Description: These modules explore the role of insight analysts, and the scientific method, in shaping decisions, generating measurable improvement, and engaging key stakeholders. The modules include practical exercises to help you identify gaps and opportunity in your current approaches.

Adding Value as Insight Analysts
This module covers:

• The importance of Insight Analysts in driving action and improvement within an organisation, emphasising the principle that there is no insight without action and no action without insight.
• The role of data as a valuable resource in shaping behaviours, processes, and strategies, including the need for accurate and relevant data to drive informed decision-making.
• The various ways data can be utilised by Insight Analysts, such as informing operations, persuading stakeholders to adopt new approaches, and exploring new strategies to improve performance and meet organisational goals.

Analysts as Improvement Champions
This module covers:

• Emphasising the role of analysts as Improvement Champions, focusing on continuous learning and striving for excellence.
• Highlighting the need to focus on critical areas, simplify tasks, and align insights with strategic goals for effective improvement.
• Outlining three phases of improvement: focussing on priorities, building an operating rhythm for engagement, and driving actionable improvements in behaviour and processes.

Shaping the Future the 4 Stages of Insight
This module covers:

• The importance of mindset change in forecasting, planning, and leading, emphasising preparation over prediction for future readiness.
• The necessity to shift from viewing forecasts as right or wrong towards using them to stay ahead, anticipate potential issues, and support decision-making.
• Encouraging strategic thinking and planning by imagining and exploring different possible futures, moving beyond traditional "what if" scenarios to "if then" approaches for goal-oriented actions.

The Scientific Method
This module covers:

• The importance of the scientific method in making data-led decisions, enhancing credibility, and providing a framework for testing hypotheses in a business context.
• A detailed explanation of the scientific method, including formulating hypotheses, conducting experiments, and analysing results to validate or disprove the hypotheses.
• The significance of sharing and communicating results effectively, emphasising that knowledge has little value if not utilised, and the ongoing nature of the scientific process, which generates new questions and insights.

Problem Statements & Hypotheses
This module covers:

• Creating effective problem statements for change management, highlighting their role in methodologies like Six Sigma. It focuses on identifying valuable questions and guiding towards practical solutions.
• Developing and testing hypotheses in a business context, emphasising specific, measurable, and testable hypotheses through scientific methods.
• Designing and executing experiments to validate hypotheses, including pilot and control methods, and the importance of managing risks and variables in these experiments.

The Scientific Method and the Improvement Cycle
This module covers:

• The integration of the scientific method and improvement cycle in analytics, emphasising the need for robust scientific approaches to ensure confidence in insights and drive the right improvements.
• A detailed framework for improvement, focusing on three key phases: identifying what matters most, aligning operations with strategy, and using scientific methods to analyse and prioritise opportunities.
• The importance of effective communication and action to realise the value of insights, including the use of performance dashboards, playbooks, and regular reviews to drive behavioural and process improvements.

Prioritising Opportunities for Improvement
This module covers:

• The importance of insight in driving organisational action and improvement, emphasising the mantra "no insight without action and no action without insight."
• The key role of Insight Analysts in adding value to an organisation by deriving actionable insights from available data, applicable to various roles such as team leaders, resource planners, and quality analysts.
• Three key ways to utilise data for organisational benefit: informing decision-making, persuading stakeholders to drive change, and exploring new strategies and approaches for future growth and improvement.

Introduction to Accuracy & Confidence
This module covers:

• The importance of understanding accuracy and confidence in data analysis, emphasising the role of random variations and bias in skewing conclusions.
• Statistical concepts like variance, standard deviation, and margin of error, illustrating their impact on data interpretation with examples like dice rolls and customer surveys.
• The influence of bias in data collection and interpretation, highlighting how biases in test groups, surveys, and customer responses can affect the validity of data.

The cost of this mini series including accreditation is great value at £325 + VAT and free for students on our assisted learning pathway.