Big Data / Data Science skills in SFIA
Click on image to see full-size interactive pdf with hyperlinked skill names.
The Big Data / Data Science view of SFIA has been developed to provide a quick start identification of the SFIA skills which are most relevant/illustrative for organisations to adopt and operate big data, data analytics and data science working practices.
- This view of SFIA identifies around 40 to 50 professional skills related to data within the complete SFIA framework of more than 100 skills.
- The content of the SFIA framework is reviewed on a regular basis, and with the latest version of SFIA, the content has been reviewed and updated to capture the professional skills needed - whether for leading, managing and executing the adoption of effective data management practices
- This includes the skills needed for developing a supportive culture and of new organisational capabilities for data management
- As with all applications of SFIA; this view should be considered against your specific organisational context and business objectives.
- The intention is not to draw a hard boundary around these skills or to imply that other SFIA skills aren't appropriate. In practice, once you have familiarised yourself with this view; it is likely that you will refer to the full SFIA framework for additional and complementary skill definitions.
- The full SFIA reference guide and a spreadsheet version of all skill descriptions are available to download. You will need to be registered as a user on the site first but it is a very simple process.
- This website provides advice and guidance on the adoption of SFIA.
- There is also an active global ecosystem of SFIA Partners, SFIA Consultants and Practitioners. They are available for advice on adopting SFIA. Full details are available here.
- If you represent a professional body or a framework owner and would like to collaborate with the SFIA Foundation on the development of additional SFIA views; please contact the SFIA General Manager
For ease of use, the Big Data/Data Science skills view has been split into 4 focus areas based on different aspects of data management practices.
- The groupings are a navigational aid only
- Once you are familiar with the content of SFIA they may not be needed
- Each SFIA skill is present in only one focus area, but in practice, some skills could easily be represented in more than one focus area
- SFIA skills for individual job roles should be selected appropriately from any of the focus areas and/or from the wider SFIA framework
- skills needed to align data management practices to organisational purpose through the development and implementation of effective strategies, policies and procedures
- skills needed to assess the maturity and effectiveness of data management practices and develop improved organisational capabilities (people, processes and technology)
- skills needed to support the full data lifecycle from generation capture, maintenance, active use, publication, archiving, purging
- skills needed for common foundational practices underpinning the other data management capabilities
Extract of SFIA design principles
SFIA defines levels of responsibility and skills.
SFIA does not define jobs, roles, people, processes or general areas of activity, however important they are.
SFIA defines the essence of skills.
SFIA is descriptive, not prescriptive. It does not define low-level tasks nor deliverables.
SFIA provides an integrated view of competency.
SFIA recognises levels of responsibility, professional skills, behaviours or attributes, knowledge and qualifications and certifications. It shows how these fit together and how they complement each other.
SFIA is independent of technology and approach.
SFIA does not define technology, methods, approaches or technical knowledge – these change rapidly but the underlying skills are more persistent. So, for instance, Cloud, DevOps, Agile, Big Data and digital roles etc. can be described using a combination of the SFIA skills.
SFIA does not assume or recommend specific organisation structures, job or role designs.
The SFIA skills and levels can be configured flexibly to support all organisational types and structures. It works for individuals, small and large teams, whole departments or entire organisations with thousands of employees.
Data management jobs and skills profiles
Job titles are typically used as a shorthand reference to Data Management jobs. So in organisation structures and job ads we see a variety of titles. e.g.
Chief Data Officer
Head of Data Engineering
Data Integration Architect
Data Ingestion Engineer
Business Intelligence (BI) Analyst
Analytics report developer
- Software Developer
- Application Architect
- Enterprise Architect
- DevOps Engineer
We should also consider the variety of prefixes commonly used to support job grading or career pathing. e.g.
- Head of Data Science
- Lead Data Scientist
- Principal Data Scientist
- Senior Data Scientist
- Data Scientist
- Junior Data Scientist
- Trainee Data Scientist
However, as in most professional disciplines, there is no common definition of what these jobs actually do. So although in common usage, referring only to a job title in isolation can be confusing and does not help organisations recruit, develop, deploy, manage and retain their valuable talent.
Using SFIA to improve Big Data/Data Science job descriptions.
SFIA provides a common language of skills and skill levels. It is very flexible and this enables organisations to design their own team structures, roles and job titles. They can then select the appropriate configuration of SFIA skills and competency levels to match.
- The SFIA skills for roles responsible for big data and data science should be selected based on an analysis of the role's accountabilities and responsibilities.
- To provide the necessary focus, aim for no more than 6 to 8 SFIA skills per role (less if possible)
- The required skills can be selected from the full range of skills in SFIA
- The skill levels chosen should be based on the responsibility levels of the role and aligned to SFIA's generic attributes for levels of responsibility
Focussing on job responsibilities, and the SFIA-defined professional skills and responsibility levels provide a much clearer definition of the requirements of the job. This, in turn, supports people management related activities such as recruitment, skills assessment, professional development, and performance management.
Standard or model skills profiles for Big Data/Data Science roles
The SFIA Foundation is looking to develop and publish a set of standard skills profiles for some of the common industry roles. This is work in progress and if you would like to know more and/or contribute please contact the Updates Manager.
Where is the Data Science skill in SFIA?
Following these principles, it should be clear why there is no single "Data Science" skill in the SFIA framework, There are many SFIA skills and attributes at multiple levels which organisations can align to their Data Scientist roles.
Potential Data Scientist Skills in SFIA
.. and their Generic Attributes
Data modelling and design
Emerging technology monitoring
Methods and tools
Listing of potential Big Data/Data Science Skills
SFIA Skill name& hyperlink to full skill description for each level
Short-form SFIA skill description
(1st few words only)
|Levels of Responsibility
see full description for each skill level by following the link
|Data governance||Information governance||The overall governance of how all types of information, structured and unstructured, whether produced ...||4||5||6||7|
|Strategic planning||The creation, iteration and maintenance of a strategy in order to align organisational actions, plans ...||5||6||7|
|Enterprise IT governance||The establishment and oversight of an organisation's approach to the use of Information systems and ...||5||6||7|
|Information systems coordination||Typically within a large organisation in which the information strategy function is devolved to autonomous ...||6||7|
|Data management||The management of practices and processes to ensure the security, quality, integrity, safety and availability ...||2||3||4||5||6|
|Information assurance||The protection of integrity, availability, authenticity, non-repudiation and confidentiality of information ...||5||6||7|
|Enterprise and business architecture||The creation, iteration, and maintenance of structures such as enterprise and business architectures ...||5||6||7|
|Relationship management||The systematic identification, analysis, management, monitoring and improvement of stakeholder relationships ...||4||5||6||7|
|Data culture change and organisational capability development||Business process improvement||The creation of new and potentially disruptive approaches to performing business activities in order ...||5||6||7|
|Innovation||The capability to identify, prioritise, incubate and exploit opportunities provided by information, ...||5||6||7|
|Organisational capability development||The provision of leadership, advice and implementation support to assess organisational capabilities ...||5||6||7|
|Organisation design and implementation||The planning, design and implementation of an integrated organisation structure and culture including ...||5||6||7|
|Methods and tools||The definition, tailoring, implementation, assessment, measurement, automation and improvement of methods ...||3||4||5||6|
|Knowledge management||The systematic management of vital knowledge to create value for the organisation by capturing, sharing, ...||2||3||4||5||6||7|
|Measurement||The development and operation of a measurement capability to support agreed organisational information ...||3||4||5||6|
|Change implementation planning and management||The definition and management of the process for deploying and integrating new digital capabilities ...||5||6|
|Competency assessment||The assessment of knowledge, skills and behaviours by any means whether formal or informal against frameworks ...||3||4||5||6|
|Learning delivery||The transfer of business and/or technical skills and knowledge and the promotion of professional attitudes ...||3||4||5|
|Performance management||The optimisation of performance of people, including determination of capabilities, integration into ...||4||5||6|
|Data lifecycle management||Systems development management||The planning, estimating and execution of programmes of systems development work to time, budget and ...||5||6||7|
|Solution architecture||The design and communication of high-level structures to enable and guide the design and development ...||4||5||6|
|Requirements definition and management||The elicitation, analysis, specification and validation of requirements and constraints to a level that ...||2||3||4||5||6|
|Data modelling and design||The development of models to represent and communicate data requirements and to enable organisations ...||2||3||4||5|
|Database design||The specification, design and maintenance of mechanisms for storage of and access to data in support ...||3||4||5|
|Business analysis||The methodical investigation, analysis, review and documentation of all or part of a business in terms ...||3||4||5||6|
|Systems design||The design of systems to meet specified requirements, compatible with agreed systems architectures, ...||4||5||6|
|Programming/software development||The planning, designing, creation, amending, verification, testing and documentation of new and amended ...||2||3||4||5||6|
|Business process testing||The planning, design, management, execution and reporting of business process tests and usability evaluations. ...||4||5||6|
|Testing||The planning, design, management, execution and reporting of tests, using appropriate testing tools ...||1||2||3||4||5||6|
|Analytics||The application of mathematics, statistics, predictive modeling and machine-learning techniques to discover ...||3||4||5||6||7|
|Data visualisation||The process of interpreting concepts, ideas, and facts by using graphical representations. Condensing ...||4||5|
|Information content authoring||The application of the principles and practices of authoring, designing, controlling, and presenting ...||1||2||3||4||5||6|
|Information content publishing||The evaluation and application of different publishing methods and options, recognising key features, ...||1||2||3||4||5||6|
|Database administration||The installation, configuration, upgrade, administration, monitoring and maintenance of databases. Providing ...||2||3||4||5|
|Availability management||The definition, analysis, planning, measurement, maintenance and improvement of all aspects of the availability ...||4||5||6|
|Storage management||The planning, implementation, configuration and tuning of storage hardware and software covering online, ...||3||4||5||6|
|Capacity management||The planning, design and management of the capability, functionality and sustainability of service components ...||4||5||6|
|Data foundations||Quality management||Quality management establishes within an organisation a culture of quality and a system of processes ...||3||4||5||6||7|
|Conformance review||The independent assessment of the conformity of any activity, process, deliverable, product or service ...||3||4||5||6|
|Quality assurance||The process of ensuring, through independent assessment and review, that appropriate working practices, ...||3||4||5||6|
|Information security||The selection, design, justification, implementation and operation of controls and management strategies ...||3||4||5||6||7|
|Security administration||The provision of operational security management and administrative services. Typically includes the ...||1||2||3||4||5||6|
|Business risk management||The planning and implementation of organisation-wide processes and procedures for the management of ...||4||5||6||7|
|Continuity management||The provision of service continuity planning and support, as part of, or in close cooperation with, ...||4||5|