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Data analyst

Data analyst


Level 4 - Higher Technical Occupation

Collect, organise and study data to provide business insight.

Reference: OCC0118

Status: assignment_turned_inApproved occupation

Average (median) salary: £31,193 per year

SOC 2020 code: 3544 Data analysts

SOC 2020 sub unit groups:

  • 3544/00 Data analysts
  • 2133/02 Data architects
  • 2433/04 Statistical data scientists

Technical Education Products


Data analyst

(Level 4)

Approved for delivery

Employers involved in creating the standard:

Estee Lauder, UBS, Risual Ltd, Network Rail, DMG Media, University of West London


This occupation is found in any employer in any sector that uses data to make business decisions. Data analysts may work in various departments within a single employer, (for example finance, sales, HR, manufacturing, or marketing), and in any employment sector, public or private, including retail, distribution, defence, banking, logistics, media, local government etc.

The broad purpose of the occupation is to ascertain how data can be used in order to answer questions and solve problems. Data analysis is a process of requirement-gathering, inspecting, cleansing, transforming and modelling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names. In today's world, data analysis plays a crucial role in making decisions more evidence-based and helping organisations operate more effectively.

For example: a data analyst may investigate social media trends and their impact on the organisation. In retail, a data analyst may break down sales figures to make recommendations on product placement and development. In HR a data analyst may investigate staff retention rates, in order to decide on recruitment strategy. In a hospital, a data analyst may investigate wait times for different departments, in order to provide a better service to its patients.

In their daily work, an employee in this occupation interacts with internal or external clients. Internally, the data analyst may work with many people within their organisation, at different levels. Externally a data analyst may provide data analysis services to other organisations on behalf of their employer. Data analysts would normally be office based and work normal business hours.

An employee in this occupation will be responsible for the creation and delivery of their own work, to meet business objectives. The data analyst will be responsible for working within the data architecture of the company and ensuring that the data is handled in a compliant, safe and appropriately secure manner, understanding and adhering to company data policy and legislation. Data analysis is a fast-moving and changing environment, and data analysts need to continue to stay abreast of, and engaged with, changes and trends in the wider industry; including data languages, tools and software, and lessons learnt elsewhere.

Employers involved in creating the standard:

Estee Lauder, UBS, Risual Ltd, Network Rail, DMG Media, University of West London

Typical job titles include:

Data Analyst
Departmental Data Analyst
Junior Analyst
Marketing Data Analyst
Problem Analyst
Energy Data Analysteco



Knowledge, skills and behaviours (KSBs)

K1: current relevant legislation and its application to the safe use of data
K2: organisational data and information security standards, policies and procedures relevant to data management activities
K3: principles of the data life cycle and the steps involved in carrying out routine data analysis tasks
K4: principles of data, including open and public data, administrative data, and research data
K5: the differences between structured and unstructured data
K6: the fundamentals of data structures, database system design, implementation and maintenance
K7: principles of user experience and domain context for data analytics
K8: quality risks inherent in data and how to mitigate or resolve these
K9: principal approaches to defining customer requirements for data analysis
K10: approaches to combining data from different sources
K11: approaches to organisational tools and methods for data analysis
K12: organisational data architecture
K13: principles of statistics for analysing datasets
K14: the principles of descriptive, predictive and prescriptive analytics
K15: the ethical aspects associated with the use and collation of data

S1: Use data systems securely to meet requirements and in line with organisational procedures and legislation including principles of Privacy by Design
S2: implement the stages of the data analysis lifecycle
S3: apply principles of data classification within data analysis activity
S4: analyse data sets taking account of different data structures and database designs
S5: assess the impact on user experience and domain context on data analysis activity
S6: identify and escalate quality risks in data analysis with suggested mitigation or resolutions as appropriate
S7: undertake customer requirements analysis and implement findings in data analytics planning and outputs
S8: identify data sources and the risks and challenges to combination within data analysis activity
S9: apply organizational architecture requirements to data analysis activities
S10: apply statistical methodologies to data analysis tasks
S11: apply predictive analytics in the collation and use of data
S12: collaborate and communicate with a range of internal and external stakeholders using appropriate styles and behaviours to suit the audience
S13: use a range of analytical techniques such as data mining, time series forecasting and modelling techniques to identify and predict trends and patterns in data
S14: collate and interpret qualitative and quantitative data and convert into infographics, reports, tables, dashboards and graphs
S15: select and apply the most appropriate data tools to achieve the optimum outcome

B1: maintain a productive, professional and secure working environment
B2: show initiative, being resourceful when faced with a problem and taking responsibility for solving problems within their own remit
B3: work independently and collaboratively
B4: logical and analytical
B5: identify issues quickly, investigating and solving complex problems and applying appropriate solutions. Ensures the true root cause of any problem is found and a solution is identified which prevents recurrence.
B6: resilient - viewing obstacles as challenges and learning from failure.
B7: adaptable to changing contexts within the scope of a project, direction of the organisation or Data Analyst role.


Duty D1

Identify data sources to meet the organisation's requirement, using evidence-based decision making to establish a rationale for inclusion and exclusion of various data sets and models

Duty D2

Liaise with the client and colleagues from other areas of the organisation to establish reporting needs and deliver insightful and accurate information

Duty D3

Collect, compile and, if needed, cleanse data, such as sales figures, Digital Twins etc. solving any problems that arise, to or from a range of internal and external systems

Duty D4

Produce performance dashboards and reports in the Visualisation and Model Building Phase

Duty D5

Support the organisation by maintaining and developing reports for analysis to aid with decisions, and adhering to organisational policy/legislation

Duty D6

Produce a range of standard and non standard statistical and data analysis reports in the Model Building phase

Duty D7

Identify, analyse, and interpret trends or patterns in data sets

Duty D8

Draw conclusions and recommend an appropriate response, offer guidance or interpretation to aid understanding of the data

Duty D9

Summarise and present the results of data analysis to a range of stakeholders, making recommendations

Duty D10

Provide regular reports and analysis to different management or leadership teams, ensuring data is used and represented ethically in line with relevant legislation (e.g. GDPR which incorporates Privacy by Design).

Duty D11

Ensure data is appropriately stored and archived, in line with relevant legislation e.g. GDPR

Duty D12

Practice continuous self learning to keep up to date with technological developments to enhance relevant skills and take responsibility for own professional development

Occupational Progression

This occupational progression map shows technical occupations that have transferable knowledge and skills.

In this map, the focused occupation is highlighted in yellow. The arrows indicate where transferable knowledge and skills exist between two occupations. This map shows some of the strongest progression links between the focused occupation and other occupations.

It is anticipated that individuals would be required to undertake further learning or training to progress to and from occupations. To find out more about an occupation featured in the progression map, including the learning options available, click the occupation.

Progression decisions have been reached by comparing the knowledge and skills statements between occupational standards, combined with individualised learner movement data.

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Level 3

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Level 4

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Level 4

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Level 6

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Level 6

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Level 7

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Level 7


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