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Analyst Competency Framework
We have researched our Analyst Competency Framework to ensure that our vision for our members is aligned to industry standards in engineering and business fields. It creates a clear pathway through the field of analytics, with multiple entry points depending on educational levels.
Our training provision for members is fully aligned to this framework and will allow all stakeholders to have a better understanding of what they can expect of members at different levels of training, and the support needed.
What skills do data professionals need?
About the Framework
Our Framework covers the core competencies needed to take an end-to-end approach to data solutions. We assume our professionals will input into every stage of the data cycle.
Our members who qualify for one of our competency designations are interdisciplinary. They have appropriate training to be able to carry out mathematical calculations, using technology and applying that skill to a real world domain.
The competencies are divided into four areas of specialisation: Knowledge and Analytics; Governance and Professionalism; Communication and Leadership.
KNOWLEDGE AND ANALYTICS
Manipulating data and producing purposeful data products.
Learn how to do analytics, from spreadsheets to Transformers and deep learning.
GOVERNANCE AND PROFESSIONALISM
Identifying risk and working within regulations and best practices.
Learn whether to use analytics, and how to work within the sector ethically.
COMMUNICATION
Translating technical insights into actionable business terms.
Learn to share complex data insights and processes with everyone.
LEADERSHIP
Growing in trust, responsibility, and influence in your career.
Learn the tricks of personal effectiveness from the management world.
Go to the Framework
Can describe a problem in data terms
Manipulates data in a table or data set
Aggregates data for insights
Manipulates data structures in preparation for use in advanced analytics
Introduces efficiencies in data processing.
Describes data and data quality in some detail
Identifies and acts on relevant laws
Sources data responsibly promoting data reuse
Works within a quality framework
Describes choices made by a friendly enquirer
Seeks and responds well to guidance from others.
Reports on own work or contributes to a wider report
Produces simple visuals to communicate data insights
Can justify some choices made in visualisation design
Communicates to a diverse audience
Defines a problem in business terms
Applies effective time management skills to achieve project deadlines
Demonstrates collaborative skills
Describes risks of own practice
Can self reflect and work with support to identify next professional development goals
Technical Requirements of our Affiliate members on entry:
Typically, in the early stages, Affiliate members will be working towards or be able to perform the following data tasks:
Uses a preferred analytics tool efficiently (spreadsheet, coding environment, software etc.)
Carries out exploratory analysis, producing and analysing the 5 number summary.
Makes use of mean, mode, and standard deviation to understand data trends.
Produces and makes use of boxplots, histograms and scatter plots, as well as other charts.
Can explain normally distributed data and skewness.
Can explain outliers and a strategy to identify outliers.
Understands the terminology in data privacy rules.
Describes data using common official terminology (numerical, ordinal, categorical etc.)
Cleans data set manually, removing duplicates.
Carries out simple linear regression.
Documents work to prioritise readability.
Creates some simple visualisations making choices beyond the default in software or libraries, such as financial and sales reporting.
Can structure a report on own work to facilitate contestability.
Plans and executes simple projects.
Makes connections between own work and the business operations.
Can explain some risks of own work.
Please see below for a breakdown of competencies at Affiliate (late stage) level.
Cleans complex data sets and joins efficiently
Systematically approaches exploratory data analysis
Creates new data features
Identifies appropriate solutions from a range of options
Critically evaluates results
Identifies and applies data controls
Synthesises data from different sources
Identifies erroneous data risks
Applies appropriate evaluation metrics
Explains known benefits and issues with technical approaches
Confidently presents orally and in writing
Embeds visuals in data narratives or stories
Produces work aligned to a recognised accessibility standard
Shares lessons learnt within own team
Identifies how data adds value to own sector
Identifies or elicits project requirements
Manages risk in own work, including timely project deadlines
Manages expectations of a range of colleagues
Critically evaluates own work and learn from experience
Technical requirements of our Affiliate (Late stage) members on leaving the level:
Typically, in the later stages, Affiliate members will be able to perform the following tasks and be working towards the Associate criteria to move up a membership grade:
Produces visualisations with some flexibility in terms of labelling, colour and chart choice, and drawing attention to core features
Intentionally joins data sets on key variables
Creates new features of data (e.g. one hot encoding or dummy variables, mathematical summations and data filtering)
Produces interactive visualisations and work with map data
Works on both downloaded data tables and cloud data sets
Manages data version control
Works independently within a data privacy regulations framework
Can produce a schema to promote data reuse and with respect to copyright laws
Carries out simple hypothesis testing
Separates data into a train and test split intentionally
Creates surveys for data collection
Please see below for a breakdown of competencies at Associate level.
Analyses data fluently, manipulating data depending on characteristics
Works with tabular and non-tabular data on data sets n > 2 million.
Updates own practice in response to improvements in the field
Ensures safe and secure management of data.
Recognises, describes and quantifies bias
Can make connections across data sets.
Documents processes systematically
Mitigates sources of uncertainty and error in data processes.
Negotiates success criteria across stakeholders
Takes a leading role in project evaluations
Produces impactful visuals that avoid deception
Can identify learning opportunities to improve communication and collaboration
Designs and delivers data products that are aligned to business strategy
Independently defines the workflow and timescale of a project
Identifies and mitigates some risks
Manages effective business relationships
Has a strategy to stay ahead of business developments
Technical requirements of our Associate members:
Some Associates may be working towards the technical requirements for full Members if they wish to pursue a data career.
For those in non-data professions, Associate Member grade evidences advanced data literacy for non-technical specialists.
Typically, all our Associates will be able to perform the following tasks.
Plans and executes a manual and automated data cleaning strategy
Explains the benefits and disadvantages of methods for dealing with missing data (e.g. listwise deletion)
Explains risks of data collection and data bias
Embeds visualisations in data stories, making intentional choices and avoiding deception
Uses a mix of automation and manual work to create new features of data
Gets a data set ready for the machine learning pipeline
Reports confidence intervals and risk factors for all results
Plans, implements and reports on a strategy to normalise the data for machine learning
Works with the output of common machine learning models (regression, clustering, categorisation, anomaly detection, tree based models and association mining)
Explains analysis of unstructured data even if they don’t carry out the analysis
Identifies black boxed techniques and name some unintended consequences
Can explain key AI architecture (e.g. transformers) and name potential risks
Writes functions
Breaks down a complex problem using computational thinking
Protects data sets and the data within them
Links data work to business strategy and outcomes
Please see below for a breakdown of competencies at Affiliate (late stage) level.
Cleans complex data sets and joins efficiently
Systematically approaches exploratory data analysis
Creates new data features
Identifies appropriate solutions from a range of options
Critically evaluates results
Identifies and applies data controls
Synthesises data from different sources
Identifies erroneous data risks
Applies appropriate evaluation metrics
Explains known benefits and issues with technical approaches
Confidently presents orally and in writing
Embeds visuals in data narratives or stories
Produces work aligned to a recognised accessibility standard
Shares lessons learnt within own team
Identifies how data adds value to own sector
Identifies or elicits project requirements
Manages risk in own work, including timely project deadlines
Manages expectations of a range of colleagues
Critically evaluates own work and learn from experience
Technical requirements of our Affiliate (Late stage) members on leaving the level:
Typically, in the later stages, Affiliate members will be able to perform the following tasks and be working towards the Associate criteria to move up a membership grade:
Produces visualisations with some flexibility in terms of labelling, colour and chart choice, and drawing attention to core features
Intentionally joins data sets on key variables
Creates new features of data (e.g. one hot encoding or dummy variables, mathematical summations and data filtering)
Produces interactive visualisations and work with map data
Works on both downloaded data tables and cloud data sets
Manages data version control
Works independently within a data privacy regulations framework
Can produce a schema to promote data reuse and with respect to copyright laws
Carries out simple hypothesis testing
Separates data into a train and test split intentionally
Creates surveys for data collection
Please see below for a breakdown of competencies at Member level.
Sources and acquires data sets responsibly
Demonstrates competence in programming
Applies machine learning to novel problems
Flexibly produces both static and interactive data products, or work with API access to data
Manages version control effectively and aspects of data architecture
Acts with integrity and respect to complex legal and regulatory environments
Acts consistently within best practice frameworks
Takes a systematic approach to reproducibility
Handles missing data appropriately
Evidences sustainability of practices in the organisation
Builds appropriate and effective business relationships internally
Engages with ideas of others including outside own organisation
Can produce interim reports that promote agile development of data work
Applies automation to promote reproducibility
Implements data provenance procedures
Produces data solutions that provide value
Critically evaluates a situation, problem and proposed solution
Determines strategic success criteria, including time frames to market and business outcomes
Maintains an ongoing risk management strategy
Supports the empowerment of others on the team or beyond
Technical requirements of our Members:
Our full Members will begin to specialise, and may have advanced knowledge of specific aspects of data analysis. The following skills, however, will be common to all Members:
Chooses imputation methods intentionally
Makes connections across data sets efficiently and effectively
Carries out machine learning processes in a coded environment (R or Python)
Works with feature engineering to improve outputs
Demonstrates thorough understanding of deep learning frameworks and architectures either through practice or technical knowledge
Demonstrates advanced understanding of mathematics, statistics, probability, pattern detection and algorithm construction relevant to the analytical process
Uses SQL flexibly
Works within emerging and sometimes complex data and AI regulatory frameworks
Our Senior Members may be highly specialised and may be working in more leadership roles, possibly with less time carrying out hands-on analytics.
The following competencies will be common to all Senior Members:
Works with a range of data structures on solutions to complex problems
Flexibly identifies environments to host solutions
Takes a systematic approach to data pipelines
Identifies strategies to work with new technology
Sets standards in the organisation for ethical data practices
Acts with integrity above and beyond own regulatory and legal jurisdictions
Constructively evaluates own work and peer work
Consistently operates within industry standards of interoperability
Actively promotes and implements sustainable product design
Negotiates effectively to improve standards
Produces engaging visual tools without compromising integrity
Works to open science research standards
Aligns to international standards of accessibility and inclusion
Builds effective external business relationships
Reliably contributes to company risk strategy
Identifies and critically evaluates assumptions/p>
Empowers others across the organisation and beyond
Contributes in a significant way to the organisation
Can stay ahead of the latest developments or lead developments in the field
Technical requirements of our Senior Members:
There are no additional technical requirements for Senior Members. We would expect our Senior Members to have considerable influence within their organisation, and typically, they will hold a Chief Data Officer or research role.
Our senior members will consolidate the technical skills already mastered, demonstrating increasing understanding of the nuance of the tools they are using. They will also demonstrate their leadership skills through increased responsibility and influence within their organisation.
Our Fellow Members will be able to demonstrate an outstanding contribution to the data professions.
Has a record of solving complex and intractable problems and / or novel problems using innovative techniques
Directs policy and leads the way in improving standards outside of own organisation
Is a respected expert in the field evidenced through a body of work
Influences policy and best practice beyond their own organisation
Technical requirements of our Fellow Members:
There are no additional technical requirements for Fellow Members and application to Fellow status is either by invitation or individual application and panel review. Please see the Membership Page for more information on how to nominate yourself or others for fellow status.
How to use the framework
People new to the IoA can use the framework to identify how their existing skills map to our membership levels.
For our existing members, these criteria can be used to decide what evidence on your experience to add to your Portfolio to build your reputation in the field.
It is common for data analysts to develop depth of skills in certain areas, but not always breadth. If you have any gaps in your skills, we have training resources to support.
You may find that some of these skills are already part of your daily practice, but the framework provides the language to discuss your practices with fellow practitioners and future employers.
Employers should refer to the framework to manage their realistic expectations of what new data hires can and cannot achieve at their current level of training and experience.
Learning the Practie of the data science community
You may need to learn new data science and apply them in your work to imporve you employability and promotion aspects.
Learning the languate of the data science community
You may already carry out some analytics in your work but need the language of the data science community to describe your employability skills.
How does membership align to the framework?
Our membership levels reflect the growing competence of our members, and support discussions around expectations of responsibility and progression at the different career stages.
Our core professional designations are:
Associate Member (AIoA) - A person with considerable sector or role knowledge and experience outside of the data professions and able to take responsibility for basic analytics work and making sensible decisions around data processes or a junior data scientist with foundational data skills.
Member (MIoA) - Our data professionals. These members can take responsibility building more complex projects and flexibly create data solutions.
Senior Member (SIoA) - Our data leaders and Chief Data Officers. They innovate and lead their teams through strategic data projects.
Fellow (FIoA) - Fellow (FIoA) - Our innovators and global leaders.
How does IoA Training align to the Framework?
We have level-appropriate training to ensure that members can access support in developing all of the technical skills and competencies to progress through our membership levels. Just check the course number to see how training aligns.
In the early years, there is a greater emphasis on developing a range of new, foundational skills.
Later career professionals will typically then begin to specialise and develop deeper understanding and practice within their areas of expertise, and there is more scope to tailor our training to their unique profile.
All our later career professionals will be able to contribute professionally beyond their data expertise and take growing responsibility for their own work and the work of others.
For more on training and how you can evidence progression through the Analyst Competency Framework levels, why not consider joining the Institute of Analytics?
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