The ability to process data, typically with the help of software.
Is able to input data, run queries, and understand the basics of data processing.
Data innovation: Can use basic data processing tools. No specialised knowledge of the subject matter.
Data pipelining: Is able to use a range of data processing tools and techniques to manage datasets.
Data management: Understands the basics of what data is and how it can be manipulated
Understands the complexities of data processing, can assess quality, and assess financial implications.
Data innovation: Has a good understanding of the data processing tools available, and has good knowledge of the subject matter.
Data pipelining: Is able to use the right tool for the job, and has a good grasp of the basics of machine learning.
Data management: Can handle large amounts of data, knows what tools to use to manipulate data, has a basic understanding of databases
Leads the team in understanding the intricacies of data processing. Has in-depth knowledge in assessing quality and financial implications.
Data innovation: Has in depth knowledge of both the subject matter and the data processing tools available. Can innovate with data sets to create new market insights for the organisation.
Data pipelining: Can assemble complex pipelines from a wide array of components, applying deep domain knowledge in real world contexts.
Data management: Manages data across multiple departments, has expertise in databases and knowledge of how machine learning fits into the process
Works alongside the director in understanding the intricacies of data processing. Has in-depth knowledge in assessing quality and financial implications.
Data innovation: Responsible for driving innovation with data sets, collaborating with senior management to analyse opportunities to use data sets in new ways.
Data pipelining: Building and operating complex pipelines and machine learning models is second nature. Leads teams in building and maintaining models that process and analyse large datasets.
Data management: Is able to make recommendations for machine learning tools and applies them to the data
Is an expert on data processing both at a technical level and can assess its impact to the organisation. Is able to identify new trends through analysis of data sets.
Data innovation: Owns the team's innovation with data sets, bringing new insights to emerging opportunities in the market. Collaborates with senior management to drive innovation agenda.
Data pipelining: Is recognised as an expert in data processing, with expertise across a range of domains. Leads team building and maintenance efforts for large datasets. Transforms problems into opportunities by understanding the underlying data.
Data management: Has mastered all aspects of data management, understands the limitations and possibilities for machine learning