Automated Financial Forecasting: Upgrading Talent Models
22 Ιανουαρίου 2023
Automated Financial Forecasting: Upgrading Talent Models
Automated Financial Forecasting: Upgrading Talent Models
22 Ιαν 2023
For leaders looking to strengthen forecasting capabilities with automation, the task can seem daunting. But by breaking the journey into its three critical components—data management, project design, and talent—with a focus on how each supports the others, the pathway to more reliable and timely analysis can come into view.
For organizations to achieve a state of automated forecasting, solving for data requirements and process capabilities may pose less of a challenge than creating an organization with a data-centric mindset focused on teaming cohesively with machines to deliver insights.
That’s because finance must become more comfortable pioneering new ideas to create and challenge data-driven hypotheses. Intense curiosity should drive talent to better understand the business processes they quantify, advocate for, and explain, and the predictive models they design, as well as drive continuous improvement of the organizational data landscape.Functional finance expertise alone is not enough. To architect a finance function to achieve continuous forecasting, it is critical for leaders to instill a bias for data-based decision-making, continuous improvement of data and predictive models, and a passion for technology enablement.
For leaders looking to strengthen forecasting capabilities with automation, the task can seem daunting. But by breaking the journey into its three critical components—data management, project design, and talent—with a focus on how each supports the others, the pathway to more reliable and timely analysis can come into view.
For organizations to achieve a state of automated forecasting, solving for data requirements and process capabilities may pose less of a challenge than creating an organization with a data-centric mindset focused on teaming cohesively with machines to deliver insights.
That’s because finance must become more comfortable pioneering new ideas to create and challenge data-driven hypotheses. Intense curiosity should drive talent to better understand the business processes they quantify, advocate for, and explain, and the predictive models they design, as well as drive continuous improvement of the organizational data landscape.Functional finance expertise alone is not enough. To architect a finance function to achieve continuous forecasting, it is critical for leaders to instill a bias for data-based decision-making, continuous improvement of data and predictive models, and a passion for technology enablement.
For leaders looking to strengthen forecasting capabilities with automation, the task can seem daunting. But by breaking the journey into its three critical components—data management, project design, and talent—with a focus on how each supports the others, the pathway to more reliable and timely analysis can come into view.
For organizations to achieve a state of automated forecasting, solving for data requirements and process capabilities may pose less of a challenge than creating an organization with a data-centric mindset focused on teaming cohesively with machines to deliver insights.
That’s because finance must become more comfortable pioneering new ideas to create and challenge data-driven hypotheses. Intense curiosity should drive talent to better understand the business processes they quantify, advocate for, and explain, and the predictive models they design, as well as drive continuous improvement of the organizational data landscape.Functional finance expertise alone is not enough. To architect a finance function to achieve continuous forecasting, it is critical for leaders to instill a bias for data-based decision-making, continuous improvement of data and predictive models, and a passion for technology enablement.
Delivering Value
Delivering Value
Automated financial forecasting puts data, reporting, and analytics at the fingertips of business leaders to effectively operate in demanding business environments. This can lead to faster time to insights and decisions that drive earnings, reduce costs, and create controls over financial outcomes.
For business leaders, this means more transparency into forecasts and assumptions to execute better pricing, predict customer demands, and reduce working capital. Across the organization, analysts to senior management will have access to real-time analytics and scenario models to inform everyday decisions. Processes will become more efficient through enabling technology and digital capabilities to keep organizations focused on delivering future value versus reconciling data and reporting on what has already happened
Critical success factors include:
End-to-end strategy and design. Concentrate design efforts across data management, process design, and workforce and talent to create the connectivity required across functions, systems, and tools to enable an automated forecasting environment.
User adoption. Build trust in machine-powered, automated forecasting to drive adoption and incorporation of these powerful tools within everyday ways of working.
Human-centric design. Focus design efforts on the end user to better align machine-enabled forecast capabilities with the intended process and forecast outputs.
Focus on data. Don’t underestimate the importance of a solid and complete data foundation to support a state of automated financial forecasting.
Executive leadership support. Establish top-down leadership support to drive commitment and focus from the organization to realize the value of automated financial forecasting
The future of forecasting will be enabled by humans and machines working collaboratively with continuous, reliable data feeds to deliver timely insights that drive decisions. Talent and workforce models across industries are already looking ahead and adapting to these future ways of working.
Automated financial forecasting puts data, reporting, and analytics at the fingertips of business leaders to effectively operate in demanding business environments. This can lead to faster time to insights and decisions that drive earnings, reduce costs, and create controls over financial outcomes.
For business leaders, this means more transparency into forecasts and assumptions to execute better pricing, predict customer demands, and reduce working capital. Across the organization, analysts to senior management will have access to real-time analytics and scenario models to inform everyday decisions. Processes will become more efficient through enabling technology and digital capabilities to keep organizations focused on delivering future value versus reconciling data and reporting on what has already happened
Critical success factors include:
End-to-end strategy and design. Concentrate design efforts across data management, process design, and workforce and talent to create the connectivity required across functions, systems, and tools to enable an automated forecasting environment.
User adoption. Build trust in machine-powered, automated forecasting to drive adoption and incorporation of these powerful tools within everyday ways of working.
Human-centric design. Focus design efforts on the end user to better align machine-enabled forecast capabilities with the intended process and forecast outputs.
Focus on data. Don’t underestimate the importance of a solid and complete data foundation to support a state of automated financial forecasting.
Executive leadership support. Establish top-down leadership support to drive commitment and focus from the organization to realize the value of automated financial forecasting
The future of forecasting will be enabled by humans and machines working collaboratively with continuous, reliable data feeds to deliver timely insights that drive decisions. Talent and workforce models across industries are already looking ahead and adapting to these future ways of working.
Automated financial forecasting puts data, reporting, and analytics at the fingertips of business leaders to effectively operate in demanding business environments. This can lead to faster time to insights and decisions that drive earnings, reduce costs, and create controls over financial outcomes.
For business leaders, this means more transparency into forecasts and assumptions to execute better pricing, predict customer demands, and reduce working capital. Across the organization, analysts to senior management will have access to real-time analytics and scenario models to inform everyday decisions. Processes will become more efficient through enabling technology and digital capabilities to keep organizations focused on delivering future value versus reconciling data and reporting on what has already happened
Critical success factors include:
End-to-end strategy and design. Concentrate design efforts across data management, process design, and workforce and talent to create the connectivity required across functions, systems, and tools to enable an automated forecasting environment.
User adoption. Build trust in machine-powered, automated forecasting to drive adoption and incorporation of these powerful tools within everyday ways of working.
Human-centric design. Focus design efforts on the end user to better align machine-enabled forecast capabilities with the intended process and forecast outputs.
Focus on data. Don’t underestimate the importance of a solid and complete data foundation to support a state of automated financial forecasting.
Executive leadership support. Establish top-down leadership support to drive commitment and focus from the organization to realize the value of automated financial forecasting
The future of forecasting will be enabled by humans and machines working collaboratively with continuous, reliable data feeds to deliver timely insights that drive decisions. Talent and workforce models across industries are already looking ahead and adapting to these future ways of working.
