Digital Business Accelerator Programme Overview
This programme equips junior talent with the business analysis and data skills needed to drive meaningful business impact. It builds foundational skills to strengthen early talent retention, increase productivity, uncover cost-saving opportunities, and increase profitability. It is part of our Data Academy Programmes.
Key Skills Gained:
Data in business
Project management
Business intelligence
Communicate effectively
Business Outcomes:
Increase retention: Equip apprentices with the tools and coaching to support their long-term professional development.
Increase productivity: Provide easy access to clean, structured data that can be utilised by the business.
Increase profitability: Equip junior team members with core skills to project manage key initiatives, saving senior team members' time.
Identify cost-saving opportunities: Reduce manual analysis and utilise data to make business decisions.
Apprenticeship Qualification Achieved: Level 3 Data Technician
Duration: 12 month delivery, plus 2-3 month assessment
Software Requirements: Digital Business Accelerator Software Requirements
Digital Business Accelerator Indicative Curriculum Breakdown
Business and data fundamentals
Module 1: Personal power
Introduction to the module project and project writing 101
Industry and role awareness
Personal power and knowing your why
Taking initiative and managing upwards
Month 2: Business fundamentals
The role of data in decision making for internal stakeholders and customers
How data is stored, shared, and saved in compliance with organisational policy
Format a raw dataset in Excel
Module 3: Data in business
Foundations of project work: communication for stakeholder expectation setting and project scoping
The importance of data quality
Assessing the quality of data
Data cleaning in Excel
Module 4: Data proficiency
Describe trends in Excel data using simple formulas, filters, formatting, tables, and statistical analysis
Create and analyse data visualisations in Excel and Power BI
Task prioritisation, stakeholder management, and upward communication
Project management and communications
Module 5: Communicating effectively
Storytelling and influencing techniques to communicate data insights
Present data narratives to various types of stakeholders
Utilise accessible design principles for all visualisations and presentations
Module 6: Portfolio development and end point assessment (EPA) preparation
Working session to learn more about the EPA, practice for the interview, and work on evidence
Module 7: Project management - The Principles
Describe how to run a project against a clear timeline
Understand how to identify and communicate with stakeholders throughout a project
Use automation to make data work more efficient
Module 8: Project management - Data Projects
Manage change for data and support stakeholders through project-related changes
Normalise data
Use data modelling techniques to analyse large datasets
Regulatory requirements for data management
Module 9: Project delivery
Measure and communicate the impact of data projects
Evaluate and measure project outcomes, effectively conveying project impact
Create interactive visuals in Power BI to communicate data narrative
Decision making with data
Module 10: Business intelligence
Prepare, analyse, and visualise data in Excel and Power BI
Link data skills learned thus far, working end-to-end to take a raw dataset and turn it into insights and recommendations that can influence decision making
Module 11: Insights and decisions
Understanding IoT, big data, cloud, and machine learning
How these technologies relate to your organisation
Staying up to date with trends in data
The effectiveness and ethics of big data and machine learning
Module 12: Gateway and end point assessment (EPA) preparation
Apprentices are reaching the end of their apprenticeship programme and will be completing their End Point Assessments (EPA).
This module will ensure they are fully prepared to be successful in the EPA.
Note: This is an example curriculum, and specific details may vary per cohort.