We as accountants have some very sophisticated data analysis tools at our disposal, and it’s fun to experiment with the advanced formulae available in Excel and other programmes.
However, data analysis is an iterative process and it is important to start with some simple analysis tools to highlight the key issues in the data before exploring those issues with more advanced tools.
That’s where this course – Data Analysis 101 - comes in. This course covers the four basic tools of data analysis that everyone working in an organisation should know. These four tools will help you carry out the initial filtering of performance data in order to highlight the important trends and issues. Often these tools are not covered adequately in business or finance training and this course will give you a good grounding in these simple but immensely useful analysis tools.
Advanced analysis is often difficult to understand for people who are not finance or data experts. Using simple tools to draw out the main points in performance data will improve engagement with the data and will help drive discussions about the appropriate actions to take, rather than heated discussions about what the analysis actually means.
This course will help you get to the heart of understanding the performance data in your organisation, and it will help you present that information to others in the organisation so that the decisions made, and the actions taken, address the real issues affecting performance.
I hope you enjoy the course.
Who this course is for
- Business managers
- Accounting students
- Finance staff
What you'll learn
- Principles of Process Management
- An Introduction to Performance Data: Tools for Data Analysis that Works
- Understanding Variation
- The Control Chart: Highlighting the variation in data
- Control Chart Special Cases
- Interpreting the Control Chart
- Strategies to deal with Special Cause and Common Cause Variation
- Using Data to Drive Improvement
- A Structure for Performance Management
There are no pre-course requirements.