level 2: Proficient
At a developing level you are able to describe each key stage in the data lifecycle and recognize how data is managed and transformed between these stages. You can identify where your own work fits within the lifecycle and begin to consider how your actions contribute to effective data uplift. This understanding helps you support better data quality and decision-making in your team.
Level 3: Advanced
At a proficient level you are able to map out the full lifecycle of data within your organization, recognizing key touchpoints from creation to archiving or deletion. You apply this understanding to support improved management and transformation of data assets. Your work enhances the quality and value of data available for analysis and decision-making.
recognizing Data Bias
recognizing Data Bias is the ability to identify and correct elements within datasets that can cause unfair outcomes. This involves comprehending how data has been collected, acknowledging any inherent biases and altering processing techniques accordingly. It aids in improving data integrity and making informed, equitable decisions in the context of data uplift.
Level 1: Emerging
At a foundational level you are able to spot basic signs of bias in data sets and question whether the data might be unfair or unbalanced. You recognize when data sources or collection methods may affect outcomes, especially in data uplift projects. This awareness helps you raise issues early and support fairer decision-making.
Level 2: Proficient
At a developing level you are starting to spot obvious biases in datasets and know that these can affect results in data uplift projects. You rely on standard checks and ask for support when you find something uncertain. Your growing awareness helps reduce some unfair outcomes, but you still need guidance to fully address more complex data bias issues.
Level 3: Advanced
At a proficient level you are able to spot bias in datasets and take practical steps to address it in data uplift projects. You consider how data was collected and actively adjust your approach to improve fairness. By doing this, you help your team make decisions that are more accurate, reliable, and equitable.
Querying Data Sets
Querying Data Sets is the capability to extract and scrutinise specific data, aiming to enhance data integrity and relevancy. It involves understanding and employing advanced data retrieval techniques, ensuring information is accessible to support data uplift tasks. The skillful execution of this function facilitates informed decision-making and contributes to the overall quality of data management.
Level 1: Emerging
At a foundational level you are able to locate and retrieve basic sets of data using clear instructions or predefined queries. You follow straightforward processes to check that the information you find is complete and suitable for the task at hand. This helps support simple data uplift activities and builds confidence in your data work.
Level 2: Proficient
At a developing level you are able to use basic queries to extract relevant data from familiar sources, supporting straightforward data uplift activities. You follow established procedures and begin to spot issues or inconsistencies in the data you retrieve. Your actions help maintain data accuracy and lay the groundwork for improving data quality.
Level 3: Advanced
At a proficient level you are able to confidently use advanced querying techniques to extract relevant and reliable data for uplift tasks. You consistently identify the best sources, check data integrity, and tailor your queries to meet project needs. Your efforts ensure that the data you provide is accurate and ready to support informed, value-driven decisions.
Promoting Data-Informed Culture
Promoting Data-Informed Culture is the fostering of an organizational ethos that values and utilises data in decision-making. It involves enhancing skills in data interpretation and analysis, and encouraging behaviors that prioritize data-driven insights. This capability contributes to data uplift by enabling a culture that optimizes data handling, quality and usage, ultimately driving strategic outcomes.
Level 1: Emerging
At a foundational level you are learning to recognize the importance of using data when making everyday decisions. You seek guidance to understand basic data concepts and start to ask questions about how data can support your work. By doing this, you help build a culture where data is valued and used more often across the organization.
Level 2: Proficient
At a developing level you are beginning to seek out data when making decisions and encourage colleagues to do the same. You contribute to discussions by asking how data can support projects or solve problems. This helps build confidence in using data day-to-day and sets an example for others to follow, lifting your team's data capability.
Level 3: Advanced
At a proficient level you are consistently encouraging your team to use data when making decisions and sharing insights. You help others develop their data skills and confidence, making sure that data-informed thinking becomes part of daily work. This uplifts data use across your area, improving both results and collaboration.
Metadata Awareness
Metadata Awareness is the ability to understand, interpret, and apply metadata within a Data Uplift setting. It involves staying informed about new metadata technologies and being capable of mapping and integrating metadata. This capability contributes significantly to improved data quality, analytics analysis, and overall data management efficiency.
Level 1: Emerging
At a foundational level you are aware of what metadata is and why it matters for Data Uplift activities. You can recognize basic types of metadata and understand their role in improving data quality and supporting analytics. Your awareness helps ensure that data is easier to locate, interpret, and use within your team.
Level 2: Proficient
At a developing level you are starting to recognize the value of metadata and can identify basic types relevant to Data Uplift projects. You follow guidance to interpret and use metadata in routine tasks, with support where needed. Your growing awareness helps to improve data consistency and supports smoother data-driven processes.
Level 3: Advanced
At a proficient level you are able to confidently interpret and use metadata in Data Uplift projects, ensuring information is both accurate and well-organized. You can map and connect different metadata sources to support smoother data integration and analysis. This means your work directly improves data quality and reliability across the organization.
Asking Data-Driven Questions
Asking Data-Driven Questions' is the ability to formulate investigative queries based on trends, patterns, and outliers within datasets. This incorporates the knowledge of statistical analysis and a strong ability to interpret data. By promoting evidence-based decision making, this capability enhances the overall effectiveness of Data Uplift strategies.
Level 1: Emerging
At a foundational level you are able to notice basic trends and patterns in simple datasets, and ask straightforward questions about what they might mean. You are beginning to use data to inform your curiosity or guide small decisions in your work. By doing this, you help your team make early steps towards more evidence-based practices in Data Uplift.
Level 2: Proficient
At a developing level you are beginning to identify simple trends and patterns in data and ask basic questions to understand their causes. You use your early knowledge of analysis to support Data Uplift initiatives by seeking explanations and clarifying outliers. This helps your team move towards more evidence-based decisions.
Level 3: Advanced
At a proficient level you are able to identify meaningful trends and anomalies in data, then turn these findings into clear, targeted questions that drive Data Uplift initiatives. You use your understanding of statistics and data interpretation to guide teams towards practical, evidence-based decisions. This helps your organization uncover new opportunities for improvement and deliver better outcomes.
Maintaining Data Accuracy
Maintaining Data Accuracy is the proficiency in upholding precise and correct data crucial to informing data uplift strategies. It encompasses regularly reviewing and correcting data inconsistencies, ensuring alignment with specified standards. The impact being improved decision-making, risk reduction, and enhanced operational efficiency in data uplift exercises.
Level 1: Emerging
At a foundational level you are careful to check basic data for obvious errors and inconsistencies during data uplift activities. You follow clear instructions and use standard tools to help keep data accurate and in line with expectations. This helps your team avoid mistakes that could affect the quality of decisions based on the data.
Level 2: Proficient
At a developing level you are starting to check data for obvious errors and make basic corrections as you support data uplift activities. You rely on guidance to spot issues and learn to follow data standards as you gain experience. Your attention helps reduce simple mistakes, making data more reliable for team decision-making.
Level 3: Advanced
At a proficient level you are consistently reviewing and correcting data to maintain accuracy in all data uplift activities. You spot and resolve inconsistencies, ensuring data remains aligned with defined standards. This leads to smoother data uplift processes, fewer errors, and more reliable insights for better business decisions.
Interpreting Data Visualisations
Interpreting Data Visualisations is the ability to understand and translate complex data sets into insightful visuals. This involves the capability of using various data visualisation tools, as well as a deep understanding of data analysis. Its impact is significant to Data Uplift, empowering teams to make evidence-based decisions and comprehend data insights more efficiently.
Level 1: Emerging
At a foundational level you are able to read and understand simple data visualisations, such as basic charts or graphs, to find key trends or patterns. You use these visuals to support your understanding of business data in projects or team discussions. This helps you contribute to evidence-based decisions and uplift data capability across your team.
Level 2: Proficient
At a developing level you are able to read basic data visualisations and explain what they show in clear terms. You can use common tools to produce straightforward charts that help your team understand key trends or insights. This supports Data Uplift by making it easier for others to act on the information you share.
Level 3: Advanced
At a proficient level you are able to accurately interpret complex data visualisations and clearly explain their meaning to your team. You use a range of visual tools to make data uplift projects more transparent and actionable. Your insights help others make informed decisions and drive measurable improvements.
Data Quality Awareness
Data Quality Awareness is recognizing the crucial role data accuracy plays in a Data Uplift framework. It involves the ability to scrutinise data meticulously, ensuring it's fit for purpose and enhances organizational decision-making. The impact promotes informed strategies, thereby elevating the quality and effectiveness of operational outcomes.
Level 1: Emerging
At a foundational level you are aware that accurate data is essential for effective Data Uplift. You recognize when data may be incomplete or unreliable, and understand that checking for quality supports better decisions. By doing this, you help set the groundwork for stronger, more informed organizational outcomes.
Level 2: Proficient
At a developing level you are beginning to notice the importance of data quality in Data Uplift activities. You check data for obvious errors and raise concerns when information looks unreliable. Your growing attention to accuracy helps your team make better choices and supports the goal of lifting data standards across the organization.
Level 3: Advanced
At a proficient level you are able to review and validate data with confidence, identifying issues that could affect accuracy within Data Uplift processes. You consistently check data sources for reliability and support colleagues in applying quality standards. Your actions ensure decisions are made on solid information, improving project outcomes and organizational trust in data.
Data Ethics and Privacy
Data Ethics and Privacy is the ability to uphold ethical standards and comply with privacy legislation while managing, analyzing, and interpreting data. This capability encompasses the knowledge of data ethics principles and privacy laws, the capability to apply them in data handling processes, and the behavior to ensure data integrity and confidentiality. It significantly contributes to improving data credibility, thereby enhancing overall Data Uplift effectiveness.
Level 1: Emerging
At a foundational level you are aware of basic data ethics principles and your responsibilities under privacy laws when handling data. You follow simple instructions to keep information confidential and recognize when to seek advice. By doing this, you help protect data integrity and support trust in your team's data uplift activities.
Level 2: Proficient
At a developing level you are beginning to understand ethical standards and basic privacy requirements when working with data. You follow established guidelines and ask for help if you’re unsure about data handling or confidentiality. By building your confidence in these areas, you help protect data integrity and support trust in our Data Uplift work.
Level 3: Advanced
At a proficient level you are consistently applying ethical standards and privacy laws when managing and analyzing data for Data Uplift projects. You recognize and address potential risks to data integrity and confidentiality in everyday tasks. This helps build trust in your team’s insights and strengthens the organization’s data credibility.
Data Collection Fundamentals
Data Collection Fundamentals is an essential skillset for accurate data gathering, crucial to Data Uplift. It involves literacy in data-sourcing techniques and a strong understanding of data integrity. Implementing robust strategies for collecting and validating data drives an organization's data uplift, empowering accurate decision-making processes and adding measurable value throughout.
Level 1: Emerging
At a foundational level you are able to follow clear instructions to collect basic data from defined sources, making sure records are complete and accurate. You understand why checking data quality matters for Data Uplift and use simple tools to check for obvious errors. By doing this well, you help your team make more reliable decisions.
Level 2: Proficient
At a developing level you are able to follow established methods to collect basic data for Data Uplift projects, asking for help when you face unfamiliar situations. You check that the information you gather is accurate and complete, understanding why reliable data matters for business decisions. Your efforts support stronger data quality and more confident reporting across your team.
Level 3: Advanced
At a proficient level you are able to collect data accurately and consistently, using agreed techniques and tools that ensure high-quality results. You recognize and address data integrity issues, taking steps to validate information before it enters organizational systems. Your approach supports reliable data uplift, enabling teams to make confident, evidence-based decisions.
Communicating Data Insights
Communicating Data Insights is the ability to translate complex data findings into clear, comprehensible and actionable information. This requires a comprehensive understanding of data patterns, analysis techniques, and a flair for storytelling to convey meaningful results. The impact is informed decision making, fueling strategic growth and operational efficiency within the Data Uplift framework.
Level 1: Emerging
At a foundational level you are able to share basic findings from data in a clear and straightforward way, helping your team understand what the numbers mean. You follow simple guidance to explain results and use everyday language to make information accessible. This helps others start to use data in daily decisions within the Data Uplift framework.
Level 2: Proficient
At a developing level you are able to share data insights in a straightforward way, using basic visual aids and simple language. You can explain what the data shows and offer some first ideas on what it means for Data Uplift projects. Your clear communication helps your team make better day-to-day decisions.
Level 3: Advanced
At a proficient level you are able to clearly explain data insights to both technical and non-technical colleagues, using straightforward language and visuals. You structure your messages to highlight what matters most for Data Uplift goals, ensuring your audience understands what actions to take next. Your communication builds trust and directly supports smarter decisions across the organization.
Collaborating with Data Professionals
"Collaborating with Data Professionals is the ability to effectively engage, communicate and work alongside data experts to enhance the accuracy, quality and relevance of data uplift initiatives. It involves understanding their unique perspective and skillsets thus driving innovative data uplift strategies. This collaboration fosters a more comprehensive and successful data uplift outcome, enriching the organizational data landscape."
Level 1: Emerging
At a foundational level you are open to engaging with data professionals by sharing information and seeking basic guidance on data uplift projects. You recognize their expertise and are willing to listen and ask questions to better understand their approach. This helps you become more aware of how collaboration can improve the value of data uplift in your work.
Level 2: Proficient
At a developing level you are beginning to work alongside data professionals by seeking their input and clarifying your own needs for data uplift projects. You listen to their perspectives and willingly share information, helping to avoid misunderstandings. This cooperation leads to more accurate results and builds your confidence in cross-functional teamwork.
Level 3: Advanced
At a proficient level you are able to work closely with data professionals, sharing information and integrating their feedback into your data uplift projects. You recognize the value of their expertise, actively seeking their input to improve outcomes. This helps deliver data uplift initiatives that are more reliable, relevant and aligned with organizational goals.
Basic Data modeling Concepts
Basic Data modeling Concepts' is the foundational understanding of data structures and relationships, pivotal for effective Data Uplift. This encompasses knowledge of different data model types and their applications, fostering accurate, accessible data interpretation. Mastery of these concepts fosters improved data quality, driving insightful decision-making and propelling organizational growth.
Level 1: Emerging
At a foundational level you are able to recognize simple data structures and basic relationships between datasets, supporting the core goals of Data Uplift. You can identify and describe different types of data models and understand their place in improving data quality. This enables you to support clearer analysis and help ensure data is accessible for better decision-making.
Level 2: Proficient
At a developing level you are able to identify basic data structures and simple relationships between data sets, applying these concepts to straightforward Data Uplift initiatives. You use common data models with some guidance, supporting accurate data organization and interpretation. This helps you contribute to clearer business insights and more reliable reporting.
Level 3: Advanced
At a proficient level you are able to apply basic data modeling concepts to real-world Data Uplift projects, confidently identifying and mapping relationships between different data sets. You structure data in ways that improve its accuracy and useability for colleagues, helping teams make better decisions and boost business outcomes.
