Reference and guide to SFIA version 7. Framework status: Development. Show revision-marked text

Analytics INAN

(modified)

The application of mathematics, statistics, predictive modeling and machine-learning techniques to discover meaningful patterns and knowledge in recorded data. Analysis of data with high volumes, velocities and variety (numbers, symbols, text, sound and image). Development of forward-looking, predictive, real-time, model based insights to create value and drive effective decision-making. The identification, validation and exploitation of internal and external data sets generated from a diverse range of processes.

Analytics: Level 7

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Directs the creation and review of a cross-functional, enterprise-wide approach and culture for analytics. Leads the provision of the organisation’s analytics capabilities. Leads the organisation's commitment to efficient and effective analysis of textual / numerical / visual / audio information.

Analytics: Level 6

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Develops analytics policy, standards and guidelines. Establishes and manages analytics methods, techniques and capabilities to enable the organisation to analyse data, to generate insights, create value and drive decision-making. Sets direction and leads the introduction and use of analytics to meet overall business requirements, ensuring consistency across all user groups. Identifies and establishes the veracity of external sources of information of relevance to the operational needs of the enterprise.

Analytics: Level 5

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Evaluates the need for analytics, assesses the problems to be solved and what internal or external data sources to use or acquire. Specifies and applies appropriate mathematical, statistical, predictive modelling or machine-learning techniques to analyse data, to generate insights, to create value and support decision-making. Manages reviews of the benefits and value of analytics techniques and tools and recommends improvements. Contributes to the development of analytics policy, standards and guidelines.

Analytics: Level 4

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Applies a range of mathematical, statistical, predictive modelling or machine-learning techniques in consultation with experts if appropriate, and with sensitivity to the limitations of the techniques. Selects, acquires and integrates data for analysis. Develop data hypotheses and methods, trains and evaluates analytics models, shares insights and findings and continues to iterate with additional data.

Analytics: Level 3

(unchanged)

Undertakes analytical activities and delivers analysis outputs, in accordance with customer needs and conforming to agreed standards.