In today’s ever-changing business environment, organisations need an agile model for allocating resources to desired outcomes. This webcast explores agile budgeting and rolling forecast models that leverage big data, analytics, AI, and machine learning for a new level of dynamic performance measurement.
There are no upcoming dates for this Webcast
Digital transformation has extended to the budgeting and forecasting functions in today’s leading organisations. This webcast will show you how to move towards an agile approach to performance management that leverages the power of technology, including big data and analytics.
The traditional budgeting model is based on annual cycles followed by analysing variances to fixed positions - a philosophy that is becoming dated. Many organisations are finding that a rolling forecast model that considers the impacts of dynamically shifting positions is more flexible and effective.
The perceived challenges of identifying changes in business drivers has been made easier and more reliable with the advent of big data, technology and analytics, providing predictive insights on business outcomes through fact-based modelling.
With finance teams and organisations as a whole moving to more of an agile environment, rolling forecasts are more relevant and allow for developing dynamic performance management frameworks that better support successful business management.
Learn how to make the shift to forecasting techniques based on predictive insights rather than unsubstantiated assumptions. Finance professionals helping their organizations remain competitive in a dynamic environment will not want to miss this webcast. Register today!
This webcast is free for CIMA members.
This Webcast offers 2 hours of CPE credit.
Note on CPE Requirements and Credits
All learning resources available in the CGMA Store qualify for CPD for CIMA members.
|Prerequisites:||Working knowledge of budgeting and forecasting|
|Access:||This is a live event. You will have access to webcast on the date/time of broadcast and the archive for 3 months after the broadcast date.|